International Conference on Complex Systems 2004 (ICCS2004)

ICCS2004 ABSTRACT BOOK

(* indicates confirmed)
Additional speakers are still to be assigned
to sessions. Session times may still change.

SUNDAY, May 16

9:00AM-5:00PM PEDAGOGICAL SESSIONS

*EVE MITLETON-KELLY - Pedagogical Sessions

*ANDREW WUENSCHE - Studying discrete dynamical networks with DDLab
Author(s):
Andrew Wuensche, Discrete Dynamics Lab, Univ. of Sussex, and Univ of the West of England, USA and UK
Abstract:
Cellular automata, with their local interactions and homogeneous logic, arguably provide the simplest systems which allow the emergence of higher levels of complex structure, whereas sparsely connected non-local networks with heterogeneous logic (random Boolian networks) provide insights in biology into genetic and neural networks. Discrete Dynamics Lab is interactive graphical software to study networks constructed according to these two paradigms, or anything in between, but generalized to include multi-value logic. Space-time patterns (in 1d, 2d and 3d) and basins of attraction can be generated and studied. The ideas, methods, results and examples will be described, including order-complexity-chaos measures and parameters, categorization (memory) in basins of attraction and subtrees, the consequences of mutations/perturbations, the automatic classification of rule-space by input-entropy, filtering, encryption, and the emergence of gliders and self-reproduction. The presentation will be made in a live demo of DDLab. For more information see http://www.ddlab.com
*URI WILENSKY - Modeling complex systems
*JOHN PETER WIKSWO - From physics to medicine
*LEV LEVITIN - Entropy and information

*URI WILENSKY - Pedagogical Sessions

*GREG CHAITIN - Mathematics
*PHILIP BALL - History of social modeling
*BILL HILLIER - Urban space syntax
*STEPHEN WOLFRAM - New science

EVENING RECEPTION

*STEPHEN HEUSER (THE BOSTON GLOBE)/
*TOM SIEGFRIED (THE DALLAS MORNING NEWS)

*ALVIN AND HEIDI TOFFLER - The Future

MONDAY, May 17

8:50AM-9:00AM CONFERENCE WELCOME

*YANEER BAR-YAM - Conference Welcome

9:00AM-10:30AM EMERGENCE

*JOHN STERMAN - Emergence

*STEVEN STROGATZ - Synchrony
*ALAN GUTH - Inflationary universe

11:00AM-12:30PM NETWORKS

*IRVING EPSTEIN - Networks

*GENE STANLEY - Networks and Liquid Water
*LEON COOPER - Neural networks in vision

1:30PM-2:50PM NETWORKS

*IRVING EPSTEIN - Networks

*NEO MARTINEZ - Food Webs
*RICARD SOLE - Complex networks

3:20PM-4:50PM ROBOTS

*ALI MINAI - Robots

*STEFANO NOLFI - Evolving Swarm-Bots
*WEI-MIN SHEN - Self-Reconfigurable Robots and Digital Hormones
Author(s):
Wei-Min Shen, University of Southern California
Abstract:
Self-reconfigurable modular robots are metamorphic systems that can autonomously change their logical or physical configurations (such as shapes, sizes, or formations), as well as their locomotion and manipulation, based on the mission and the environment in hand. Because of their modularity, versatility, self-healing ability and low cost reproducibility, such robots provide a flexible approach for achieving complex tasks in unstructured and dynamic environments. They are well suited for applications such as search and rescue, reconnaissance, self-assembly, inspections in hazardous environments, and exploration in space and ocean. The construction and control of these robots, however, are very challenging due to the dynamic topology of the module network, the limited resource of individual modules, the difficulties in global synchronization, the preclusion of centralized decision makers, and the unreliability of communication among modules. This talk presents a distributed and reliable control architecture and related algorithms for these challenges. The approach is inspired by the biological concept of hormones (thus the name ?digital hormones?) and it provides a unified solution for metamorphic systems? self-reconfiguration, locomotion, and manipulation. Modules are modeled as autonomous agents free from globally unique identifiers and they can physically connect and disconnect with each other and can communicate via content-based messages. In particular, the talk will present: (1) a particular self-reconfigurable robot called CONRO; (2) a general representation for self-reconfigurable systems; (3) distributed solutions for ?task negotiation,? ?topology-dependent behavior selection? and ?synchronization?; (4) distributed detection and reaction mechanisms for topology changes and message loss; and (5) demonstrations of unique, online, self-reconfiguration capabilities on the CONRO robots for bifurcation, unification, behavior shifting, and shape-alternations. An application for self-assembly in space may also be presented when time permits.

6:00PM-9:00PM EVENING PARALLEL SESSIONS

*ROGER HURWITZ - Social Systems

DUNCAN A. ROBERTSON - A Dynamic Model of Inter-Firm Competition
Author(s):
Duncan A. Robertson, University of Oxford, United Kingdom
Abstract:
Complexity and agent-based models have recently been introduced to strategy research, and management science more generally. However, these models and frameworks have not realized the promise that they initially commanded. This is partially due to the fact that there are no simple and accessible models in management science that incorporate the benefits of considering inter-firm competition as complex systems. One of the most promising models to be introduced into management science is the NK model, pioneered by Kauffman (in a biological setting), and transferred to organization science by Levinthal. The NK model produces a complex ‘fitness landscape’ which firms can explore and thereby attempt to maximize their fitness. Whilst this model offers an enticing introduction to complex models, there may be problems when considering the application of the model to management science. Several comments and criticisms are offered in this paper, including the fact that the NK model offers a static representation of a firm’s decision set, and therefore is of little use in turbulent or high-velocity environments. The concept of landscape models is extended in the model presented in this paper. However, instead of basing the model on the NK framework, a competitive landscape model is produced from the interaction of firm and customer agents co-existing in a product space. (This is extended to more general firm – ‘value-generating agent’ interactions.) From this simple model, a complex landscape is generated. This has the advantage over the NK model in that it can be represented as an actual landscape rather than relying on a schematic representation. The model produced by this interaction is dynamic, in that the movement of firms or customers produces a change in the landscape. Furthermore, the landscape in which a firm operates is different for each firm. This leads to true inter-firm heterogeneity, rather than the homogeneous firms intra-group as in the strategic groups literature or that of economic models more generally. Easily representable 'Profit Lanscapes' are generated from the model, rather than the schematic representations as in the NK model. As firms and value-generating agents move in this space, the landscape deforms and produces a dynamic, changing landscape. Simple strategies such as ‘hill climbing’ algorithms as suggested by agent-based modellers (such as Rivkin / Rivkin and Siggelkow) may not be appropriate under this model, whereas they may be appropriate when exploring a static NK landscape. Entry decisions, the effect of movement of competitors, and optimal strategies for such landscapes are considered. Extensions to the model, for example customers with different value are suggested as extensions to the model. Agent-based modelling can be used to explore the landscape introduced in this model, and is introduced as a further extension to this research. Overall, the model introduced in this paper provides a dynamic model of inter-firm competition, extends the extant models, and provides a basis for further research for revitalizing the positioning literature.
*KAZUYUKI IKKO TAKAHASHI - An application of percolation theory on political science
Author(s):
Kazuyuki Ikko Takahashi, Meiji University, Tokyo, Japan
Abstract:
I have published an article which deals with the relation between activists with strong beliefs and their passive supporters. In this effort, I have made use of percolation theory. Supporters form groups. Activists persuade non supporters to become supporters, and also combine groups of supporters. When the probability of supporters exceeds a critical threshold, that is, the size of the cluster of supporters becomes practically infinite, activists are able to work efficiently and effectively. I have made some simulations on these models. This time, I will put some interpretations on these results from a political scientific point of view.
*CESAR E. GARCIA-DIAZ - Market Partitioning Under Varying Resource Landscapes
Author(s):
Cesar E. Garcia-Diaz, University of Groningen, The Netherlands
Abstract:
Market Partitioning Under Varying Resource Landscapes César E. García-Díaz First-year PhD Student University of Groningen Faculty of Economics Department of International Economics & Business The Netherlands Abstract In its attempt to understand population-level adaptation through the behavior of founding and mortality rates and the proliferation of organizational forms, Organizational Ecology (Hannan & Freeman, 1977, 1989) has brought alternative views to classical organization theory’ contingency approach regarding optimal strategies in uncertain environments through the formulation of Niche Width Theory (Freeman & Hannan, 1983; Péli, 1997; Bruggeman, 1997; Bruggeman & O’Nualláin, 2000; Baum & Amburgey, 2002; Hannan, Pólos & Carroll, 2003), to organizational change theories through the formulation of Structural Inertia Theory (Hannan & Freeman, 1984; Péli et al., 1994; Péli et al., 2000; Hannan, Pólos & Carroll, 2003) and to economists’ neoclassical theory regarding the understanding of markets composition. Organizational Ecology has also introduced its own vision of market structures and, as emphasized by Vermeulen & Bruggeman (2002) and Carroll & Hannan (1995), has proposed opposite perspectives to classical industrial organization theory views on the role of market concentration, as is the case in Resource Partitioning Theory (Carroll, 1985). Carroll (1985)’s seminal paper in Resource Partitioning Theory (RPT) in Organizational Ecology gives explanation about the coexistence of generalist organizations with specialist organizations in a two-dimensional resource space characterized by scale economies and a center. RPT emphasizes that some necessary (but no sufficient) conditions are needed for such dual market structure: heterogeneity of resources, the presence of a market center and economies of scale/scope. Although Witteloostuijn & Boone (2003) develop a market structure typology with eight typical cases, there is no theoretical investigation that connects the emergence of such typology with different sets of initial conditions for the n-dimensional resource space in which such market evolves. A theory to explain the emergence of these market configurations structures is needed. Some attempts like the first-order logic model by Vermeulen & Bruggeman (2001), which states that resource partitioning occurs independently of organizational mass, size-localized competition, diversifying consumer tastes and changes in niche width, misses important elements needed to fully understand the dynamics process that generates market partitioning. Through computer simulation of different resource landscapes, with “spatial” variations, we want to understand how such different resource spaces generate specific partitioned-market structures. We study which are the thresholds in the degree of homogeneity at the market center that account for specific levels of generalist concentration (and consequently, if size-localized competition (Baum & Amburgey, 2002) is effectively related to size or if it is a consequence of certain levels of homogeneity of the resource space). In the search of conditions for sufficiency in market partitioning, we also explore which are the heterogeneity thresholds that allow market partitioning to appear.
*CHRISTOPHER NEWMAN - REVOLUTION BY OSMOSIS: A CASE STUDY OF WEST FLORIDA, TEXAS, CALIFORNIA AND HAWAII
Author(s):
Christopher Newman, Roosevelt University/ Elgin Community College, USA
Abstract:
The process whereby the United States acquired West Florida, Texas, California and Hawaii followed roughly the same course: colonists emigrate from the United States into territory controlled by another sovereign government. The colonists swear allegiance to their new homeland, pledging to be good and industrious citizens and to obey the statutes and usages of their new country. After a period of time has passed and more Americans have entered, some legally and some illegally, the former citizens of the United States declare their independence of the central government, set up a new revolutionary republic and shortly thereafter petition the United States for admission as a territory. After a period of time (very short in some cases, longer in others) the United States admits the newly declared independent nation to the United States. This paper proposes to utilize agent-based modeling (ABM) and Spreadsheet Modeling of Fuzzy Cognitive Maps (Spreadsheet FCMs) to analyze revolution as an emergent property of American immigration into territory controlled by foreign governments. It is possible to use Spreadsheet-FCMs as well as the NetLogo ABM modeling system to vary the characteristics and investigate the operation of the process. Use of these two methodologies provides a useful check of one against the other, as Spreadsheet-FCM concentrates on the operation of macro-level forces and NetLogo ABM evaluates the results of interactions of many individual agents, allowing a situation to play itself out once agents have been given characteristics expressed as simple rules of behavior. Each method compensates for the implicit assumptions (and potential omissions and oversights) of the other. Both Spreadsheet FCMs and NetLogo ABM are applications of the theory of complex adaptive systems. By generating data in simulations, computer modeling can image behavior of both the individual component agents and the collective revolutionary system in candidate explanatory hypotheses. The “goodness of fit” of various proposed explanations can be tested by running the model and then comparing results to the actual record of events to determine the theory that comes closest. Both modeling techniques permit variation of individual components while maintaining others as constants (this is particularly a feature of NetLogo’s Behavior Space tool), thus the degree of interdependence of the parts of the model can be measured. With an explanatory model chosen and the degree of interdependence of its components determined, the overall complexity of the Osmotic Revolution process can be determined.
*MAKINEN SAKU - System dynamics approach to evolution and process of corporate political action
Author(s):
Makinen Saku, Tampere University of Technology, Industrial Management, Finland
Skippari Mika, Tampere University of Technology, Industrial Management, Finland
Lamberg, Juha-Antti, Helsinki University of Technology, Finland
Abstract:
The fields of industrial management and business studies have been actively borrowing ideas from the study of complex systems. Especially pertinent has been system dynamics approaches ever since 1970’s. Despite this long history of modeling approaches to management and business issues there still remain quite a few research gaps in the current knowledge. This paper takes a systems thinking approach to studying political actions corporations and enterprises are carrying out in their operations. This is a special class of operations and actions of a firm that has multiple actors (though very restricted in numbers), and multiple possibilities to carry out these actions and operations and multiple choices for actions and operations. Therefore, this is a typical situation in human systems that represents dynamic complexity with low level of combinatorial complexity. We approach the corporate political action (CPA) from an evolutionary, processual point of view and present a propositional inventory in a form of system dynamics model. Our model includes several levels of actors and ties the corporation into industry level as well as society and international level and further larger environmental context. Feedback loops are explicitly considered in order to present the processual outcomes of actions of a corporation. The model facilitates more detailed analysis of this special class of operations that corporations engage in.
*BRIAN LONSWAY - A Self-Organizing Neural System For Urban Design
Author(s):
Brian Lonsway, Rensselaer Polytechnic Institute, USA
Ajith Mulky Rao, Rensselaer Polytechnic Institute, USA
Abstract:
The dynamics of urban systems are characterized by complex non-linear relationships between socio-economic attributes of land use and spatial interactions. Traditional urban models have many limitations in simulating these urban dynamics. To overcome these limitations, a number of new approaches have been adopted. This paper examines a neural network based approach to the analysis of growth factors in an urban design proposal. The system incorporates Kohonen’s self-organizing map algorithms within an existing GIS application to function as a design and decision support system. Urban data of a simulated region is embedded in the neural net and correlated, in varying degrees, with data obtained from case studies and/or other local regions. This allows the user to visualize and understand the impacts of the proposal, which is otherwise difficult to envision because of its complexity.
*SORIN BAICULESCU - Mathematical models of stochastic level that may be used within the complex system of ferry-boat sea navigation
Author(s):
SORIN BAICULESCU, NATIONAL COMPANY FOR FREIGHT RAILWAY TRANSPORT "CFR-MARFA"SA, ROMANIA,38 DINICU GOLESCU Avenue,77111 Bucharest 1,The Ministry of Transport
Abstract:
Mathematical models of stochastic level that may be used within the complex system of ferry-boat sea navigation Keywords:traffic,system,stochastic At present, within the complex system of ferry-boat sea navigation in Romania, there are 12,500 TDW specialized ships which navigate in the international waters of the Black Sea, along sea lines Constantza-Derince (Turkey) and Constantza – Batumi (Georgia). In this paper there are several propositions of stochastic level mathematical models used to determine the optimum time horizon and the minimum time required for these ships to reach the destination harbors. These methods take into account some aleatory elements intervening in achieving a turnus, such as: weather conditions, possibilities to get into and out of straits, the situation of the navigable waters, the possibilities of getting into and out of harbor berths, the strait-roads-berth-ship sub-system being considered a complex order I waiting system, characterized by the Law of Poisson. The paper uses both the statistic data recorded in the year 2003, and a few methods of stochastic analysis and specific operational research (Monte-Carlo, Markov, waiting processes, prospective dynamic analysis, the decision theory, the games theory, the affecting theory), identifying the possibilities of an optimum feed-back. The obtained results are new in the field, having significance of theoretical and applying level, useful mainly in characterizing any complex system of ferry-boat navigation according to its local and general conditions. The paper also presents a soft program by which the respective system may be predicted and optimized.
*DAVID SYLVAN - Organized All the Way Down: The Local Complexity of "Thick" Social Systems
Author(s):
David Sylvan, Graduate Institute of International Studies, Geneva, Switzerland
Abstract:
Most discussions of social systems treat their degree of complexity as an emergent property, one all the more spectacular because arising from simple and highly constrained local interactions. However, there is a particular class of social systems – for example, groups of friends; regional clusters of national states – in which base interactions are “thick,” i.e., in which the very nature of the interaction is constructed by the participants, as one of many possibilities, at the moment they engage in it. In these systems, the local interactions are themselves complex and the systems display no strongly emergent features. Computational modeling of these systems must accordingly be modified from extant “thin” approaches in a more sociolinguistic direction.
*VLADISLAV KOVCHEGOV - A model of communications as random walk on the semantic tree
Author(s):
Vladislav Kovchegov, Horizon Blue Cross Blue Shield of New Jersey, USA
Abstract:
This paper contains a few models on free communications (communications without agendas). We will describe two types of free communication models. The first type is a model in which people have to sit in designated areas. In the second model participants are allowed to move about freely. An average person has a set of favorite themes. The themes of conversations are words that belong to some “themes alphabets” and can be present as a path of the “semantic tree”. The state communicated system is a partition or market partitions, where any elements of partitions are conversation’s subgroup marked by the theme of conversations. The first model is a model about communications with a given theme and it was implemented as a Markov stochastic process defined on the set of partitions. We define both the conditional probability that emerges as the conversation subgroup and the probability to fall to pieces for a small period of time. These conditional probabilities give us the ability to write the differential equations for probabilities to get partitions. The second type of model involves not only partitions, but partitions and themes as well. This means that the conversation process is defined on the marking partitions and this looks like a random walk on the semantic trees. The methods were demonstrated to small groups of people and differential equations for probabilities were found and stable probabilities. For the modeling of a conflict situation we have to just add the set of “sick” theme for any person (where a “sick” theme is a theme that invokes unusual reactions). Conflicts occur when the marking partitions are reached that are marked by “sick” theme(s).
*B. COHEN - Modelling the Enterprise and its Actors as Triply-Articulated Anticipatory Systems
Author(s):
B. Cohen, City University, UK
P. Boxer, Boxer Research Ltd., UK
Abstract:
We present PAN, a toolset for the analysis of strategic risk in large enterprises. We take an enterprise to comprise one or more actors, an actor being an embodied individual whose anticipation of its world may be characterised by three models: how things in the world behave, what states-of-affairs the actor desires and how the former can be orchestrated to satisfy the latter. All these models are necessarily inaccurate and, as they are continually being recast by the actor in the light of its experience, they may not even be mutually consistent. Inaccuracies in the first model expose the actor to 'performance' risk, e.g. that the behaviour of a designed system differs from that predicted by its design. Inconsistencies between the first and third models expose the actor to 'composition' risk, e.g. that a group of interoperating systems collectively exhibit unanticipated outcomes, either destructive or emergent. Inconsistencies among all three models expose the actor to 'implementation' risk, e.g. that an expressly demanded state-of-affairs, although successfully established, fails to satisfy the actor's desire. The advent of 'open systems' (e.g. the 'Semantic Web') has increased the significance of composition risk. Simultaneously, consumers of systems and services have been encouraged to express their demand in terms of states-of-affairs relevant in their own models (e.g. network-enabled conflict), rather than those of suppliers. This 'asymmetric demand' increases the significance of implementation risk. The discipline of systems engineering addresses performance risk, through powerful analytical and simulation models, and covers some aspects of composition risk. Soft systems methodologies seek to address composition risk and some aspects of implementation risk, but offer no analytical power. Using PAN, the enterprise's actors may construct representations of their models of themselves and compose these models together to locate, estimate and mitigate the risks posed to the enterprise by gaps and inconsistencies among them. Further, the resulting composite models define the granularity and stratification of the ontologies operative in the enterprise and thereby entail the architectural structure of enterprise-wide data platforms and agent-based systems.
DADANG SUBARNA - VALIDATION AND VERIFICATION OF MONEY MARKET FORECASTING WITH A NON-LINEAR METHOD (USD VS IDR CURRENCY CASE)
Author(s):
Dadang Subarna, National Istitute Of Prediction, Indonesia
Abstract:
In this paper, non-linear method is used for 5 days forecast of interbank exchange rate for US Dollar (USD) against Indonesia Rupiah (IDR) currency based on October 23, 1993 through February 20, 2004 observed interbank exchange rate daily data. Interbank exchange rate daily anomaly prediction was done for daily interbank exchange rate where here I evaluate anomaly is daily data subtracted by average from observation period. The optimal parameters of non-linear method have been gotten in prediction exchange rate for US Dollar (USD) against Indonesia Rupiah (IDR) currency anomaly were: 2 days lag time, 24 embedding dimensions. The result of model validation was got 0.99 (coefficient correlation). The results model prediction will show that interbank exchange rate anomaly for first next 3 days is very good performance in accurateness and trend but next 2 days is poor in accurateness but good in trend. But if I make average for 7 best embedding dimension the result is opposite (first next 3 days is good in trend only but not in accurateness and the rest of next 2 days is very good performance in accurateness and trend. Therefore, non-linear method is very effective for shorterm prediction in money market, but if we want to make longterm prediction, we must run the model for several dimension embedding and choose some of the best result for the averaged.
*CARLOS PARRA - Evolutionary Dynamics of Knowledge
Author(s):
Carlos Parra, Tokyo Insitute of Technology, Japan
Masakazu Yano, Tokyo University, Japan
Abstract:
This study discusses the human version of an artificial agent’s interpretative devices (Arthur, B. 1997) by presenting a definition of interpretants, which follows Varela’s (1999) neurophenomenological perspective coupled with a cybernetic understanding of Piercian semiotics, namely: experiences, made of alternate bundles of embodied experiences (distinctions.) This definition of interpretants is not only useful from a human development perspective when capabilities are comprised of alternate bundles of choices or functionings (an individual’s beings and doings, Sen, 1993) and these choices then, standing for embodied distinctions (Parra and Yano, 2002), stem from distinctions that in turn stand for embodied experiences (Parra and Yano, 2004); but more so because this approach uses liberty instead of utility for economic decision-making, replacing widely used traditional assumptions (i.e. individual rationality) and thereby adopting recent behavioral and experimental discoveries. In particular, this paper proposes a learning model (or inner-world reconstructing model) that can overcome neo-classic obstacles, and increase the predictive power of computational economics, by letting agents’ knowledge evolve by itself, irrespective of globally specified goals and even individual motives of behavior; using simultaneous (or parallel) Genetic Algorithms (GA) to evolve a single agent’s learning strategy, each GA with different general specifications, in a multi-agent setting. In order to implement our definition of interpretants computationally, artificial agents would need to be designed so as to: experience something; distinguish the source of this experience; also ground what they are experiencing; such that when this new experience is eventually employed, be able to embodied it as a distinction; and also be able to self-provoke random interpretations accounting for the effects of chance, leading to “misinterpretations” that could end up having positive effects on the performance of an agent, or the system as a whole. Moreover, this single agent inner world reconstruction model, when used in a multi-agent scenario could help scrutinize the transition from freewill-guided agents to rule-based interactions (i.e. cooperation and/or self-organization). Eventhough we do not provide detail specifications about how to implement the learning model, or put it into practice, we do give real-life perspective on what the outcomes of such an exercise could be in institutional terms (North, 1990) pointing to the evolutionary dynamics of experiences, distinctions and choices. This is done so as to contribute to the cognitive debate around agent-based learning models, which in our perspective should be about the methods for handling variation (inside learning algorithms, between algorithms, among agents, and for systems in general.)
*ANDREAS KEMPER - Network Externalities in Corporate Financial Management
Author(s):
Andreas Kemper, European Business School, Germany
Abstract:
Nonlinear network externalities are omnipresent within the business environment, influence strategic management decisions, and affect the corporate performance. Particularly relationship networks of corporations have proved increasingly significant for institutions adapting to their changing environment. Although the vital significance of networks is generally acknowledged in management research and their fingerprints are ubiquitous, an implicit and underdeveloped treatment dominates the financial theory and the management practice. Linearly approximating or simply ignoring the distinctive nonlinear properties of networks, contemporary corporate finance literature for the most part fails to internalize the nonlinear network externalities from a complex systems perspective. This paper investigates the effects of an internalization of network growth and network resilience on corporate financial decisions in a value-based decision-making framework. In a first step, the nature of the network phenomenon is outlined from a complex systems perspective. Based on this framework, a research approach to the analysis of network growth and network resilience is formulated with structural and locational network methods complemented with dynamic network tools. In the main part of the paper, the graph theoretical approach is applied to illustrative case studies. In order to outline the potential of network research for corporate financial management, the network results are compared to conventional results. In essence, the assessment of the case studies reveals that the nonlinearity of networks is vitally influencing the risk and the value of corporations. Consequently, according to shareholder value principles, managers have to be aware of the implications of network externalities and should incorporate them in their managerial decision framework. Particularly the explicit investigation of vital accounting data, such as revenues, investments, and provisions, for nonlinear network interactions helps to explain the observed deviations between conventional linear heuristics and the complex real-world development. Therefore, the proposed internalization of the network externalities with the graph theory establishes a link between network analysis, corporate finance, and strategic business development. Furthermore, it emphasizes the necessity for financial research on the design of optimal network management strategies and the benefits of a general diffusion of the underlying complex systems principles in management.
*PASCAL MOSSAY - Economic Geography and Rational Expectations
Author(s):
Pascal MOSSAY, Universidad de Alicante, Spain
Abstract:
Eventhough the consequences of the rational expectation assumption have been somewhat explored in the literature concerning two-country models, we are not aware of any attempt to explore the role that rational behavior may have in a continuous spatial economy. Our model builds on Fujita et al. (1999)'s racetrack economy. However, here, workers are assumed to have a perfect foresight ability. Our result reemphasizes the role of the local market structure on the convergence process: like in Fujita et al. (1999), scale economies at the local level and free mobility of workers contribute to spatial divergence. However, unlike in the corresponding myopic case studied by Mossay (2003), the size of agglomerations increases with the taste for variety and the expenditure share on manufactured goods, and decreases with transport costs. The role of rational adjustments with respect to myopic adjustments is to thus distort the relationship between the amplification factor and the wavelength.

*CEFN HOILE - Concepts, Formalisms, Methods and Tools

*DANIEL JOSHUA STEINBOCK - Self-Modeling Networks
Author(s):
Daniel Joshua Steinbock, University of California, Santa Cruz, USA
Marko Antonio Rodriguez, University of California, Santa Cruz, USA
Abstract:
Landscape metaphors are ubiquitous in descriptions of complex systems, intuitively representing the non-homogeneous search space explored by adaptive agents and evolving systems. In many fields, formal landscapes models are used to understand how systems search for optima in a space of possibilities. Physical systems explore a space of possible states for ones that minimize free energy. Living organisms search for peaks in genetic and ecological fitness landscapes. Engineers explore design spaces for good solutions just as law-makers search for good public policies. Critical to these search processes is how agents measure optimality. We consider a class of landscape searching (also known as global optimization) where agents use knowledge encoded in the structure of the landscape for navigation. We present a model based on a probabilistic network where a particle's position at a given node denotes the current state of the system and each outgoing link to other nodes is weighted by the probability of the system transitioning to that state from the current state. Models like this have been used to simulate complex dynamical systems governed by stochastic processes and are meant to represent statistical features of an ensemble of system trajectories. Hence it is appropriate to speak of a particle swarm traveling the network, tracing out unique paths, revealing the characteristic landscape of the model system. Attractors in the landscape are defined as regions where we are more likely to observe a particle at any arbitrary moment. As a practical example we show how these landscape/particle models are highly suited to describing the macrostructure of social networks and modeling complex exchange processes that operate across them, namely status and reputation formation. For instance, the landscape could provide a map of expertise within an organization, where attractors signify regions of high expertise because they are more likely to receive tokens (particles) of esteem. This paper contributes the following discovery: with the addition of a single construct we show how all such models of dynamical systems contain an intrinsic self-modeling algorithm such that a proper subset of the state-space can plausibly represent the entirety. This algorithm has extremely useful applications to the human sphere: enabling distributed problem-solving in groups, optimizing division of labor in organizations, and identifying representatives that accurately reflect public opinion for policy-making in government.
*ELIZAVETA PACHEPSKY - A conceptual framework for mechanisms of self-organization
Author(s):
Elizaveta Pachepsky, University of California Santa Barbara, USA
Bernardo R. Broitman, University of California Santa Barbara, USA
Abstract:
The concept of self-organization is increasingly used to describe patterns arising in systems in many fields such as biology, physics, sociology and economics. However, no general theoretical understanding exists as to the mechanisms that lead to self-organization in systems. Some of the main reasons for this are that definitions of self-organization vary across different fields, and occurrence of self-organization is mainly demonstrated by examples. We examine current theories and models that deal with self-organization and synthesize them into a general conceptual framework. This framework allows us to examine a diverse collection of approaches from the same perspective and points to the mechanisms that generate self-organization.
*PABLO SANCHEZ-MORENO - Information Planes for Complex Systems
Author(s):
J.S. Dehesa, University of Granada, Spain
E. Romera, University of Granada, Spain
R.J. Yañez, University of Granada, Spain
Abstract:
The concept of information plane as a new tool to distinguish between the regular and chaotic parts of the eigenvalue spectrum of complex systems is suggested. Various information planes ( Fisher-Shannon, Cramer-Rao,..) are considered to define new statistical measures of various complex systems with different character, ranging from atoms and nuclei to linguistic systems.
*TED BACH - Using SIMP, a Laboratory for Cellular Automata and Lattice-Gas Experiments
Author(s):
Ted Bach, Boston University, USA
Tommaso Toffoli, Boston University, USA
Abstract:
We introduce SIMP, a programming environment for cellular automata and lattice-gases, and demonstrate its use as a laboratory for studying n-dimensional, spatially-distributed systems in which complex macroscopic phenomena arise as the aggregate of some simple, local, spatially-invariant, microscopic dynamics. By way of example, we demonstrate methods of specifying, visualizing, statistically characterizing, scripting, and interactively controlling SIMP experiments. SIMP experiments are expressed as Python scripts and implemented by our fast, hardware-flexible STEP platform. SIMP is available as free, open-source software from pm.bu.edu.
*WILLIAM SULIS - Archetypal Dynamical Systems
Author(s):
William Sulis, McMaster University, Canada
Abstract:
Archetypal dynamics is a new field which explores the nature of meaning laden information flow in complex systems. Information flow within a complex system is organized by virtue of a semantic frame. The semantic frame is realized by the complex system, and interpreted by the agents within the system, and by external users which interact with the system. Archetypal dynamical systems provide a formal representation of the realization relation. In the simplest terms an archetypal dynamical system is a combinatorial game played out over a labelled multigraph, which represents the coherence relationships existant among a collection of informational instances. The formalism will be described, along with several measures of informational coherence based upon the surreal number field.
*LEN TRONCALE - Is Artificial "Systems" Research Possible?
Author(s):
Len Troncale, California State Polytechnic University, USA
Abstract:
Artificial “life” research was an early specialty that contributed to the current explosion of work on complex systems. This talk will explore the possibility of a new, related, but unique specialty called Artificial “Systems” research (ASR). ASR would be based on a general model of systems that consists of a network of 75 systems mechanisms and hundreds of “linkage propositions” that describe how they influence each other. The propositions and their influences appear to be programmable in Prolog or LISP. The resulting dynamic network is an expression of what is common to many natural and social systems. ASR would place this network in computer space and systematically add, alter, or remove specific linkages to measure across computer time how the change affects overall measures of systems stability and/or performance. This talk will present arguments for the need for Artificial Systems Research programs and explore its initial tenets and assumptions. The talk will explore the similarities between Artificial Life Research and Artificial Systems Research. For example, both exist in computer space and require considerable, often dedicated, computer resources and time. The talk will also explore the differences between Artificial Life Research and Artificial Systems Research. For example, ASR programming would model general systems process interactions and not depend solely on characteristics of bio-systems as ALR does. The former depends on genetic algorithms using “mutation” and “recombination” of information sequences, while the latter does not. Also, selection, if it exists at all in ASR, would not be based on evolutionary selection. ASR would model a process of “emergence” that is distinct from the process of “evolution” as programmed in ALR. The talk will also list problems or obstacles inhibiting the development of ASR including identification of what might be optimized in systems structure and process when modeled as a general system rather than a specific, real system. How could ASR runs measure that optimization? What would be a generalizable systems output? The relationship between, or usability of ASR for work in Systems Pathology will be examined. Important challenges will be posited, such as, what would be the expected results of ASR, or what are the correspondence principles between Artificial Systems research and its results and the real world? The talk will also explore practical organizational needs for doing ASR and its prospects for funding.
*DAVID H. WOLPERT - Metrics for sets of more than two points
Author(s):
David H. Wolpert, NASA, USA
Abstract:
As conventionally defined, a metric is a function of two points that, intuitively, tells us how "spread out" from one another those two points are. However there are many scenarios where we need a function that tells us how spread out from one another a set of more than two points is. Potential applications of such an extension range throughout machine learning and statistical analysis. In particular, both supervised and unsupervised learning, as well clustering, are conventionally done using metrics that only take two arguments at a time. Accordingly, such techniques need to somehow combine values of a metric for all pairs of elements of a data set. The generalization of a metric to more than two points would allow such techniques to instead be applied to entire data sets at once. This paper shows how the conventional definition of a metric, and in particular the triangle inequality, can be extended to apply to collections of more than two points. This extension allows some of the points to be duplicates of one another. According, a natural generalization of it allows points that occur fractionally. This allows the extension to tell us how "spread out" a probability distribution is. It is also shown how the extended definition of a metric can be used to "bootstrap" from a measure of how spread out a set of points is to a measure of how spread out a set of such sets is. This can then be used to give a metric for how different from one another two probability distributions are.
*DAVID H. WOLPERT - Self-dissimilarity as a high dimensional complexity measure
Author(s):
David H. Wolpert, USA
William Macready, NASA, USA
Abstract:
For essentially any system commonly characterized as "complex", the spatio-temporal patterns exhibited on different scales differ markedly from one another. Biological organisms obviously exhibit this nature. The Earth climate system is another excellent example, having very different dynamic processes operating at all spatiotemporal scales. Complex human artifacts also share this property, as anyone familiar with large-scale engineering projects will attest. Conversely, the patterns at different scales in "simple" systems (e.g., gases, crystals) do not vary significantly from one another, and therefore allow the entire pattern over all scales to be encoded into a short description. It is the self-similar aspects of such systems, as revealed by allometric scaling, scaling analysis of networks, etc., that reflects their inherently simple nature. Accordingly, it is the self-dissimilarity (SD) between the patterns at various scales that constitutes the complexity "signature" of a system. Intuitively, such a signature tells us how the information stored at one scale in a system and its processing at that scale is related to the information and processing at the other scales. Highly different information processing at different scales means the system is efficient at encoding as much processing into its dynamics as possible, whereas having little differences between the various scales is often associated with robust dynamics. The SD signature of a system is a function purely of the spatio-temporal pattern of that system, and does not directly depend on the rules generating that pattern. This means that while SD signatures may incorporate prior knowledge about the system generating a data set (to statistically extend that data set), they are functions of data sets rather than of models of the underling system. They therefore provide model-free synopses of the information-processing of the underlying system. This model-independence allows SD analysis to be applied to a broad range of types of systems/data, and for the associated SD signatures to be compared. This suggests that such analysis may prove quite useful in formulating a broadly applicable science of complex systems. This paper presents a formalization of SD, taking care to show how it differs from previously suggested complexity measures. We demonstrate this formalization on several examples, including the logistic map both with and without noise. Other examples are various two-dimensional patterns, including photographs, crystal patterns, and multifractals. These examples illustrate the potential utility of SD analysis for understanding complex systems.
*HIDESHI ISHIDA - Linearly time-dependent information on invariant set
Author(s):
Hideshi ISHIDA, Osaka University, Japan
Hideo KIMOTO, Osaka University, Japan
Abstract:
In this paper a family of information I(b) with a parameter b is introduced and the reason why its coarse-grained information Ic(b) becomes to be independent of b near t=0, i.e. the probability density on the invariant set is uniformized and Ic(b) shows linearly time-dependent behavior, was theoretically examined. If the time-dependent probability measure of coarse-grained systems is described by a master equation (ME), a diffusion effect is necessarily brought into the systems, and the uniformization is caused by the information production originated from the diffusion effect. The production is completely corresponds to the entropy production, which is generally related to the phase space volume contraction rate in dissipative systems, and, therefore, the phenomena can be observed for a wide class of dynamical systems as irreversible processes go.
*SETH TISUE - NetLogo: A Simple Environment for Modeling Complexity
Author(s):
Seth Tisue, Northwestern University, USA
Abstract:
NetLogo (Wilensky, 1999) is a multi-agent programming language and development environment for modeling complex systems. It is designed for both education and research and is in use across wide range of disciplines. In this talk, I will outline the design principles underlying NetLogo and describe recent and planned enhancements. Our goal is to make complex systems modeling accessible to students and researchers who are not professional programmers or have never programmed before. The NetLogo language extends Logo to support large numbers of agents interacting concurrently. The NetLogo environment includes tools for programming, building a user interface, and interacting with a model as it runs. NetLogo runs on any platform that supports Java. Models can be run as applets in a web browser. Over 140 example models are included. Notable recent enhancements to NetLogo include increased speed, more flexible control of graphics, improved exchange of data with other applications, and the ability for users to write their own add-on modules to extend NetLogo's capabilities. Recently, we have begun a project to use NetLogo to model the growth of cities; as part of this and other ongoing projects, we will continue to expand NetLogo's capabilities as a research tool.
*RICARDO NIEVA - An ANC Analytical Payoff Function for 3-Agent-Multistage-Network-Games with Endogenous Coalitions and Communication Structures
Author(s):
Ricardo Nieva, Concordia University, Canada
Abstract:
For any three-agent network game, we extend the Aumann-Myerson (1988) (Shapley) solution by allowing non-myopic pairs of players to propose at each stage not only bilateral communication links (necessary for cooperation), but also a pair of individual payoffs. For a link to form the sum of the payoff proposals has to be equal to the sum of both agents' Myerson Values in the prospective graph implied by the added link. For equilibrium selection, we allow agents to use a "two-agent" dynamic extension of the Nash bargaining rule (NBR). This rule selects among multiple credible Nash equilibria of the strategic form of the proposal game. Loosely speaking, the strong Pareto efficiency of the NBR implies in cases of indifference of one agent towards linking with two partners that earlier agreements will be honoured first. Thus, the agent will will end up linked with only the first partner if the latter is as least as better off by the agent doing so. We provide by construction an alnost non-cooperative (ANC) analytical payoff function. The outcome is always efficient. If multiple graph structures exist, they are payoff equivalent. For strictly superadditive games, we only predict two link graphs. A necessary condition for a one link or coalition of two agents to form is that the colluded can achieve what the grand coalition can.

*HIROKI SAYAMA - Evolution and Ecology

*STEVEN S. ANDREWS - Simulated niche partitioning by bacteria
Author(s):
Steven S. Andrews, Lawrence Berkeley Laboratory, USA
Adam P. Arkin, Lawrence Berkeley Laboratory, USA
Abstract:
Bacterial evolution is simulated for continuous growth conditions using a population balance method, in which net bacterial growth and nutrient uptake are expressed with differential equations. Bacterial metabolism for the simulation is simplified from a highly complex biochemical network to three different designs that are composed of a simple module. It is based on the Michaelis-Menten reaction scheme and obeys thermodynamic constraints. When the simulated metabolic network has two modules in parallel that metabolize different substrates, and is grown with both substrates, evolution leads to coexisting bacterial populations that specialize on different nutrients. Upon increasing the bioreactor flow rate, the bacterial community undergoes a rapid transition to a single generalist population that consumes both substrates, leading to the possibility of experimental observation. This transition is reversible and hysteretic. In a different design, the modules are placed in series and the cell permeability to an intermediate metabolite is allowed to evolve. Again, it is possible to achieve stable coexisting populations: one population metabolizes the substrate to the intermediate metabolite while the other consumes the intermediate metabolite. Community matrices and stability criteria are discussed.
*ARIEL CINTRON-ARIAS - Rumors on Complex Attractors
Author(s):
ariel cintron-arias, cornell university, United States
Carlos Castillo-Chavez, Arizona State University, United States
Abdul-Aziz Yakubu, Howard University, United States
Abstract:
Social psychologists have developed the theory of psychology of rumor, where the traditional focus has been given at the individual level. On the other hand there are studies concerning the dynamics of rumor propagation at a collective level. These studies include experimental data collection as well as mathematical modeling (epidemic models). We consider rumor "invasion" into populations with strong fluctuations in density. Prior to the rumor arrival, the dynamics of the target population is assumed to be at a demographic "steady-state". In fact, it is modeled by a preselected attractor. We assume that while the rumor circulates it divides the target population into two classes; spreaders, and non-spreaders. The transitions between classes are functions of the contact rates and the proportion of spreaders. Will the spreader population survive? and if it does; Will it settle on a different attractor?; How does the dynamics of the rumor compare to the dynamics of its analogue epidemic process? Rumor dispersal between two patches is briefly addressed.
*JACOB BEAL - Predictive Modelling for Fisheries Management in the Colombian Amazon
Author(s):
Jacob Beal, MIT, USA
Sara Bennett, Parque Nacional Natural Amacayacu, Colombia
Abstract:
A group of small indigenous communities and Amacayacu National Park are experimenting with the design and implementation of regulations intended to recover and maintain the integrity and productivity of their shared natural resources, especially the fish populations on which they depend. We are modeling the behavior and stability of alternative management schemes for this system with the objective of developing guidelines for strategic future investment of time, energy, and money. The goal is to improve the probability of actually achieving fair, sustainable and community-managed subsistence fishing in the region. Sustainable long-term use of common pool resources is an inherently difficult political challenge. The Amacayacu communities face the additional complications of complex and poorly-understood ecosystems, uncontrolled demographic growth and cultural change in the population of legitimate resource appropriators, weak and often corrupt national and regional institutions, and primitive transportation and communication infrastructure. Balanced against these are the positive factors of a relatively small and well-defined population of users, appropriation practices that can be monitored relatively effectively at relatively low cost, recognition of the legitimacy of local regulations by regional and national institutions, and an explicit commitment on the part of the appropriator population to developing fair, sustainable, and democratic ground rules for natural resource use. We approach this problem from the perspective of self-organizing commons management principles [1] and present a first-draft model for human/fish interactions in the Amacayacu region based on common-pool resource non-cooperative games [2], along with analysis and comparison of some of the model's preliminary predictions with field results. [1] Ostrom, "Governing the Commons: The Evolution of Institutions for Collective Action". Cambridge, Cambridge University Press, 1990. [2] Ostrom, Gardner, and Walker, "Rules, Games, & Common Pool Resources". Ann Arbor, University of Michigan Press, 1994.
*DONNA K. FISHER - Complexities of River Basin Management, The Case of the Pitimbu River in Brazil.
Author(s):
Donna K. Fisher, Georgia Southern University, USA
Paulo Rower, Georgia Southern University, paulorower@coastalrivers.org
Abstract:
Complexities of River Basin Management, The Case of the Pitimbu River in Brazil. This research proposes to answer the question: What is the most efficient allocation of water resources between the diverse stakeholders in the Pitimbu River Basin in Rio Grande do Norte, Brazil? The World Health Organization estimates that 1.7 million people die each year from diseases related to contaminated water (WHO 2002). In the northeast of Brazil, 34% of the residents lack running water, while over 60% are without adequate sanitation (World Bank 2002). Natal, Rio Grande do Norte (RN), capital of one of the northeast states, relies on the Pitimbu River for approximately 40% of its water (Borges 2002). The city is one of many stakeholders along the Pitimbu; others include hotels, agribusiness firms, paper mills, agricultural producers, and other municipalities. The Pitimbu River has been the focus of several ongoing environmental protection activities (Souto Filho 2002, SERHID/RN 2001). However, most projects have been directed at enforcement of regulatory violations by industries along the river. This project utilizes the Policy Relevant Monitoring Systems (PRMS), a model designed to monitor systems that have direct relevance to the management of natural resources (Hazell et al, 2001). This research extends the PRMS model to specifically include health-related water issues. The complex interrelationships between the diverse stakeholders make river basin management an ideal topic for exploration with system dynamics modeling. Stella system dynamics modeling software will be used to model the Pitimbu River Basin PRMS.
MBABAZI DISMAS - Trophic structure of the fish communities in the Kyoga Basin lakes (East Africa) based on stable Nitogen and Carbon isotopes.
Author(s):
Mbabazi Dismas, Fisheries Resources Research Institute, P.O. Box 343, Jinja, Uganda, East Africa, Uganda
Mbabazi Dismas, Fisheries Resources Research Institute, P.O. Box 343, Jinja, Uganda, East Africa, Uganda
Ogutu-Ohwayo Richard, Fisheries Resources Research Institute, Box 343, Jinja, Canada
Hecky E. Robert, Biology Department, University of waterloo, 200 University Avenue, Waterloo.ON, NL-3GI., Uganda
Country: Uganda, E-mail: , Name: Orach-Meza Faustin,Organization: Lake Victoria Environmental Management Project, Box 5, Entebbe Country: Uganda, E-mail: , Name Makanga Boniface, Organization: Zoology Department, Makerere University, Box 7062, Kampala.
Abstract:
Trophic structure of the fish communities in the Kyoga Basin lakes (East Africa) based on stable δ15N and δ13C isotopes . Mbabazi D1., R. Ogutu-Ohwayo2, J.S. Balirwa1., R.E. Hecky3, F. Orach-Meza4, B. Makanga5, S.B. Wandera1 and G. Namulemo1 Fisheries Resources Research Institute,P.O. Box 343, Jinja, Uganda1 Lake Victoria Fisheries Organisation, P.O.Box 1625, Jinja, Uganda2 Biology Department, University of Waterloo, 200 University Avenue, Waterloo, ON, N2L-3G1; Canada3 Lake Victoria Environmental Management Project, P.O.Box 5, Entebbe, Uganda4 Zoology Department, Makerere University, P.O.Box 7062, Kampala, Uganda5 Abstract Until 20 years ago, Lake Kyoga had a similar fish fauna to that of Lake Victoria. Lake Victoria alone contained at least 500 fish species comprising at least 12 trophic groups out of at least 350 species, dominated by a monophyletic species flock of haplochromine cichlids, about 99% of them endemic exploiting virtually all food sources in the lake. Introductions of exotic species prompted by overfishing, and habitat degradation fuelled by an expanding human population resulted into reduction of the native species, number of trophic groups and led to the simplification of the lakes’ food web and therefore reduced ecological efficiency of the lake. Between 1998 and 2001 this study examined the trophic structure of the fishes present in Lake Kyoga, and the less impacted satellite lakes in its basin, using stable carbon (δ13C) and nitrogen (δ15N ) isotopes. Results indicated that, isotope composition of the fishes in the main lake Kyoga ranged from –25.4 to –13.8 ‰ for δ13C and 5.3 to 9.9‰ for δ15N. Among the satellite lakes, Bisina recorded the largest range both for δ13C (–25.9 to –11.3 ‰) and δ15N (3.2 to 10.4 ‰). Lakes Nawampasa and Nakuwa recorded the least ranges for δ13C (-24.4 to -21.7 ‰) and δ15N (4.1 to 6.3 ‰) respectively. Noteworthy, Lake Nakuwa with the least ranges in δ15N like Lake Kyoga contain the predatory Nile perch. The broad ranges in (δ15N) indicate a more diverse trophic status in the Nile perch free satellite lakes. The broad ranges in (δ13C) indicate a wide range of food sources. Lakes with less disturbance had a narrower range of food sources compared with the more disturbed ones. The Kyoga satellite lakes are important refugia for functional and species diversity lost in lakes Kyoga and Victoria and deserve targeted conservation measures to protect them.
*GUY HOELZER - A new model for exploring emergent phenomena in spatially explicit ecology and evolution
Author(s):
Guy Hoelzer, University of Nevada Reno, USA
Lael Parrott, University of Montreal, Canada
Abstract:
We have begun to develop and explore a model of ecology and evolution in a spatially explicit context. At this point, either sexual or assexual individual animals are represented as individual agents that can consume energy (possibly in the form of other animal agents), move across the landscape in limited ways, reproduce, and die. Death may occur prior to reproduction if the animal has not obtained a sufficient amount of energy. The foundation of the food web is represented by plants, which are currently represented as a lawn that is seeded (in the biological sense) randomly with regards to space. The genomes of the animals are represented by strings of numbers analogous to the strings of nucleotides in DNA, which determine the energetic requirements of the organism. Mutation, and sexual recombination in the sexual mode, provide sources of novel genetic/ecological types. We plan to initially study the self-organization of individual gene pools (evolution of discrete polymorphisms; symmetrybreaking) driven by both disruptive selection and the scale-dependent threshold for the emergence of locally distributed forms. We will also examine how imposed (not responsive to the bios) heterogeneities on the landscape influence the patterns realized through such self-organization. We also expect that this model will permit emergent speciation; so we can use it to explore the evolutionary outcomes of ecological interactions among reproductively isolated, and semi-independently self-organizing, species. Taking the opposite perspective, we will also be able to explore the ways in which the evolution of species molds patterns of ecological interaction. There is a great deal of mathematical mean-field theory that considers these kinds of interactions, and our model should be able to recreate the outcomes predicted by this body of theory when approximating the mean-field assumptions. We anticipate that the novel contribution of this model to theory will come from systematically studying the conditions leading to emergent structures in the population biology context and in revealing the consequences of bi-directional ffeedback between ecological and evolutionary processes.
*YUN ZHOU - An approach to holistic ecological risk assessment: Food web responses to a warmed climate as a case study
Author(s):
Yun Zhou, University of California, at Berkeley, USA
Ulrich Brose, Rocky Mountain Biological Laboratory, USA
William Kastenberg, Uc, at Berkeley, USA
Neo Martinez, Rocky Mountain Biological Laboratory, USA
Abstract:
Abstract There is ample evidence on the ecological impacts of recent climate change, from polar terrestrial to tropical marine environments. The Earth's climate has warmed by approximately 0.6 oC over the past 100 years. IPCC has projected the mean global surface air temperature to increase by 1.4 oC to 5.8 oC from 1990 by 2100, with the magnitude of the increase varying both spatially and temporally. Coastal ocean temperature increases are expected to be slightly lower than the IPCC projected increases for land, but still are expected to rise measurably. Responses by individual species to climate change may disrupt their interactions with others at the same or adjacent trophic levels, as well as their metabolic and reproductive processes. The inevitable uncertainties and ambiguities and highly complex dynamics associated with ecosystems present considerable challenges when assessment of potential ecological risks is attempted. The objective of this study is to examine robustness of ecosystems under a warmer climate (?). Specifically, whether the persistence of species in food-webs will be reduced by an increased metabolic rate expected under a warmed climate will be examined. In this study, two target food webs, the marine food web and the terrestrial food web, are studied. The theory of trophic interactions, competition among producer species by synthesizing nutrient-dependent growth of producer species, and one of food-web structure models, the niche model are used. Temperature is assumed as one of the primary factors to affect metabolic rate through various mechanisms. Three climate scenarios are studied: the best scenario with a minimal increase in global temperature of 1.4 oC, an average scenario with an average increase in global temperature of 3.6 oC, and the worst scenario with a maximum increase in global temperature of 5 oC. Three different types of climate are considered: the tropical climate, the temperate climate, and the Arctic/Antarctic climate. A computer simulation tool combining a nonlinear bioenergetic model of complex ecological networks with a food-web structure model is used to simulate food-web responses to metabolic rate for marine and terrestrial food webs. A thirty-species food web is assumed for every single simulation. Major outputs from simulations include species richness, connectance, links per species, and percentages of top, intermediate, and basal species in a food web. These properties in the two target food webs under each climate scenario in each type of climate are simulated and compared. The outcome of this study should help understand ecological risks associate with climate change.
TOLIBJON E. BURIYEV - Bifurcation Study of Self-Oscillatory and Chaos Regimes in Predator-Prey Systems.
Author(s):
Tolibjon E. Buriyev, Dept. of Mathematics Samarkand State University, Uzbekistan
Vafokul Ergashev, Samarkand State University, Uzbekistan
Abstract:
For models of the dynamics of the quantity of a predator-prey systems such as the generelized three dimensional Volterra-Lotka model with the additional effects of an intraspecific competition and saturation in predator populations under stationary and periodic environmental fluctuations we show existence of stable Self-Oscillatory and Chaos regimes of behaviour. Investigated them bifurcations at changes value of parametrs and initial values. The investigations has been carried out qualitatively based on the bifurcation theory and perturbation theory, as well as by means of a computer experiments.
*MICHAEL HAUHS - A new type of models for managed ecosystems
Author(s):
Michael Hauhs, University of Bayreuth, Ecological Modeling, Germany
Holger Lange, Norwegian Forest Reserach Institute, Norway
Abstract:
Using the encoding-decoding metaphor of Robert Rosen, a model in the natural sciences can be characterized as a relationship between a natural and a formal system. This modeling relation with its notion of states from which behaviour can be deduced is the conventional paradigm in dynamic systems theory. The encoding operation from the natural to the formal system requires an intersubjective (among scientists) agreement about observations that can be encoded as states of a formal system. However, the process of encoding faces serious problems when applied to living systems as already pointed out by R. Rosen. Some of the states act as memory and are unobservable for any external agent (at least in all practical situations). In ecological modeling, these models have not been able yet to produce a single counter-intuitive logic entailment (in the formal system) that could be confirmed after decoding into the corresponding natural system; in other words, no successful prediction of ecosystem behavior has been made for this type of models. Most models in ecosystem research are overparameterised and calibrated to observations, thus they are used effectively to interprete and evaluate observations rather than explain and predict them. We propose a new type of models for managed ecosystems which is complementary to the one of Robert Rosen and overcomes the above problems. It is based on a notion of interactive computing that has recently been developed in theoretical computer science. Using sustainable forestry as paradigmatic example, we relate “real” behaviour (encountered in interactive management decisions in silviculture) with “virtual” behaviour in which the same interactive tasks are posed through a model interface. The encoding operation from “real” to virtual behaviour requires an intersubjective (among forestry experts) agreement about aspects within their memories that can be encoded as behavioral patterns of a virtual system. We argue that this process of encoding of the heuristics of experts is the appropriate approach when dealing with interactive processes such as life. Implications for ecosystem modeling are discussed with respect to forest growth models.
*RICARDO AZEVEDO - The Simplicity of Metazoan Cell Lineages
Author(s):
Ricardo Azevedo, University of Houston, USA
Abstract:
Developmental programs are thought to be both highly complex and highly modular, but there are no generally accepted ways of quantifying either of these properties. Here we introduce a measure of cell lineage complexity: the length of its shortest description. We then use this measure to estimate the complexity and degree of modularity of the embryonic lineages of four metazoan species from two different phyla. We find that these cell lineages are significantly simpler and more modular than expected by chance. Furthermore, evolutionary simulations suggest that the complexity of the embryonic lineages surveyed is near that of the simplest lineages evolvable assuming strong developmental constraints on the spatial positions of cells. We propose that selection for decreased complexity plays a major role in molding metazoan cell lineages.
*PAN-JUN KIM - Spatio-temporal Dynamics in the Origin of Genetic Information
Author(s):
Pan-Jun Kim, Korea Advanced Institute of Science and Technology, Republic of Korea
Hawoong Jeong, Korea Advanced Institute of Science and Technology, Republic of Korea
Abstract:
We study evolutionary processes induced by spatio-temporal dynamics in prebiotic evolution. Using numerical simulations we demonstrate that hypercycles emerge from complex interaction structures in multispecies systems. In this work we also find that `hypercycle hybrid' protects the hypercycle from its environment during the growth process. There is little selective advantage for one hypercycle to maintain coexistence with others. This brings the possibility of the outcompetition between hypercycles resulting in the negative effect on information diversity. To enrich the information in hypercycles, symbiosis with parasites is suggested. It is shown that symbiosis with parasites can play an important role in the prebiotic immunology.
*JORGE DE BARROS PIRES - Cognitus
Author(s):
Jose Wagner Garcia, Petrobras, Brasil
Jorge de Barros Pires, Petrobras, Brasil
Jorge Vieira, Petrobras, Brasil
Lauro F. B. da Silveira, Petrobras, Brasil
Fernando Pellon de Miranda, Petrobras, Brasil
Lucia santaella, Petrobras, Brasil, Moacir Carnelos Filho, Petrobras, Brasil,
Abstract:
PETROBRAS (the Brazilian national Oil Company) has built a pipeline to transport crude oil from the Urucu River region to a terminal in the vicinities of Coari, a city located on the right side of the Solimoes river. Tankers then ship the oil to another terminal in Manaus. Between dry and wet seasons the Solimoes river oscillates water level reaching up to 14-meter difference. This strong seasonal character of the Amazonian climate gives rise to four distinct scenerios in the annual hydrologic cycle: low, high, receding and rising waters. These scenarios constitute the main frame for definition of oil spill response planning in the region, since flooded forest and flooded vegetation are the most sensitive fluvial environments to oil spills. The actual methodology applied to evaluating environmental risk areas includes image processing, cartographic conversion and generation of value-added product using 3D visualisation. It focuses on improving information supply about oil spill environmental sensitivity in Western Amazon in order to improve analysis and interpretation of remote sensing and digital topographic data. It is undeniable that this index has provided a great deal of information about the oil spill environmental sensitivity. However, this view is poor and ambiguous when applied to understanding of Amazon environment. Unfortunately, it is not sufficient to cover the whole complexity of Solimoes River's patterns. The River seasonal variation is represented by intricate arrangement that change in time. The hydrological cycle produce and do away with flooded forest. The landscape will regulate the flora and wildlife spatial distribution and the riverside communities' habits. This web of relations is progressive and evolutionary. A reductionist treatment is not possible. A wider approach to this problem is needed. This problem gives rise to the COGNITUS project. It is a theoretical and empirical study of the Amazon complex system. From molecular to ecological and evolutionary scales it is treated as interdisciplinary research. There is a deep interaction of Mathematics, Art, Philosophy, Semiotics, Computational Science, Robotics, Remote Sensing, Chemistry, Hydrology, Geology, Ecology, Botany, Genetics, Sociology, Economics, among others. They become partners in building up a cognitive tool to identify esthetical and logical patterns in the Solimoes river flood plain. Our group intends to establish a regular framework for understanding the natural complex process that occurs in this place. We are starting from the hypothesis that aesthetic perception, logic of relatives, and complexity theory must be included to understanding these environmental relations. It will allow us to build up a wider sensibility map that will provide a solid base for designing better strategies to oil spillage contingency. It is fundamental to reduce risks of accidents and to allow PETROBRAS to elaborate more efficient oil spill response planning.
*THOMAS HILLS - Animal foraging and the evolution of goal-directed cognition
Author(s):
Thomas Hills, University of Texas at Austin, United States
Abstract:
One of the overarching lessons of complexity theory is that complex behaviors often emerge out of less complex local rule structures. Evolutionary theory holds that complex phenotypes evolve out of less complex phenotypes. This work presents evidence for the evolution of cognition out of simple molecular structures initially operating in the control of spatial foraging behavior. The evidence is constructed from and helps to unify observations from behavioral ecology, mathematical biology, molecular genetics, neuroscience, attention studies, and research on human goal-directed pathologies. Similarities in foraging behavior across eumetazoans (i.e., vertebrates, insects, and mollusks) suggest the early evolution of a foraging behavior called area-restricted search. Area-restricted search is characterized by initially concentrated searching around local areas of highest historical payoff, followed by more global and less focused searching as payoffs become infrequent. Mathematical models of area-restricted search show that it is optimal when resources are clumped and when only temporal information is available about resource density. I present a genetic algorithm that supports the mathematical findings and describes minimal molecular structures necessary for the evolution of area-restricted search. I show how these structures are present in existing neural pathways controlling area-restricted search and are internalized in the control of goal-directed behaviors in more recent vertebrates. Human pathologies of goal-directed behavior (e.g., ADHD, obsessive compulsive disorder, schizophrenia, drug addiction, and Parkinson’s Disease) share molecular similarities with foraging behavior, involve both motor and cognitive dysfunctions, and also appear to organize themselves along the gradient of behavior described by area-restricted search, from perseverative to interrupted. Studies of priming, memory chunking, and the prefrontal cortex provide evidence for the existence of hierarchical cognitive neighborhoods. Taken together, this work suggests that cognitive neighborhoods are the evolutionary emergent world of the foraging mind.

*ALI MINAI - Learning / Neural, Psychological and Psycho-Social Systems

*BROCK DUBBELS - Teaching Complex Systems in the Classroom with Video Games
Author(s):
Brock Dubbels, The University of Minnesota, USA
Abstract:
Teaching Complex Systems in the Classroom with Video Games Poster or presentation International Conference on Complex Systems 2004 May 16-21, 2004, Boston, MA Brock Dubbels The Center for Cognitive Sciences 310 Elliott Hall The University of Minnesota Minneapolis, MN 55455 Dubbe003@umn.edu (612) 827-2714 Abstract: A case will be made for exploring the use of commercial video games to teach and develop skills necessary in understanding complex systems and rule based environments. Specifically, one game, Civilization III will be examined and presented as opportunity for students to engage in play, as well as be led with guided reflection to deconstruct the experience of the game as a representational system created as a virtual environment, where rules and assumptions about the world are simulated, and whether these rules and depiction of the system are representational and how these rules and assumptions work in co-variation to create response to student developed tactics and strategies. Key elements involve the role of decision making, ability to differentiate a complex system from a simple system, and use of supporting materials to question the assumptions that the game was built upon to engage players in strategy generation for interacting in a complex and dynamic virtual environment with quantifiable outcomes, and examining history and our knowledge of the world as factual, but process and context dependent.
*OREN ZUCKERMAN - Hands-on modeling and simulation of systems concepts
Author(s):
Oren Zuckerman, MIT Media Lab, USA
Mitchel Resnick, MIT Media Lab, USA
Abstract:
The behavior of systems is best understood through interactive simulations. In addition, modeling of simple systems promotes better understanding of core systems concepts. In recent years, much progress has been done in the field of modeling and simulation tools for complex systems. Nevertheless, modeling of systems is not accessible to many students or novices. We present System Blocks, a new digital manipulative that enables hands-on modeling and simulation of dynamic systems. System Blocks are a set of physical blocks with embedded computation that provides an easier introduction to core concepts of systems such as positive and negative feedbacks, levels and rates, time delays, and behavior over time. System Blocks modeling language is based on the stocks & flows modeling technique, originally developed by Forrester as the foundation of the System Dynamics field. Each block has a predefined behavior, including stocks, inflows, outflows, constants, variables, and comparators. When connected together in different arrangements, these blocks can simulate different feedback-based systems, such as exponential growth and decay, goal-seeking growth and decay, as well as different oscillating systems. The simulated dynamic behavior is presented in real-time using a variety of mediums, including numerical display, line graph, moving lights, and MIDI-based sound. We report on an exploratory study we conducted with ten 5th grade students and five preschool students. These students used System Blocks to interact with systems concepts that are traditionally not taught at schools. We conducted one-on-one interviews with the students while they used System Blocks to model and simulate systems that relate to their own lives. We observed how the 5th grade students, with the appropriate tools and support, are able to learn concepts and techniques such as stocks and flows mapping, net-flow dynamics, and positive feedback to the extent that they can generate their own examples using these concepts. We observed how 4-year-old preschool students are capable of using System Blocks as a modeling and simulation tool, recognizing processes such as accumulation from a general representation of moving lights. Our preliminary findings suggest that using System Blocks as a modeling and simulation platform can provide an opportunity for students to confront their misconceptions about dynamic behavior, and help students revise their mental models towards a deeper understanding of systems concept.
*ARNO KLEIN - Activity patterns in the brain: breaking up the problem into pieces
Author(s):
Arno Klein, Columbia University, USA
Dr. Joy Hirsch, Columbia University, USA
Abstract:
Human brain image data can provide overwhelmingly complex patterns related to neural activity. These patterns vary across individuals, as do the shapes of their brains. To draw inferences about common activity patterns across individuals, it would be prudent to first match corresponding structures within which those patterns are observed. We have recently developed a method and software package for automating anatomical labeling of human brain image data (http://www.arnoklein.net/mindboggle.html) [1]. The program, called Mindboggle, breaks up gross brain anatomy into small pieces, and performs combinatoric matching between the pieces of different brains and applies a modified Self-Organizing Map algorithm [2] to label about these pieces. New extensions to Mindboggle include the ability to label activity data directly from the gross anatomy, as well as provide multiple anatomical labels for each data point based on the anatomy of multiple brains. [1] Klein, A., Hirsch, J. 2004. Mindboggle: a scatterbrained approach to automate brain labeling. (in press, NeuroImage) [2] Kohonen, T. 1997. Self-organizing maps, 2nd ed. Springer-Verlag, New York.
YUE JIAO - Neuro-fuzzy modeling of human fatigue
Author(s):
Yue Jiao, Dept. of Industrial & Manufacturing Systems Engineering, Kansas State University, Manhattan KS 66506, USA
E. Stanley Lee, Dept. of Industrial & Manufacturing Systems Engineering, Kansas State University, Manhattan, KS 66506, USA
Abstract:
Neuro-fuzzy modeling of human fatigue Yue Jiao and E. S. Lee Dept of Industrial & Manufacturing Systems Engineering Kansas State University, Manhattan, Kansas 66506 Email: eslee@ksu.edu Human fatigue is one of the critical factors influencing health, safety and work performance. However, fatigue is not well defined and is influenced by both physical and psychological factors. In fact, even the measurements or what variables to use to express the degree of fatigue cannot be uniformly defined. There exists a large volume of data with physiological variables such as heart beat and blood flow to measure stress or fatigue. However these data are obtained under extreme conditions with short duration, which is more suited for physical competition. In factory working environment or long distance driving, stress is mild with long duration. Under this mild condition, various measurements or independent variables have been used, some examples are duration, leg swelling, frequency of eye movement, the various physiological measurements and the various psychological measurements or scales such as the Borg rating. Because of the vague and not well-defined nature, neural-fuzzy adaptive network appears to be ideal to model human fatigue. In this work, the adaptive network is used to model human fatigue under both the extreme physical competition condition and the mild factory working condition. The adaptive network not only modeling, but, also, improves the model by learning as more data become available.
*IGOR YEVIN - Controlling Chaos in the Brain and Structure of Music Tonality
Author(s):
Igor Yevin, Mechanical Engineering Institute, Russian Academy of Sciences, Russia
Abstract:
Recent researches revealed that music tends to reduce the degree of chaos in brain waves. For some epilepsy patients music triggers their seizures. Loskutov, Hubler, Ott, and others carried out a series of studies concerning control of deterministic chaotic systems. It turned out, that carefully chosen tiny perturbation could stabilize any of unstable periodic orbits making up a strange attractor. Bondarenko shown in computer experiments a possibility to control a chaotic behavior in neural network by external periodic pulsed force or sinusoidal force. Low-dimensional outputs are observed when the frequency of the external force is close to delta-, theta-, alpha-, and beta frequencies. We suggest that music acts on the brain near these eigenfrequencies of self-excited oscillations in the neural network to suppress chaos. We explain the structure of music tonalities using concept of resonant action. One may propose that the aim of this control is to establish coherent behavior in the brain, because many cognitive functions of the brain are related to a temporal coherence.
*PIETRO PANZARASA - The Emergence of Collective Cognition in Social Systems
Author(s):
Pietro Panzarasa, Queen Mary, University of London, United Kingdom
Abstract:
The idea that social systems can retain forms of cognition through sharing in a way that transcends the mental states of the individual members has been around for quite some time However, despite the apparent enthusiasm for the subject, a number of important foundational issues still remain to be addressed. One of these is concerned with the nature of the relation connecting the two levels - individual and collective - at which cognition occurs. What is puzzling about this relation is the fact that one level - the collective - is determined by, depends on, the other - the individual - and yet takes on an autonomous existence. It is the objective of this paper to make this seemingly untenable combination of dependence and autonomy more intelligible. To this end, a general conceptual framework is proposed in which an account of collective cognition will be predicated on the notion of emergence. The main implications of this account will be explored, particularly in terms of multiple realisability, irreducibility and downward causation.
*ADAM DOBBERFUHL - ENVIRONMENTAL COMPLEXITY INFLUENCES VISUAL ACUITY IN CICHLID FISHES
Author(s):
Adam Dobberfuhl, New England Aquarium, USA
Jeremy Ullman, New England Aquarium, USA
Jessica Hunter, New England Aquarium, USA
Elizabeth Higgins, New England Aquarium, USA
Maggie Allan, New England Aquarium, USA
Mateo Nenadovich New England Aquarium USA Caroly Shumway New England Aquarium USA
Abstract:
ENVIRONMENTAL COMPLEXITY INFLUENCES VISUAL ACUITY IN CICHLID FISHES. Adam P. Dobberfuhl, Jeremy Ullman, Jessica Hunter, Elizabeth Higgins, Maggie Allen, and Caroly A. Shumway Dept. of Research, New England Aquarium, Boston, MA How do environmental forces shape neural evolution? The highly visual cichlids of the African Great Lakes, renowned for their explosive radiation, lend themselves to exploring how environmental pressures influence the evolution of brain and behavior. In this study, we behaviorally measured the visual acuity of three closely-related Tanganyikan cichlid species from the Ectodini clade differing solely in habitat preference. The fish were of the same size, lens size was the same, and social behaviors (mating and parental care) were identical. One species, Xenotilapia flavipinnis, lives in sandy, simple habits. A second species, Xenotilapia spilopterus lives in a habitat classified as intermediate, between rocky and sandy environments. A third species, Asprotilapia leptura, prefers rocky, complex habitats. We compared the visual acuity of these phylogenetically close, yet ecologically different cichlids to better understand how environment shapes brain and behavior. Visual acuity is the minimum angle formed at the eye by two objects that appear as separate (Douglas and Hawryshyn, 1990). To assess the visual acuity of these fish, the optomotor response and optokinetic responses were measured at different square-wave gratings. After a ten minute period of acclimation in the experimental tank, a fish was presented with a rotating drum of a given square-wave grating (black and white), rotated around a round experimental tank at 4Hz. If a fish could detect the separate black and white lines, it would interpret the drum as moving, responding with either an optomotor (swimming with the stimulus) or optokinetic response (visual following: both pursuit and saccades); if it could not distinguish the black from the white lines, it would interpret the drum as stationary and not move. Each trial was videotaped, and each fish was examined three times for two minutes at each grating size, ranging from 10.11mm to .32 mm. A gray background was presented as a control. Using ANOVA and post-hoc tests, the significance between each species at the fifty percent response was calculated. We found a statistically significant difference between the visual abilities of the rock-dwelling A. leptura and the sand-dwelling X. flavipinnis. We also found a significant difference with respect to social behavior. X. flavipinnis provides biparental care, whereas E. melanogenys is a polygamous lek-forming breeder. The lek-forming species was found to have better spatial acuity than the monogamous species. X. spilopterus had a visual acuity in between that of the species from complex and simplistic habitats; however, the difference was not statistically significant. These results demonstrate the importance of habitat complexity on the visual capabilities of cichlids from Lake Tanganyika. Our next step is to compare the density of retinal ganglion cells to determine whether the enhanced visual processing is occurring in the retina or in the brain. Funding provided by the National Science Foundation: grant number 0218005.
*BURTON VOORHEES - Cognitive Illusions and the Evolution of Science
Author(s):
Burton Voorhees, Athabasca University, CANADA
Abstract:
If society is thought of as an organism living within an environment, then science can be taken as analogous to the cognitive structures and processes involved in obtaining objective knowledge of that environment. At the same time, science is carried out by individuals and communication between scientists occurs only between individuals. Thus determination of the conditions for obtaining objective knowledge of the world require investigation of both the conditions for unambiguous intersubjective communication, and the internal cognitive operations of individual scientists. Much research has taken place in the psychology of decisions under uncertainty, indicating that people in general do not decide on the basis of logic and probability analysis, but instead on the basis of three general heuristics called representativeness, availability, and anchoring. Each of these is vulnerable to a characteristic class of errors that have come to be called cognitive illusions. At the same time, the three decision heuristics can be seen to be the necessary conditions for any expression of experience in language. Thus the question is not one of finding better heuristics but of learning to use the existing ones without falling into their associated illusions. From this point of view, the history of science can be seen to involve the development of techniques and methods to employ the decision heuristics with greater accuracy. In particular, three crisis periods in this history can be identified, each associated with one of the three decision heuristics. In this paper we discuss the nature of the decision heuristics and the first two crises in the history of science. This provides a picture of the anatomy of a crisis that can be applied to the third crisis which is in process today.
MIHNEA MOLDOVEANU - The Economics of Cognition. I. Algorithmic Information-Theoretic Explanation of Cognitive Biases and Fallacies
Author(s):
Mihnea Moldoveanu, University of Toronto Rotman School of Management, Canada
Abstract:
This paper attempts to explain well-established, incorrigible cognitive biases and fallacies in lay reasoning as outcomes of motivated but possibly unconscious cognitive choices over a set of models. The paper begins by modeling the agent as an information processor in the classic information-theoretic of Claude Shannon, with a small but significant change: the Shannon decoder (Maximum Likelihood or Minimum Mean Squared Error) is replaced with a generalized computational device with finite or costly memory and computational resources. The problem of cognitive choice that each agent can be understood as attempting to solve when formulating a judgment about an unknown quantity is that of selecting a working model (WM) that either minimizes the use of computational resources (computational load) for convergence to a judgment subject to working memory limitations, or one that tries to minimize working memory requirements subject to hard limits on the use of computational resources. This framework allows us to study computation-bound and memory-bound cognitive choice scenarios and to adduce cognitively rationalizable explanations for well-known characteristics of human reasoning processes, such as cognitive dissonance aversion, representativeness heuristics, conjunction biases and disjunctive biases.
MIHNEA MOLDOVEANU - The Economics of Cognition II. Fundamental Cognitive Choices that Shape Adaptation to Complexity
Author(s):
Mihnea Moldoveanu, Rotman School of Management, University of Toronto, Canada
Abstract:
This paper models a particular and important aspect of a rational agent’s adaptation to complexity, defined as subjectively experienced difficulty in making predictions about an empirically well-defined but factually unknown quantity. It posits that such an agent faces at each step of the adaptation process an important choice between gathering and storing k more bits of information about the quantity in question in order to sharpen likelihood estimates regarding the value range of the unknown variable and iterating one more time on an iterative computational algorithm for calculating the value of the variable in question, which will add n more bits of information to the agent’s working memory. Optimal trade-offs are calculated as a function of the relative costs of information gathering and storage on one hand and computational work on the other hand. Examples given include the prediction of outcomes of coin tosses and roulette wheels and the calculation by various methods of transcendental numbers.
*OLGA MITINA - The use of fractal dimension calculation algorithm to determine the nature of autobiography memoirs.
Author(s):
Olga Mitina, Moscow State University, Russia
Veronica Nurkova, Moscow State University, Russia
Abstract:
In the given research we offer the technique for the calculation of the density of events which people remember in autobiographical memory. We wanted to proof non-uniformity nature of distribution memoirs in the course of time. When a person is asked to recall important events which happened during his or her life as a rule the important events (about 20) and very important, critical ones (no more than 7) are named separately. We were interested with the law of distribution of these events during life course. The hypothesis to be tested is formulated as follows: «The important events are grouped around the critical events in non-uniform way ». For testifying and specification of this hypothesis we chose a formal model, proceeding from which the important events were represented by fractal sets in one-dimensional intervals, centered around the critical events. Fractal dimensions for centered one- and two-sided neighborhood intervals of different radius were calculated. In our example in the neighborhood of each critical event the system of centered intervals which lengths correspond among themselves as 1/2:1/3:1/4 has been constructed. If the important events occur in personal life in uniform intervals, irrespective of distance to critical event the number of events that got in each of three centered intervals corresponds proportionally to the length of corresponding intervals. If we accept a hypothesis that frequency of the important events is higher near the critical events, then the ratios between the amount of events which have occurred inside each of the three time intervals should be different. They are easy for calculating. Therefore to test the hypothesis we have to test that in the sample of parameters calculated for all subjects the average differs from the value calculated for uniform distribution. On a material of reports of 40 subjects it has been statistically proved, that fractal dimension grows as approaching the center of neighborhood from the right faster then the cubic function. The results let us make a conclusion that critical events as a rule are connected in personal consciousness with directly following important events. However there is a question here for the further research: what is a real direction of this connection. Is each critical event the reason for the whole sequence of the important events, or for an explanation of the whole cascade of the important events, so that the person tries to find in his or her life some «trigger», consciously or unconsciously, in a retrospective way and then call it as the critical event.
*MICHAEL D. FISCHER - Indigenous Knowledge Systems: Emergent order and the internal regulation of shared symbolic systems
Author(s):
Michael D. Fischer, University of Kent, United Kingdom
Abstract:
One of the key issues in agent-based modelling is the relationship between active and passive processing that arises from synergies between agents. Using results from research projects I explore indigenous knowledge systems (IKS) and the relationship of IKS to agent-based models of culture more generally. IKS are typically found in the wild in the form of general knowledge and specialised knowledge. Specialised knowledge is local to specific individuals or groups of individuals, but the specification of this knowledge and its uses are part of general knowledge. Maintaining and reproducing IKS requires a high level of fidelity in both general and diversified specialised knowledge to serve as a consistent resource for an agent community to apply in diverse circumstances to unique problems. I am focusing on two issues that greatly impact the maintenance, evolution, transmission and instantiation of IKS. First, how the fidelity of IKS impacts its use; how consistent does IKS need to be, and what aspects need to be consistent. Second, what are likely ways of preserving fidelity in a distributed framework. The paper is based on current research on the structure of indigenous environmental knowledge together with research in collaboration with Dwight Read on instantiation of kinship relationships into kinship terminologies, work that that confirms Read's long standing conjecture that the outer logic of kinship terminologies is regulated by very strong internal structuration. I propose that maintenance of the internal structure of local knowledge domains is more important than maintaining the external specifics of domains of knowledge. I will present results from agent-based models that suggest internal structure maintenance is more likely to arise from inter-agent synergies than from individually encapsulated methods; that agents are most likely to succeed by identifying and adopting domain structures that arise from processes that would otherwise increase entropy in alternative domain structures.
*JENNIFER GOLBECK - Complex Systems Analysis on the Semantic Web
Author(s):
Jennifer Golbeck, University of Maryland, College Park, USA
James Hendler, University of Maryland, College Park, USA
Abstract:
The Semantic Web is envisioned as the next generation of the web. The World Wide Web, in it current form, was conceived and evolved as a mechanism for presenting information in human readers. As media and data have become more important on the web, a need has arisen for a way to encode that data so that computers can process it, understand how pieces fit together, and present it in a variety of ways. Web standard languages, such as RDF, RDFS, and OWL, allow users to create ontologies with interlinked classes and properties, to extend existing ontologies and modify them to fit the needs of a given project, and to create instances of those classes, relate them to one another. The result is a second level of web. On top of the interlinked pages of data on the hypertext web are interlinked descriptions of data and the content of those pages on the Semantic Web. This network of interconnected data is a complex system, and because the links have clear meaning – unlike links on the hypertext web – the systems can be analyzed in great depth. This paper will present background on the semantic web and illustrate how it emerges as a complex system of data. Using the Semantic Web project FOAF (Friend-of-a-friend) and an extension to represent trust and reputation ratings between individuals, we will show the results and applications of social network analysis when applied to the networks that arise on the semantic web. This paper will go on to outline several areas where traditional complex systems analysis can benefit from the Semantic Web, and how the results can be useful to understanding the relationships between data, projects, and fields of science.

Networks

*ANDRE X. C. N. VALENTE - 2-Peak and 3-Peak Optimal Complex Networks
Author(s):
Andre X. C. N. Valente, Harvard University, USA
Abhijit Sarkar, Harvard University, USA
Howard A. Stone, Harvard University
Abstract:
A central issue in complex networks is tolerance to random failures and intentional attacks. Current literature emphasizes the dichotomy between networks with a power-law node connectivity distribution, which are robust to random failures but fragile to targeted attacks, versus networks with an exponentially decaying connectivity distribution, which are less tolerant to failures but more resilient to attacks. We prove analytically that the optimal network configuration under a classic measure of robustness is altogether different from both of the above: in all cases, failure and/or attack, there are no more than three distinct node connectivities in the optimal network.
*JOAO RODRIGUES - Network dimension and the topology of life
Author(s):
Joao Rodrigues, Instituto Superior Técnico, Portugal
Abstract:
Consider a directed network model, interpreted as an ecosystems (nodes are species and links are interactions). A new metric is defined - local network dimension, LND - that decreases with the degree of specialization of a species. LND defines the "ecological niche" in a network system: When LND grows above 2, the species undergoes evolutionary branching, under 1 the species undergoes extinction. Dynamics of ecological interactions are defined by changes in size of the LND of "meta-nodes" aggregating several species and the sign of the interactions between them.
*MAZIAR NEKOVEE - Rumor-like information dissemination in complex computer networks
Author(s):
Maziar Nekovee, Complexity Research Group, BT Exact, UK
Yamir Moreno, Dept. of Theoretical Physics, Univesrity of Zaragoza, Spain
Abstract:
We investigate the dynamics of a rumor-like process for information dissemination in complex computer and communication networks. We perform large-scale Monte Carlo simulations of these process on top of a scale-free network topology, as a prototype model of networks with strongly heterogenous degree distributions, and compare the results with simulations performed for random graphs, which have a homogeneous degree distribution. Our study provides new insights on how the dissemination dynamics is affected by the complex interplay between network structure and the spreading process. Our results are relevant to other complex systems where rumor-like information dissemination takes place
*JUKKA-PEKKA ONNELA - Studies in correlation based financial networks
Author(s):
Jukka-Pekka Onnela, Helsinki University of Technology, Finland
Abstract:
STUDIES IN CORRELATION BASED FINANCIAL NETWORKS Network theory provides an approach to complex systems with many interacting units, where the details of the interactions are of lesser importance. Recently this approach has proved to be very useful in a broad field of applications ranging from the Internet to microbiology. In the financial market companies certinly interact with one another, creating an evolving complex system. Although the exact nature of these interactions is not known, they are reflected in temporal correlations based on either stock returns or, alternatively, on flow of capital, calculated as a product of price and volume. We construct correlation based financial networks for a subset of NYSE traded stocks. In the resulting graphs the nodes correspond to stocks and the edges to correlation based ultrametric distances between them. As studies with empirical data have shown, a large majority of eigenvalues for empirical correlation matrices fall within the spectrum predicted for random matrices by random matrix theory. Since these matrices are predominantly noise, a central issue is to prune these systems in such a way that the noise if filtered out but the actual information is retained. We offer two approaches to this problem. In the first approach we construct a minimum spanning tree of edges. We have demonstrated that the MST method leads to a scale-free network, where the scaling exponent is fairly stable over time, except for crash periods, which are characterized by a lower exponent [1]. During crash periods a strong reconfiguration takes place, and the tree shrinks both topologically and in terms of its overall length [2]. We have also demonstrated how the stocks of the minimum risk Markowitz portfolio lie practically at all times on the outskirts of the tree [3]. The second approach is based on agglomerative clustering, i.e. we add a variable number of edges in the graph, one edge at a time, based on their rank. This approach better captures the strong clustering present in the market and leads to a more robust structure than the MST approach [4]. We have also compared some other properties for empirical graphs against those of a completely random graph, for which results are well known. It is postulated that deviations from theoretical predictions are indicative of genuine information. At a critical threshold, the random graph undergoes a radical change in topology related to percolation transition and forms a single giant cluster, a phenomenon not observed for the empirical graph. Differences in mean clustering coefficient lead us to conclude that most information is contained roughly within just 10% of all edges [5]. References: (available at http://www.lce.hut.fi/~jonnela) [1] J.-P. Onnela, A. Chakraborti, K. Kaski, J. Kertesz, and A. Kanto: Dynamics of market correlations: Taxonomy and portfolio analysis, Physical Review E 68, 056110 (2003). [2] J.-P. Onnela, A. Chakraborti, K. Kaski, and J. Kertesz: Dynamic asset trees and Black Monday, Physica A 324/1-2, 247-252 (2003). [3] J.-P. Onnela, A. Chakraborti, K. Kaski, and J. Kertesz: Dynamic asset trees and portfolio analysis, European Physical Journal B 30, 285-288 (2002). [4] J.-P. Onnela, A. Chakraborti, K. Kaski, J. Kertesz, and A. Kanto: Asset trees and asset graphs in financial markets, Physica Scripta T106, 48-54 (2003). [5] J.-P. Onnela, K. Kaski, and J. Kertesz: Clustering and information in correlation based financial networks, European Physical Journal B, in press (2004).
*JEVIN WEST - Comparing the dynamics of stomatal networks to the problem-solving dynamics of cellular computers
Author(s):
Jevin West, Utah State University, USA
Susanna Messinger, Utah State University, USA
David Peak, Utah State University, USA
Keith Mott, Utah State University, USA
Abstract:
Is the adaptive response to environmental stimuli of a biological system lacking a central nervous system a result of a formal computation? If so, these biological systems must conform to a different set of computational rules than those associated with central processing. To explore this idea, we examined the dynamics of stomatal patchiness in leaves. Stomata—tiny pores on the surface of a leaf—are biological processing units that a plant uses to solve an optimization problem—maximize CO2 assimilation and minimize H2O loss. Under some conditions, groups of stomata coordinate in both space and time producing motile patches that can be visualized with chlorophyll fluorescence. These patches suggest that stomata are nonautonomous and that they form a network presumably engaged in the optimization task. In this study, we show that stomatal dynamics are statistically and qualitatively comparable to the emergent, collective, problem-solving dynamics of cellular computing systems.
*NATHAN EAGLE - Genetically Modified Network Topologies
Author(s):
Nathan Eagle, MIT Media Lab, USA
Leon Danon, Dept de Fısica Fonamental, Universitat de Barcelona, Spain
Derek Cummings, Johns Hopkins University, USA
Abstract:
We present a mechanism for constructing networks with a given set of parameters using genetic algorithms. The tunable parameters include number of nodes, number of links, clustering coefficient, entropy and average distance. It is shown that the effects of maximizing entropy while constraining the number of links reproduces an exponential degree distribution, as can be seen in many real networks. We also introduce the concept of the Optimal Network Manifold, a boundary in parameter space that constrains a network’s potential characteristics.
*MARKUS BREDE - Interaction networks of agents that exploit resources
Author(s):
Markus Brede, CSIRO, Australia
Rich Little, CSIRO, Australia
Abstract:
We present a simple model for the evolution of knowledge networks between agents that exploit resources. In this context agents may, e.g., represent fishermen, people influenced by fashion, or companies; resources can be thought of as regions of the sea, fashions, the demand for products, or more generally a set of alternative strategies with different pay-offs. Generally, an individual agent's decision is not guided by a total knowledge of its surrounding world. Incorrect information and uncertainties about the other agents' behaviours complicate an optimal decision making. In our model, a resource is defined by its size and growth rate. Every timestep an agent can exploit exactly one resource. The agent's gain from a resource is a function of the resource size and decreases with the number of other agents exploiting the same resource. The agents' knowledge about their environment is represented by a directed graph with continuous strength links, given by its adjacency matrix with elements $w_{i j}\in [0,1]$. The strength of a connection between two agents $i$ and $j$ gives the probability that agent $i$ will `watch' agent $j$. Every agent will have at least a certain minimum knowledge $w_\text{min}$ of the rest of the world. The system's evolution is governed by the iteration of the following update steps: (i) agents $\{i\}$ exploit resources gaining a catch $\{ c_i\}$ (ii) all pairs of agents $i$ and $j$ compare their respective catches with probabilities given by the adjacency matrix $W$. If agent $i$ finds another agent $j$ with higher catch, it assumes $j$'s strategy, i.e. exploits the same resource as $j$ in the next timestep. Strategy adoption leads to a tightening of the connection between $i$ and $j$, i.e. $w_{i j}(t+1)= \min (1,w_{i j}(t)+q)$. (iii) loss of information with time $w_{i j}(t+1)=a w_{i j}(t)$. Depending on the parameters $w_\text{min}$ (minimum knowledge), $q$ (link strengthening after strategy adoption), $a$ (aging) and given resource growth rates we find a transition between a regime where networks are formed and a regime with close to zero connectivity. We investigate typical network structures in the first regime. From a classification of the lifetimes of network structures we discriminate between unstable and stable networks. We attempt a classification of the latter. Introducing measures of link stability we distinguish between `essential' and `non-essential' links. It turns out that stable network contain at least one of very few `core subgraphs'. Based on the latter notions of `stable networks' and `core subgraphs' we give an explanation of the dynamics, finding scale-free transition networks. The model shows how and when interaction networks emerge from strategy adaptions of agents that exploit resources and gives insight into mechanisms by which transitions from one metastable network to another one occurs. We interpret `core-subgraphs' as different modes of behaviour and elucidate the role which individual agents play in each distinct mode.
*BYUNGNAM KAHNG - Avalanche dynamics on complex networks
Author(s):
Byungnam Kahng, Seoul National University, Korea
Deok-Sun Lee, Seoul National University, Korea
K.-I. Goh, Seoul National University, Korea
Doochul Kim, Seoul National University, Korea
Abstract:
Recently the emergence of a power-law degree distribution in complex networks have attracted considerable attentions. Such scale-free (SF) networks are ubiquitous in nature. Due to the heterogeneity in degree, SF networks are vulnerable to attack on a few nodes with large degree. However, more severe catastrophe can occur, triggered by a small fraction of nodes but causing a cascade of failures of other nodes. The recent blackout of power transportation in the northeastern US and Italy is a typical example of such a cascading failure. Here, to study the avalanche dynamics, we investigate the Bak-Tang-Wiesenfeld sandpile model on SF networks, a prototypical model exhibiting the avalanch dynamics. We obtain the avalanche size distribution analytically by using the branching process approach. Finally, related problems in various systems such as the metabolic networks and the Internet will be discussed.
*WILLIAM SULIS - Phase Transitions if Random Graphical Dynamical Systems
Author(s):
Irina Trofiomova, McMaster University, Canada
William Sulis
Abstract:
Random graphical dynamical systems are systems which evolve in time to generate large scale graphical networks. Previous work has demonstrated the existence of stochastic phase transitions which possess critical parameters distinct from those that appear in standard random graphs. This is a reflection of the dynamical nature of the graph in an RGDS as opposed to the fixed nature of a random graph. A critical parameter has been shown to be the sociability, defined as the maximal number of connections which can form at a given vertex. A new RGDS model is presented in which the only tunable parameter is the sociability. A double stochastic pahse transition is illustrated, in which the probablity distribution of conencted cluster sizes first shifts from unimodal to bimodal biased towards mow clusters, and then shifts again towards bimodal baised towards large clusters.
*YONG-YEOL AHN - Extremely clustered network
Author(s):
Yong-Yeol Ahn, KAIST(Korea Advanced Institute of Science and Technology), South Korea
Hawoong Jeong, KAIST, South Korea
Abstract:
It is now well-known that most real world networks are clustered, i.e. having relatively large clustering coefficient. Motivated by this fact, we optimized a network to have very large clustering coefficient with evolutionary algorithm. Interestingly, we found that optimized network shows scale-free behavior in its degree distribution, another important characteristics of real world network, as well as high clustering coefficient. We show that our model is equivalent to the social network evolution model. As an implementation of this result, we categorize complex networks into two groups: functional and non-functional networks based on their design principles
*M.V. SIMKIN - Theory of Aces: Fame by chance or merit?
Author(s):
M.V. Simkin, UCLA, USA
V.P. Roychowdhury, UCLA, USA
Abstract:
We study empirically how fame of WWI fighter-pilot aces, measured in numbers of web pages mentioning them, is related to their merit or achievement, measured in numbers of opponent aircraft destroyed. We find that on the average fame grows exponentially with achievement; to be precise, there is a strong correlation (~0.7) between achievement and the logarithm of fame. At the same time, the number of individuals achieving a particular level of merit decreases exponentially with the magnitude of the level, leading to a power-law distribution of fame. A stochastic model that can explain the exponential growth of fame with merit is proposed. The model also provides likelihood of deviations from expected fame; it predicts, that the odds to be ten times more famous than expected from one's merit are ten in a million, while the odds to be ten times less famous are as high as one in ten.

Physical Systems

*THOMAS PORTEGYS - A Robust Game of Life
Author(s):
Thomas Portegys, Illinois State University, USA
Janet Wiles, The University of Queensland, Australia
Abstract:
We propose a Game of Life enhancement in which disrupting noise in the form of random state changes is corrected. This enhancement does not change the original rules: a noiseless enhanced run is identical to an unenhanced run. To an extent the rules of Life provide for a certain level of self-correction, for example, a cell becoming alive due to noise in a neighborhood of dead cells will immediately die. However, in general the game is quite fragile. The error-correction scheme is based on a plausible mechanism for a networked node configuration involving propagating state information. Cells in the grid are visible to each other at the "speed of light" (one cell per step), limited by a maximum range. A cell uses this "light cone" information to construct a set of state histories for its locale. The most recent history, that of the previous step, is the conventional 3x3 Life neighborhood. The histories can then be used for error-correction. A neighbor's state is compared against its neighbors' states during the previous step. If they agree according to Life rules, then the neighbor is considered to be valid. If they disagree, then an error must have occurred either in the neighbor or its neighbors. The checking then continues back in time. Once a cell's neighborhood is verified, limited by the number of available histories, its value is corrected. The noise is a probabilistic tendency to toggle the state of a cell at each step, which not only affects current states but past ones as well. We believe this scheme could be applicable to other networked state-based computing systems.
*NATALIA A. BRYKSINA - FRACTAL STATISTICS OF OSCILLATORY ZONING PATTERNS IN CALCITE: A QUALITATIVE AND QUANTITATIVE COMPARISON OF MODELED ZONING PATTERNS
Author(s):
Natalia A. Bryksina, University of Manitoba, Canada
Norman M. Halden, University of Manitoba, Canada
Sergio Mejia, University of Manitoba, Canada
Abstract:
Four main types of oscillatory zoning patterns (OZP) produced by Wang-Merino dynamic model are described quantitatively and qualitatively and displayed as simulated cathodoluminescence images. The behavior of the dynamic model was investigated in terms of the parameter θ, which is the ratio of diffusivities of Ca2+ and H2CO3 in aqueous solution. Qualitatively, the dynamics of the model may be characterized by a stable focus, an unstable focus, a stable node and a limit cycle. Quantitative characteristics, including amplitude of oscillations and duration of oscillations, change between the patterns. Hurst exponents calculated for each type of OZP are in the range of 0.8-0.96 indicating the patterns are persistent.
*MICHAEL BUKATIN - "Russian Troika" as the New Spatio-Temporal Paradigm
Author(s):
Alexander Levichev, Sobolev Institute of Mathematics, Russia
Abstract:
It is proved that the (local) causal structure of the (flat) Minkowski space-time M can be (metrically) defined by each of the three (curved) worlds D, F, L (and there are no other options to represent M in such a way). D, F, L are (the most symmetric) general relativistic space-times. They are supposed to substitute M (like a hundred years ago the Newtonian world had to give up its leading role when M emerged). It is argued that a description of a physical system is inevitably incomplete without taking the new F-, and L-energies into consideration (whereas the D-energy is the standard one).
*EUGENIO DEGROOTE - Fire Safety on Flame Spreading Over Liquid Fuels
Author(s):
Eugenio Degroote, Universidad Politecnica de Madrid, Spain
Pedro Luis García Ybarra, CIEMAT, Spain
Abstract:
Flame Spreading Over Liquid Fuels presents a complex behavior, depending on the initial conditions of the fuel surface temperature. It has been proved that, depending on the the initial surface fuel temperature, flame spreading is generally uniform but, for some range of intermediate temperatures, a pulsating behaviors appear. It has been observed that everything occurs in the vicinity of the fuel surface. The basic mechanisms that control flame spreading have been proposed, that correlate very well with our experimental data. All the results can be applied to the improve fire safety conditions in fuels containers.
*MIKA LATVA-KOKKO - Capilllary rise and fluid-solid contact angle in Lattice Boltzmann simulations
Author(s):
Mika Latva-Kokko, MIT, USA
Dan Rothman, MIT, USA
Abstract:
We have studied numerically the capillary rise in simple (circular and rectangular capillary tubes) and more complicated (fracture) geometries. We use the Lattice Boltzman method. In the simplest version of the algorithm the contact angle that the wetting fluid has with the solid surface is highly dependent on the choice of fluid that initially coats the surface. This is a result of lattice pinning and can be fixed with a slight change to the method. We show that our method also provides the correct interfacial pressure drop in the case where the surface has two independent finite radii of curvature.
S. POPESCU - Physical basis of the self-organization at critically concept
Author(s):
S. Popescu, Complex System Laboratory, “Al. I. Cuza” University, Romania
E. Lozneanu, Complex System Laboratory, “Al. I. Cuza” University, Romania
M. Sanduloviciu, Complex System Laboratory, “Al. I. Cuza” University, Romania
Abstract:
The theory of the self-organized at critically concept suggests a general interpretation of signals of all sizes and durations produced when a dynamic system, driven in a critical state, produces chain reactions. Usually the self-organized at criticality concept is related to the ubiquity of flicker noise experimentally emphasized by the presence of a 1/f power spectra. Because systems revealing flicker noise contain many components and are governed by different kinds of interactions it was not possible hitherto to construct a mathematical model to be both totally realistic and theoretically manageable. In this paper we will show that chain reactions in plasma conductors are related both to the instability whose result is a structure emerged after self-organization as well as to its spontaneously de-aggregation (catastrophic disruption). For proving this we appeal to a new phenomenological model of self-organization suggested by plasma experiments [1]. Its consideration offers, in our opinion, the physical basis for the missed realistic theoretical model of flicker noise and implicitly to the concept of self-organization at criticality. As known, flicker noise appears in plasma devices in connection with the emergence and de-aggregation of complex space charge configurations (CSCC) [1]. Similarly with the “construction” of the sand-pile, the self-assembling of the CSCC can also be controlled by the experimentalist. This is not the case when the CSCC reaches the critical state for which it de-aggregates. Since during the breakdown (collapse) of the framework of the CSCC chain processes of different sizes and durations are produced the system emits electrical signals whose frequency distribution reveals the presence of flicker noise. This distribution is related to the energies stored in the electric fields of the different patterns of the CSCC framework, which are released during its de-aggregation. Thus the double layer (DL) as a whole disrupts because of the presence of stochastic phenomena in time intervals in the range of 10-2 s. Between every disruption the DL performs a proper dynamics with frequencies of around tens of kHz. Signals of hundred of MHz are emitted during every disruption of the DL since this involves the release of the electric fields energy stored in the structure of “mini-double layers” located at its negative side. The superposition of these durations explains the appearance of the 1/f spectra in the current transported by the plasma conductor. Related to the flicker noise is the anomalous transport of matter and energy whose intrinsic mechanism is another challenging problem with interest for all branches of science. Since matter and energy are stored in every DL during its self-assembling phase, it is evident that its detachment from the CSCC and its propagation through the conductor, followed by its de-aggregation, involve a transport that can not be described by the classic transport theories. References: M. Sanduloviciu et al., Chaos, Solitons & Fractals 17 (2003) 183 and 203, and the references therein.
HAI LIN - 1/f^a Random Fields, Scaling Properties and Local Averages
Author(s):
Hai Lin, Department of Physics, Princeton University, USA
Abstract:
I mainly use the techniques developed in the book, Random Fields: Analysis and Synthesis, by Prof. E. H. VanMarcke to study some properties of the random fields, especially those with 1/f^a spectral densities. I analyzed the scaling properties and fractal structures of the random fields with spectral density 1/f^a and their local averages. I explored the properties of the derivatives of the local-averaged random fields and their relations with the un-averaged ones. I also put forward a generalization of local averages in terms of general response functions.
*KONSTANTIN L KOUPTSOV - Short Time Quantum Revivals in Chaotic Quantum Systems.
Author(s):
Konstantin L Kouptsov, Washington State University, USA
Abstract:
Classical dynamical systems are subject to the famous Poincare recurrence theorem: if the phase space available to the system is finite, the system will eventually return arbitrarily close to its initial state. In quantum systems having classical analogues, the initially localized wave packets will spread and disperse as they propagate, but will reconstruct after a relatively short time. The perfect and fractional revivals in such systems are considered a consequence of a special (e.g. quadratic) distribution of energy levels. In quantum systems with chaotic classical dynamics this reason for early revivals is no longer valid. For extended wave packets, however, there is an interplay of several other factors, such as selection of the energy levels, high dimensionality, and spatial overlap of the initial and final states, that allow early revivals to occur. Since Poincare recurrences are quite common for a variety of dynamical systems, both classical and quantum, I am going to discuss these factors in the context of a classical and a quantum systems.
PRAGYA SHUKLA - Level Statistics of Complex Systems
Author(s):
PRAGYA SHUKLA, INDIAN INSTITUTE OF TECHNOLOGY, KHARAGPUR-721302, WEST BENGAL, INDIA
Abstract:
The dynamical studies of various complex systems require a statistical information about eigenvalues and eigenfunctions of the generators of the dynamics. It is very useful, if possible, to identify a common mathematical structure among them and analyze it to gain information. Our successful search in this direction leads to Calogero-Sutherland Hamiltonian, a one-dimensional quantum Hamiltonian with inverse-square interaction, as the common base. This is because both, the eigenvalues of complex generators, and, a general state of Calogero Hamiltonian, evolve in an analogous way for arbitrary initial conditions. The varying nature of the complexity is reflected in different form of the evolution parameter in each case. A complete investigation of Calogero Hamiltonian can then help us in the spectral analysis of complex systems.
*YONGTAO GUAN - Spatial self-organization in interacting particle systems with cyclic local dynamics
Author(s):
Yongtao Guan, University of Idaho, USA
Steve Krone, University of Idaho, USA
Abstract:
We consider a class of stochastic spatial models characterized by local dynamics that have individual sites changing state in a strict cyclic progression. These include generalizations of epidemic models, rock-scissors-paper type competition models, host-parasitoid, etc. In some of these models, spatio-temporal self-organization is required for persistence of the system. We compare the behavior of the PDE's and Interacting Particle Systems. Throughout, we emphasize issues of persistence and pattern formation. Our individual-based model is both stochastic and spatially explicit.

9:00PM-10:00PM INITIAL POSTER SESSION

Systems Biology

DOLOMATOV M. YU. - FEATURES OF EQUILIBRIUM THERMODYNAMICS COMPLEX SYSTEMS WITH CHAOS OF CHEMICAL CONSTITUTIONS AND ALLOCATION OF ORGANIC MATTER IN THE UNIVERSE
Author(s):
Dolomatov M. Yu., The Technological Institute of Service, Russia
Abstract:
FEATURES OF EQUILIBRIUM THERMODYNAMICS COMPLEX SYSTEMS WITH CHAOS OF CHEMICAL CONSTITUTIONS AND ALLOCATION OF ORGANIC MATTER IN THE UNIVERSE M. Yu. Dolomatov The Technological Institute of Service 145, Chernyshevsky Str, Ufa, 450014, Russia ; e-mail dolomatov@ufacom.ru In the nature and the technologies exist systems of matters with chaos of Chemical compound or, stochastic, multicomponent systems (MSS). Such systems create components of interstellar molecular clouds, matters of biogeochemical systems, for example, humic components of soils, natural hydrocarbon systems (oil, gas condensates, solid fuel), products of their processing, natural and artificial resins, polymer blend etc. Feature MSS is the capability of existence in an elementary volume of matter of the greater number of components of the different nature from simple moleculas up to of high molecular weight substans. I select in insulated MSS a statistical ensemble from infinite number of components - N, each component is characterized by a defined value of a thermodynamic potential or property. Is identifiable probability of existence in such system of group from M of components with a definite thermodynamic potential or property distinguished from mean property of a system. Such probability (W) is determined by binomial distribution: (1) At p ≈ 1-1/z - probabilities of chemical difference z of microcondition of components in an insulated system; At z = ∞ systems is constructed from completely miscellaneous components p=1). At z = 1 p = 0 systems consist of one component, but such situation is impossible, as, аccording to the second law of a thermodynamics variety of condition. At p → 0 we have a system with Poisson character of distribution of the thermodynamic characteristics (pure matters). In representative cases MSS the probabilities, 0 < p < 1 (hydrocarbon, biogeochemical systems) will be realized. Then from (1) the Gaussian distribution of an chemical constitutions on thermodynamic potentials and properties of components follows. A corollary of gaussian distribution of an chemical constitutions on free energies of derivation is the similar distribution on standard boiling temperatures, heats of phase changes (PC), molecular weights, relaxation times and etc . It is distributions means, self-reproduction, metastability and contingency of components MSS in a unified statistical power system. For MSS are characteristic a blurring both spatial and temporary interception PC, when one of transitions was not finished, and other already of beginnings, and the distribution of correlation radiuses (R) and order parameters (η) PC follows the laws exp (-R -6) and exp (-η -6) accordingly. If each component MSS with a defined value of the thermodynamic characteristics to defines an own eigen states insulated systems ,then under the second law of a thermodynamics the number of such states is increased. Means in the nature there is a special macroscopic entropy, which one plays a role during formation of chemical variety and development of matter. I had been investigated resources of organic substance in seen area of the universe. It is established, that the probability of occurrence of complex substances such as DNA in a seen part of the universe is close to 0. But stocks of amino acids and the nucleinic bases are very great and make 1010 -1015 weights of the sun. The literature: 1.Dolomatov M.Ju Chemical physics of multicomponent organic systems. Ufa, , 2000.
*MITRA SHOJANIA - The effect of minus ends on the microtubule steady state
Author(s):
Mitra Shojania, Virginia Tech (Applied Bioscience Center), U.S.A
William Spillman, Virginia Tech (Applied Bioscience Center), U.S.A
Abstract:
Dynamic instability describes microtubule assembly in which individual microtubules exhibit alternating phases of elongation and rapid shortening. This dynamic is significantly different for the plus and minus ends of a microtubule. In this work, by considering the contribution of the plus and minus ends, we have investigated the behavior of T-tubulin concentration in the steady state and while being disturbed by the small perturbation.
*SIMONETTA FILIPPI - Complex Dynamics of the Cardiac Rhythms
Author(s):
Simonetta Filippi, University Campus Bio-Medico of Rome, Italy
Christian Cherubini, University Campus Bio-Medico of Rome, Italy
Abstract:
Many biological systems that are complex in both space and time need a new theoretical approach in the methods of nonlinear dynamics. In particular a theoretical analysis of the underlying mechanisms of heart dynamics could clarify the question about the chaotic behavior of the normal heartbeat and specially the control of the bifurcations of dynamics arising in situations of disease. The goal is to find a possible clear distinction between the normal and pathological regime. The real Ginzburg Landau equation, is used as the amplitude equation valid near a stationary bifurcation, and the Complex Ginzburg Landau equation (CGLE) is studied as a prototype for heart dynamics and the related spatiotemporal chaos. This is a very general model exhibiting spiral wave solutions. In this work we try to clarify the link between the phenomenology of the heart and the model (CGLE).
*VAL BYKOSKI - Emergence of Genome: Generalization and Use of Environmental Information by Cell
Author(s):
Val Bykoski, Virtek, Inc., USA
Abstract:
A cell dynamics is considered to be driven by environment - in geological timeframe. Accordingly, the response structures get gradually built up in cell and become its current genome. In this process, repeated environmental signals are generalized by spatial superposition and form a central core of the cell, a nucleus. Likewise, the novelty signals form the peripheral components of the cell – cytoplasm, mitochondria, etc. This is a differential encoding, specifically a space-division multiplexing. So, a cell – its structure and functions – is a mechanism to accumulate, generalize and use the environmental information to build its responses to the environment Its genome emerges as a compressed model of the long-term (geological timeframe) history of the environment. So, the genome is incrementally updated by novelty in the environment, and the cellular structures and functions also get built up incrementally rather than from scratch. From this viewpoint, a cell is a novelty encoder using the space-division multiplexing. With no novelty in the environment, there is no growth, the cell is in balance with the environment. The same model is applicable to organs, organisms, populations. The flexible controlling substrate in a cell is identified. This is a lattice of hydrogen atoms in the H-bonds of the nucleotide base pairs. In fact, the nucleotide sequence determines the positions of H atoms in the H-bonds (Watson-Crick model); however, not uniquely. The reason is that these H atoms are indeed in metastable state (double-well potential) and are able to change their position under environmental control – thus changing the complementarity relations. So metastable H atoms in the H-bonds of the base pairs are critical for immediate (adaptive) response to environment. When a cell is divided, the H-bond based adaptivity resource is roughly doubled thus increasing the adaptation options. The novelty stimulus gets integrated in a stimuli-response (SR) pair which is a structural and functional unit of responding to the environment. The response component of a SR pair contains all the protein-synthesizing (expression) circuits. In multi-cellular environments such as tissue or organ, novelty is created by the immediate as well as external environment, and so the growth is driven by this mixed novelty. In particular, in an organ, a “central core” is a response base for generalized, repeated stimuli and the peripheral (cortical) layers – for novel stimuli. A computer model based on these principles demonstrates the basic mechanisms discussed including emergence of an artificial genome under control of highly dynamic environment. The model builds up the stimulus-response patterns from different stimuli and generalizes them by spatial superimposition. With the permanent components in the stimulation stream, the genome develops a central core, whereas the variable components develop fine peripheral structures.
*JOSEPH G. HIRSCHBERG - FLUORESCENCE IMAGING FOR COMPLEX DYNAMIC INTERACTIONS OF ORGANELLES IN LIVING CELLS
Author(s):
Elli Kohen, University of Miami, USA
JOSEPH G. HIRSCHBERG, University of Miami, USA
ROGER LEBLANC, University of Miami, USA
MARCO MONTI, University of Miami, USA
Abstract:
Fluorescence imaging for the study of organelle interactions in living normal and pathological cells uses selective vital probes: dimethylaminostyrylpyridiniummethyliodine and tetramethyl- rhodaminemethylester for mitochondriaal visualization and membrane potential, pyrenebutylrhodamine for mitochondrial oxygen, NBD ceramide for the Golgi apparatus, dihexyloxacarbo- cyanine idodide for the endoplasmic reticulum, atebrine and the fluorogenic probe nonylumbelliferylglucoside for the lysosomes. Designs of Fourier interferometry based on Michelson, Sagnac interferometers or a Pentaferometer are being introduced for improved fluorecence excitation/emission spectral imaging. Combinedfluorescence/photoacoustic imaging is proposed to also account for the component of the excitation energy going into internal coversion. Biomedical applications have been initiated on a variety of cancer cells including colon and breast, with potential significance for diagnostics, prognostics and dru trials. There are also biotechnological applications on organelles of hydrogen-producing algae, e.g. Chlamydomonas.
*LEN TRONCALE - Systems Pathology as Systems Biology
Author(s):
Len Troncale, California State Polytechnic University, USA
Abstract:
As systems biology joins other natural sciences (astronomy, particle physics, earth systems sciences, combinatorial chemistry) in struggling with very large scale data sets, we may want to harvest their experiences and other sources of knowledge external to biology that have an established tradition of examining how systems are constructed and operate. Many of the disciplines in engineering (systems engineering, feedback and control systems, electrical engineering) have mined theorems on systems behavior and efficiency for decades. Already systems biology has made good use of graph theory, and the expanding interest in commonalities across networks of all kinds. Cell systems-level simulations are using concepts from circuit theory. The ICCS, at least implicitly, is dedicated to examining a wide range of complex systems, with some workers actively seeking the foundational similarities that make any system feasible. This talk will attempt to suggest and critique non-traditional sources of, and ways to present useful systems knowledge from unusual sources to systems biologists. The inherent difficulties in educating sufficient numbers of “systems” biologists using our traditionally disciplined-based educational system will be covered. Most of the talk will focus on the prospects for a new specialty named Systems Pathology as a useful future source for Systems Biology. Systems Biology would be related to Systems Pathology as cell and molecular biology are to medicine, with some unique attributes that extend beyond this simple analogy. The talk will outline the possible concepts, approaches, and methods of Systems Pathology and what both fields might learn from each other. Initially, Systems Pathology consists of a conscious application of the successful historical traditions and methods of medicine to the general systems sciences. Systems Pathology would extend biomedical techniques to system’s disease recognition & naming through identification of repeating symptoms of systems malfunction regardless of system scale or domain. It would continue with system’s disease classification, search for system’s diagnostic tests, possible system’s level treatments based on the type of general malfunction, reliable prognosis for classes of system’s disease, and, most important, strategies for system’s disease prevention. The latter might be especially useful for those engaged in legislation or socio-economic systems design. For example, Systems Pathology emphasizes the need for a system’s level Hippocratic Oath. The leap from the particular complex system of the human body to complex systems in general is admittedly great, but it will not happen unless the traditions that were selected over time for medicine (evolved) are consciously adopted by sufficient numbers of systems workers, perhaps through educational systems. Case studies in Systems Pathology from the work of Miller (Living Systems Theory) and the Linkage Proposition System of Systems Mechanisms will be surveyed. One of the oldest and traditional systems professional societies (the ISSS) is initiating a SIG (Special Integration Group) on Systems Pathology with its first meeting at Asilomar this summer. It is the purpose of SIGs to organize and ensure work on their topic between meetings. As such the ISSS Systems Pathology SIG would become a working consortium for further development of this approach to complex systems in general. Systems biologists working at the level of interpretation of system’s regularities and irregularities in vast data sets would be especially welcome in this consortium.
*RENHUA LI - Modeling pleiotropic genetic systems in mice
Author(s):
Renhua Li, The Jackson Laboratory, USA
Shirng-WernTsaih, The Jackson Laboratory, USA
Cheryl Ackert-Bicknell, The Jackson Laboratory, USA
Ron Korstanje, The Jackson Laboratory, USA
Michal Mrug, Division Nephrology, university of Alabama at Birmingham, USA
Wes Beamer, The Jackson Laboratory, USA, Jon Wergedal, Musculoskeletal Disease Center, J.L. Pettis memoral VA Med Ctr, Loma Linda, CA 92357 Gary A Churchill, The Jackson Laboratory, USA,
Abstract:
Variation of complex traits in a genetically randomized population of laboratory mice (GPRLM) can provide observations that shed light on the system’s structure. In biological systems, experimental crosses provide unique features that admit causal inference. Naturally occurring genetic variation can be distributed in a combinatorial fashion among a set of cross progeny. The transmission of genetic variants through the process of meiosis serves as a natural randomization mechanism. Thus genetic crosses share properties of statistical designed experiments that allow us to infer causation. This is consistent with the intuitive notion that causation flows from genes to phenotypes. We have applied methods of network inference to GRPLM to investigate a variety of phenotypes. A typical study involves the measurement of a number of correlated phenotypes and genotyping on the same set of mice. The problems to be addressed are three. First, how to identify the genetic loci involved in the system? Second, how to infer the network of causal relationships among genes and phenotypes? Third, what can we learn from the network? We illustrate this concept with an application to analyses of three complex trait systems. We are able to identify genetic loci that are causal for variation in multiple phenotypes. Moreover we are able to infer causal networks that provide insight into the pleiotropic nature of the genetic effects.
*JIYONG PARK - Computational study on mechanical properties of self-assembled peptide filaments
Author(s):
Jiyong Park, MIT and Seoul National University, USA, KOREA
Wonmuk Hwang, MIT, USA
Shuguang Zhang, MIT
Roger D. Kamm, MIT
Byungnam Kahng, Seoul National University
Abstract:
We investigate the supramolecular structure and mechanical properties of a $\beta$-sheet nanofiber made from the self-assembling peptide RAD16II. The candidate molecular structures are constructed based on the available experimental data, and are confirmed by comparing their energy profiles and stability. To quantify the elastic properties of the identified filament structures, we perform two types of molecular dynamics simulations, thermal motion of the filament and forced bending simulation, as well as normal mode analysis. These approaches consistently give the Young's modulus of the filament in the range of 1~3GPa, comparable to other biofilaments such as microtubule and the F-actin. In addition, we investigate the rupture tension of the filament. Such quantitative information obtained {\em in silico} on the basic structural and mechanical properties of individual fibers, will help in the fabrication of the peptide-based biomaterial for specific tissue engineering applications.
*MICHAEL L. BLINOV - Modeling combinatorial complexity of signal transduction systems
Author(s):
Michael L. Blinov, Los Alamos National Laboratory, USA
James R. Faeder, Los Alamos National Laboratory, USA
William S. Hlavacek, Los Alamos National Laboratory, USA
Abstract:
Signal transduction systems are complex for combinatorial reasons: during signaling, a protein may occupy a number of phosphoforms, and it may interact with multiple binding partners, such that proteins combine dynamically to form various heterogeneous complexes. The known interactions and activities of signaling molecules imply hundreds to thousands of possible molecular species even for cases that involve only a few proteins. We have developed an approach and software for modeling the dynamics of a signal transduction system that allows one to account comprehensively and precisely for the possible molecular species implied by specified interactions, activities, and modifications of the molecules in a system (http://cellsignaling.lanl.gov/bionetgen/). Analysis of such detailed models allows to identify the molecular species, reactions and pathways that are prevalent during signaling, and to investigate different assumed signaling mechanisms. Such models are relevant for rational drug discovery, analysis of proteomic data, and mechanistic studies of signal transduction system.

Networks and Structural Themes

*ATIN DAS - From Bibliometrics to Webometrics: A Case Study
Author(s):
Atin Das, PRA Vidyalaya, India
Gottfried Mayer-Kress, Penn State University, US
Carlos Gershenson, Universiteit Brussel, Belgium
Pritha Das, BE College (DU), India
Mason A. Porter, Georgia Institute of Technology, US
Andrej Probst Comenius University, Slovakia. Matus Marko Comenius University, Slovakia.
Abstract:
The field of bibliometrics is concerned with conducting quantitative analyses of documents, traditionally printed ones such as books and journal articles. For this Impact Factor (IF) is used. On the other hand, Webometrics is the application of informetric and other quantitative techniques to the study of the Web. One of the most popular measures for this is the Google PageRank (PR). We attempt in this paper to compare these indices in complexity related research field. This has become particularly important in the recent years with the advent of many web-based publication activities where there is no scope of formal print version (and hence of IF). On the other hand, many print-only publications have set up web sites and on-line versions for readers (hence, there is PR of them). Particularly, we shall discuss the relative importance of such sites with the example of the Complexity Digest (ComDig)- a weekly publication of complexity related excerpts and links with data available since 1999. For this purpose, we collected both IF (2001) and PR of 24 journals. The objective was to study whether a journal with high (low) impact factor attract more visitors to its online version and thus gather a higher (lower) PR. We plotted the (IF/PR) for each journal and could not find any visible relation between IF and PR- although expected. Apart from other factors for these, the very way PR is calculated casts some doubt. We then attempt to compare the PR of some of the most widely known complexity related web sites. We also analyzed the pattern of the visits to the ComDig site both manually and algorithmically. We thoroughly investigated the incoming links to the ComDig site, keeping in mind the fact that the incoming links (‘votes’) increase the PR as explained by Google. We also describe outward links, that also count as important factor in PR calculation. Finally, we discuss more content based activities that are not directed to a higher PR only and which include of incoming and outgoing links. These may lead to new evaluation system of web citation metric -different from PR.
*MANUEL MIDDENDORF - Classification of Biological Networks Via Walks and Words
Author(s):
Manuel Middendorf, Columbia University, USA
Etay Ziv, Columbia University, USA
Chris Wiggins, Columbia University, USA
Abstract:
Over the last half-decade, researchers in the network community have proposed a diversity of models aiming to describe naturally occurring networks based on fundamentally different mechanisms (e.g., preferential attachment, fitness-selection strategies, duplication with mutation). Generically, these models are validated by measuring a few basic graph properties (e.g., clustering coefficient, mean geodesic length or degree distribution). Since different models can often be tuned to yield exactly the same values for such properties, these features are unfortunately non-discriminative. To obviate this ambiguity, we develop a high-dimensional feature space for graphs, and exploit classification techniques standard in the machine learning community. We then classify biological data sets such as protein-protein interaction networks, neuronal networks, and genetic regulatory networks. The resulting classifications deepen our understanding of the design and evolution of the networks studied. Our conclusions have also guided the development of new network models. We are thus able for the first time to quantify objectively the success of a model in describing a given network without prior choice of graph properties.
*ERIK VOLZ - Random graphs with clustering and arbitrary degree distribution
Author(s):
Erik Volz, Cornell University, USA
Abstract:
Random graphs may be suitable models of real networks, such as social networks, the Internet, and epidemiological networks of susceptible and infectious individuals. Random graphs may be used as null hypotheses to test conjectures about network structure, and sometimes it is essential to use random graphs as a model of a real network when it is impossible to sample an entire network. Recent work has shown that real networks have statistical properties which differ from pure-random graphs, most prominently in their degree distribution and their clustering coefficient (a measure of triadic closure). Random graph models have been devised to mimic both specified degree distributions (Barabasi model) and clustering levels (Watts-Strogatz model), but so far no algorithm has been devised to generate random graphs with both specified degree distributions and clustering levels. We here propose such an algorithm, and use it to illustrate the vulnerability of networks to diffusion processes.

Socio-economic Systems

*DAVID SAAKIAN - Inter-hierarchic resonance in complex systems: search of anti-resonance situation.
Author(s):
David Saakian, Yerevan Physics Institute, Armenia
Abstract:
The notion of complex resonance is defined as a situation, when global parameters of the system characterizing the total state coincide for different hierarchic levels. Among those global parameters are frequencies in classical mechanics systems, temperatures (Nishimori like) in disordered systems and wave function's phase. The more involved case could be connected with the functions like replica symmetry breaking schemes. The anti-resonance is defined as a situation, when highlevel (hierarchic) parameter is chosen to suppress the motion in a maximal away. We applied the last concept to the problems of the modern history.
*VLADISLAV KOVCHEGOV - The inhomogeneous product-potential social systems
Author(s):
Vladislav Kovchegov, USA
Abstract:
This article contains theory of potential networks as very important type of locally interrupted system with relations and reactions. The potential networks were deduced from locally interrupting system by using so call "principle of maximum non-ergodicity". The system are balanced in social sense if the set of psychological reactions on the graph of relation satisfy the principle of maximum non-ergodicity and this system of reactions are product potential on the so call "two-steps" graph of relations. In real life survive only relatively stable groups and we can observe only stable groups that in social science call balanced. So reason why social system (group) is stable based on hidden potentiality of reactions: only social systems with potential system of reactions (potential fields) are stable (balanced in social sense). This conclusion is not a big surprise for natural (physical) systems. For instance the system consisting of a star and a single planet is stable because the gravitational interaction is potential (friction is absent). The potentiality of gravitational interaction means that work done along any closed path is zero. But for social science and, particularly for human groups, a similar property comes as a surprise. The main problem that was presented in this paper is problem of existence of potential fields. For solving this problem was used method of smooth fields on the solid domain and product integrals. For of smooth fields will be write the system of infinitesimal equations (system of partial differential equations) that must be hold for all potential fields and then was found solutions of infinitesimal equations. Then smooth potential field on domain will be transformed into discrete potential marking on embedded graph of relation by using product integrals. The finally will be found system differential equations that transfer any initial fields of reactions into potential.
*PIERPAOLO ANDRIANI - POWER LAW PHENOMENA IN ORGANIZATIONS:
Author(s):
Pierpaolo Andriani, Durham Business School, UK
Bill McKelvey, UCLA, USA
Abstract:
In this paper we are going to argue: 1. That power law distributions emerge in three different types of models; these models can be shown to describe respectively nodal, links and dynamic properties of networks; 2. That the three models are related and represent three complementary approaches to different network properties. The same underlying phenomenon, that is, self-organization in networks, is the cause of the emergence of a power law. 3. That a power law is inherently related to the emergence of a collective system of relationships among the agents of a system. This implies that a power law distribution can be used to distinguish systems of coevolving agents from aggregates of independent agents. The closeness to a power law is an indicator of the emergence of a system from an aggregate and can be used to distinguish, for instance, between an industrial cluster and a simple geographic concentration of firms. 4. That power law and the edge of chaos dynamic are correlated.
*VLADISLAV KOVCHEGOV - The dynamic of product potential social systems and representation theory
Author(s):
Vladislav Kovchegov, Horizon Blue Cross Blue Shield of New Jersey, USA
Abstract:
The product potential system is a locally interacting system with a potential (defined by requiring that a product-integral along any closed path on a graph equals to an identical transformation) system of psychological reactions (consisting of marks or fields on the edges of the graph of relations). A locally interacting process for the product potential system of relations can be given by an algebraic representation of an process of multiplication on the randomly chosen so call “control matrix”. We found one to one maps between thermodynamic states of system (the thermodynamic state for the system is measure) and so call “left ideals” on the semi-group of control matrices. The ideal matrices have a very important property: when an arbitrary stochastic/control matrix is multiplied from the left by an ideal matrix one obtains a left ideal matrix. So the set of left ideal matrices is the termination set for our stochastic product process (spatial Markov’s chain). It means that once the system reaches the termination set the process can never leave the termination set. Thus the left ideal matrices play a crucial role in the description of our process.
*ATIN DAS - What Does Composite Index Of NYSE Represent In The Long Run?
Author(s):
Atin Das, PRA Vidyalaya, India
Abstract:
Here we analyze the Composite Index (CI) for 35 years [From 1966 to 2000] of the New York Stock Exchange (NYSE) collected on daily-basis. The NYSE Composite Index averages the prices of all the stocks traded on the New York Stock Exchange. This index is automatically updated after each transaction and is sent electronically to the trading floor. We want to investigate what type of dynamics this data represents. There are plenty of works on the chaotic dynamics of the stock exchange data since the very beginning by Manelbort’s pioneering work investigating the price changes dynamics of an open market in 1963. While the fluctuation of individual share prices following prevailing market relations makes them unpredictable, is it true for CI also? Particular question we ask is what dynamics of the stock exchange does CI represent? We find that up to first 16 years starting from the year 1966, CI is confined to values in the range of 36 to 75 and fluctuations are very small (20 points on either side). For the next 16 years that is up to year 1997, CI rises to 600 point mark although with fluctuations. For more precise analysis using nonlinear techniques, we break up the entire dataset in two parts- each having 16 years of CI points. To investigate the nonlinearity of the data, we resorted to the technique of surrogate data analysis [Das et al., Complexity Int., 2002:9].. We have calculated the Lyapunov Exponent (LE) of both the original and surrogate of each dataset and then compared the values. For the first half of CI data, the change is only 1.3% while for the next half, the change is 8.7%. The difference between a highly chaotic data and its surrogate counterpart should be much higher, for example, above 30% in our earlier work with electroencephalogram (EEG) data collected from human brains [Das et al., Complexity, 2002:7,3]. So it can be safely said that CI is not unpredictable in longer time scales. Moreover, the second half of the data fits well with some growing function of time- although with small random fluctuation predominant particularly in the last 3 years data. This confirms the observation that CI is a fairly good indicator of general market strength – here, the US economy itself.
*HYUNG SAM PARK - Evolution of Organizational Rationality in Social Complexity: Interorganizational Networks in Environmental Biotechnology Industry
Author(s):
Hyung Sam Park, University of Pittsburgh, USA
Abstract:
This research demonstrates how organizational rationality is bounded in social complexity and how interorganizational networks interplay when organizations are dealing with resources of social issues (‘risk organizations’, below). Social complexity is measured in terms of increased normative constraints in the field of interest indicated by the increased number of organizations that support environmentalism, regulations on development and use of hazardous environmental technologies, and civil/international events on environmentalism. Four relationships are specifically examined concerning social complexity and organizational rationality. As normative constraints increase in organizational environments, it is, first, observed that structural positions of risk organizations are peripheralized in network hierarchy. The structural positions are measured in terms of centrality, core/periphery, and structural equivalence of organizations in network hierarchy. Second, it is shown that subgroup formations are more active among risk organizations than among other organizations. Subgroups such as cliques and k-plexes are detected and compared to examine whether or not there is any difference in forming coalitions between risk organizations and other organizations. Third, it is demonstrated that risk organizations in subgroups have more ingroup ties over outgroup ties, compared with other organizations. Lastly, it is revealed that peripheralized risk organizations depend on less variety of ties than other organizations do. A historical network analysis is carried out based on longitudinal collaborative ties between environmental biotechnology firms (EBFs) and other organizations in the industry of environmental biotechnology over the period since 1970. The findings contribute to locating structural positions, sub-networks, and cohesion/solidarity of organizations with resources of social issues that vary depending on social complexity – especially, the levels of normative constraints –, and predicting the flows of the hazardous resources in complex interorganizational networks.
*HIROYUKI MASUDA - Integrated Model of Emergency Evacuation of People after a Big Earthquake at the Busy Quarter near a Major Junction Station in Suburban Tokyo
Author(s):
Hiroyuki Masuda, Tokyo University of Science (TUS), Japan
Takeshi Arai, Tokyo University of Science (TUS), Japan
Kotaro Nomura, Tokyo University of Science (TUS), Japan
Takashi Hasegawa, Tokyo University of Science (TUS), Japan
Yasuhiro Tsutsui, Tokyo University of Science (TUS), Japan
Author #6; Name:Takumi Hosoi; Organization:Tokyo University of Science(TUS); Country:Japan; Email: j7403630@ed.noda.tus.ac.jp
Abstract:
In Tokyo we may have a big earthquake even today. We have national and local plans to prevent the catastrophe caused by a big earthquake in Japan. In the metropolitan regions huge numbers of people commute by train between their homes and the CBD area of the major city everyday. Accordingly, there are some busy shopping streets and big stores near to the major junction station in the suburbs. However, we have only few plans to evacuate a number of people who are unfamiliar with the places on the way between their homes and places of work. In this study, we present the framework of an integrated model of emergency evacuation of people after a big earthquake at the busy quarter near a major junction station in suburban Tokyo, Japan. In addition, we explain the ideas of a macro evacuation model described by System Dynamics and some micro evacuation models using one of the multi agent based systems. By these models we can simulate evacuation process from the big store, from the train through a platform of a railway station, from an underground floor through stairs and escalators and from buildings to some safer places through the streets on which many obstacles are scattered.
*JAMES K. HAZY - SIMULATING AGENT INTELLIGENCE AS LOCAL NETWORK DYNAMICS AND EMERGENT ORGANIZATIONAL OUTCOMES
Author(s):
James K. Hazy, The George Washington University, USA
Brian F. Tivnan, The George Washington University, USA
Abstract:
We build upon our previous work to represent organizations as a network of agents, tasks, resources and knowledge (Krackhardt and Carley 1998) to explore the emergent effects of agent interactions on organizational outcomes. To do this, we define agents in the context of their position in the network, describe the agent’s symbolic representation of its position in the network, and develop a probabilistic function associated with each agent that acts locally to change the network. We conclude with a brief overview of our research in this area to date and the usefulness of this network representation.
*XIANG SAN LIANG - Evolution of Money Distribution in a Simple Economic Model
Author(s):
Xiang San Liang, Harvard University, USA
Thomas J. Carter, California State University - Stanislaus, USA
Abstract:
An analytical approach is utilized to study the money evolution in a simple agent-based economic model, where every agent randomly selects someone else and gives the target one dollar until he runs out of money. (No one is allowed to go in debt.) If originally no agent is in poverty, for most of time the economy is found to be dominated by a Gaussian money distribution, with a fixed mean and an increasing variance proportional to time. This structure begins to be drifted toward the left when the tail of the Gaussian hits the left boundary, and the drift becomes faster and faster, until a steady state is reached. The steady state generally follows the Boltzmann-Gibbs distribution, except for the points around the origin. Our result shows that, the pdf at the origin is half of that predicted by the Boltzmann solution. An implication of this is that the economic structure may be improved through manipulating market rules.
*DMITRY CHISTILIN - To the wave nature of the economy cycle
Author(s):
Dmitry Chistilin, Institute World economy and international relations, Ukraine
Abstract:
TO THE WAVE NATURE OF ECONOMIC CYCLES D.CHISTILIN Institute World economy and International Relations Ukraine Academy of Science Ukraine Current Address P.B 4721 Dnepropetrovsk 49094 Ukraine E-mail unid@a-teleport.com abstract The question of economic cycles' origin is still one of the most actual problem up till now. One of defined economic regularities and reasons initiating cyclic fluctuations in every researching sphere of functioning community is a trend of population growth, scarceness of resources for production and benefits for consumption. At the same time diffusion and integration of knowledge in different spheres and revealing of common regularities characteristic for organic and inorganic natures are taking place in the modern science. It includes discovery of dynamic chaos, principle of dissipation of open systems, etc. There is one more accomplishment, which is common for both organic and inorganic nature: formation of wave processes. In inorganic nature the wave process appears as a form of system's existence and as reaction of environment against outside disturbance. While considering conditions of origin of stationary wave in physics of non-linear processes it is obvious that wave process is a qualitative characteristic of the system itself existing in non-equilibrium state. According to the definition both characteristics -- density and viscosity -- initiate two opposite processes: twisting and overturning in case of outside disturbance availability which, in their turn, create the base for appearance of stationary wave. Analyzing different communities, social-economic systems we can observe an analogue process. Socium as an environment, a separate element of which is a human being, possesses two basic characteristics. It is different values of capability of every separate human being on one hand and organization of scarce resources for production and level of consumption of limited benefits, on the other hand. These two characteristics create two opposite trends of production and consumption in condition of growth of population and scarceness of resources during long period of time. These two mentioned trends of opposite directions initiate stationary wave process, which economics defines as economic cycle. Conclusions. Thus, the property of organic system to initiate wave processes under the influence of outside environment is a necessary condition for supporting dynamic stability of the system in the process of its development, i.e. supporting homeostasis.
*INGAR MALMGREN - The Complex Economics of Innovation, -Implications for the Shipping Industry
Author(s):
Ingar Malmgren, Chalmers University of Technology, Sweden
Abstract:
The shipping industry is rapidly turning into a knowledge based sector and is therefore becoming subject to other economic rules then traditionally such as increasing returns. This is especially noticeable for the subsystem developers due to the high share of know-how and high-tech in the products compared to the material costs. Being aware of the complexity theories and their implications can help a shipper or supplier to balance the R&D resources between sustaining and disruptive development and to manage the organization as a complex adaptive system and thereby gaining competitiveness
*MARCIA ESTEVES AGOSTINHO - The Building-Block Advantage
Author(s):
Marcia Esteves Agostinho, PUC-Rio (Pontifical Catholic University of rio de Janeiro), Brazil
Abstract:
The advances in the study of complexity and evolutionary processes bring new insights on how to describe production systems. It is argued in this paper that such new perspective provides new skills to cope with contemporary business challenges. It is known that nature operates through the recombination of independent parts – through a continuous process of rearrangement of “building blocks”. Likewise, along modern history, production systems have been benefited by this principle. The evolution from craft to industrial production shows that the focus has been changing from the individual’s talent toward the productive system’s capacity. The consequent complexification of production is a response to the selective pressures imposed by a socio-economic context that also becomes more complex. But most importantly, the greater adaptability and robustness gained by industrial productive arrangements to survive in such environment are due to successive processes of internal differentiation and aggregation – as it happens with natural complex systems. From fordist assembly line to cellular layouts, there are signs of recognition of the advantages associated with “building-blocks”. In the first, the building blocks are tasks or elementary operations that are arranged according to the processing sequence for a given product. In the last, these building blocks are arranged as a function of a group – a “family” –of products. This fact causes the emergence of aggregates – “production cells” – that are as “factories inside a factory”, producing a hierarchical organization typical of adaptive complex systems. As being more complex (in the sense of complexity theory) than assembly lines, cellular layouts become more flexible and adaptive, as competitive markets require. Escaping from the extreme of chaos represented by craft production, the classic age of industrialization took advantage of standardization (that is the identification of building blocks and multiple possibilities of recombination) and guaranteed a long period of stable success. However, some decades ago, this orderly scenario was threatened and industrial organizations were forced into the edge. Fortunately, the edge between order and chaos is a place full of possibilities. In this regard, this paper aims at pointing out how knowledge about recombination of building blocks and aggregates formation may contribute to the manufacturing strategy of organizations that are trying to develop new capacities for thriving in a complex world.
PAUL LANG - An Essay on SFEcon’s ‘Perfect Markets Model’
Author(s):
Paul Lang, SFEcon, USA
Abstract:
An Essay on SFEcon’s ‘Perfect Markets Model’ ABSTRACT: All SFEcon models of macroeconomic adjustment are essentially ‘finite state machines’ in that they are only aware of their current state, and of a set of rules for advancing the current state through one differential element of time. To such a view, the global economic order would appear to be an analog computer, simultaneously controlling all stocks S of Good J among the assets of Sector I in Economy K at time t. The task of economic science would then be to imagine how a physical state Sijkt might be continuously controlled by an abstract system of financial state variables giving expression to prices and interest rates. SFEcon’s prototypes respond to this challenge with inherently stable and goal-seeking behaviors, proceeding through quite recognizable business cycles toward a material balance in which general equilibrium prices cooperate with marginal products to produce Pareto’s optimum. (Py dY/dE = Pe at each cell in the matrix, while everything being used in current production E is exactly replaced by that production Y). SFEcon’s models of economic adjustment are structured by a three-dimensional input/output matrix comprising any number of sectors, and segmented into any number of national tables. Models operate by continuously re-evaluating and actuating rates of material flow among the economic sectors. These flows are controlled by prices, currency values, and interest rates which are themselves systematically adjusted by references to the model’s physical state, even while it advances through a succession of chaotic states toward a unique general economic optimum. Novel hyperbolic production functions have been devised to regulate the informational exchange between the models’ physical and financial states. Parameters shaping these highly non-linear boundary conditions are easily derived in mathematically closed-form from routinely published I/O data. SFEcon posts several prototypes at www.sfecon.com. The economic theory embodied by these models can be expressed in terms most compatible with academic interchange if three limits are imposed on what the prototype models portray: 1) efficient markets, whereby everything in supply at a given moment is being used; 2) modeling of an isolated economic system having no foreign trade or investment; and 3) activity in every cell of the input/output matrix describing the economic system. These simplifications allow a sufficiently compact notation to reveal solutions the familiar Vienna Problem, while demonstrating the computation of an absolute measure of value for economics that is no more variable than the metric ton, the board-foot, or the BTU.
*M.V. SIMKIN - Stochastic modeling of citation slips
Author(s):
M.V. Simkin, UCLA, USA
V.P. Roychowdhury, UCLA, USA
Abstract:
We present empirical data on misprints in citations to twelve high-profile papers. Analyzed within the framework of our stochastic model of citation process, the data indicates that about 70-90% of scientific citations are copied from the lists of references used in other papers.
*PIERPAOLO ANDRIANI - Modelling diffusion of innovation using Cellular Neural Network approach
Author(s):
Francesca Conti, DIEES - University of Catania, Italy
Pierpaolo Andriani, Durham Business School, United Kindom
Luigi Fortuna, DIEES - University of -Catania, Italy
Mattia Frasca, DIEES - University of -Catania, Italy
Giuseppina Passiante, Departiment of Innovation Engineering, Italy
Alessandro Rizzo Dipartimento di Elettrotecnica ed Elettronica Italy
Abstract:
CNNs have been traditionally used for pattern recognition. This paper shows that Cellular Neural Networks (CNNs) constitute a powerful new paradigm for modeling complex systems. We argue that diffusion of innovation can be modeled by using CNNs and that the results obtained are consistent with previous Cellular Automata-based simulations. The CNN approach can be generalized to model more complex problems.
*ALEX YAHJA - A Model of Biological Attacks on a Realistic Population
Author(s):
Kathleen M. Carley, Carnegie Mellon University, USA
Douglas Fridsma, University of Pittsburgh Medical Center, USA
Elizabeth Casman, Carnegie Mellon University, USA
Alex Yahja, Carnegie Mellon University, USA
Li-Chiou Chen, Carnegie Mellon University, USA
Boris Kaminsky, Carnegie Mellon University, USA, Neal Altman, Carnegie Mellon University, USA, Demian Nave, Pittsburgh Supercomputing Center, USA,
Abstract:
Assessing the impacts of biological attacks on a heterogeneous city population with diverse demographics in enough detail and fidelity to enable effective response is important from intelligence and planning perspectives. The complexity of the spread and impacts of weaponized disease outbreaks -- particularly contagious ones -- is compounded by the existence of naturally-occuring diseases which may have similar symptoms to those of weaponized diseases. Moreover, the outbreaks are modulated by physical (e.g., road networks and urban geography), social, health, communication, economical, institutional and govermental infrastructures. These infrastructures often are dynamic networks in form. These networks -- particularly social and health ones -- need to be considered in order to systematically reason about the nature and impacts of outbreaks, the potential of media, prophylaxis and vaccination campaigns, and the relative value of various early warning devices. Conventional SIR models in epidemiology do not address this and only operate on the homogeneous sample population level. Multi-agent models provide an effective and ethical system for reasoning about biological attacks on a city. Our model, BioWar, combines state-of-the-art computational models of social networks, communication media, and disease transmission with demographically and spatially resolved agent models, urban spatial models, weather models, and a diagnostic error model to produce a single integrated model of the impact of a biological attack against the background of naturally-occuring diseases on a city. Unlike traditional models that look at hypothetical cities, BioWar is configured to represent real cities by loading census demographics data, social network data, school district boundaries, business location and type data, healthcare infrastructure data, and other publicly available information. Moreover, rather than just providing information on the number of infections, BioWar models the agents as they go about their lives – both the healthy and the infected (each agent has its own spatial -- longitude and latitude -- coordinates). This enables the analyst to observe the repercussions of various attacks and containment policies on factors such as absenteeism, medical web hits, medical phone calls, insurance claims, over-the-counter pharmacy purchases, and hospital visit rates, among others, in addition to epidemiological factors such as infection rate, prevalence, incidence, and death rate. The values of these factors are used in syndromic and behavioral surveillance to evaluate early detection algorithms. BioWar was implemented in multithreaded C++ and achieved fast runtime both on a personal computer and on supercomputer nodes. We will present the validation results of BioWar for anthrax and smallpox outbreaks based on empirical data and comparison against population-based epidemiological models. The results of smallpox and anthrax attack simulations using BioWar on select US cities will be described and analyzed. The results show that BioWar produces higher fidelity and high granularity predictions than what conventional SIR models can attain and produces nonlinear dynamic emergent behaviors and patterns similar to what happen in the real world. Comparisons with other computational models will also be presented. BioWar is thus useful for preparedness training, intelligence planning, response analysis, detection algorithms evaluation, stakeholder communication, and public policy analysis.
*CARLOS PARRA - Evolutionary Dynamics of Knowledge
Author(s):
Carlos Parra, Tokyo Insitute of Technology, Japan
Masakazu Yano, Tokyo University, Japan
Abstract:
This study discusses the human version of an artificial agent’s interpretative devices (Arthur, B. 1997) by presenting a definition of interpretants, which follows Varela’s (1999) neurophenomenological perspective coupled with a cybernetic understanding of Piercian semiotics, namely: experiences, made of alternate bundles of embodied experiences (distinctions.) This definition of interpretants is not only useful from a human development perspective when capabilities are comprised of alternate bundles of choices or functionings (an individual’s beings and doings, Sen, 1993) and these choices then, standing for embodied distinctions (Parra and Yano, 2002), stem from distinctions that in turn stand for embodied experiences (Parra and Yano, 2004); but more so because this approach uses liberty instead of utility for economic decision-making, replacing widely used traditional assumptions (i.e. individual rationality) and thereby adopting recent behavioral and experimental discoveries. In particular, this paper proposes a learning model (or inner-world reconstructing model) that can overcome neo-classic obstacles, and increase the predictive power of computational economics, by letting agents’ knowledge evolve by itself, irrespective of globally specified goals and even individual motives of behavior; using simultaneous (or parallel) Genetic Algorithms (GA) to evolve a single agent’s learning strategy, each GA with different general specifications, in a multi-agent setting. In order to implement our definition of interpretants computationally, artificial agents would need to be designed so as to: experience something; distinguish the source of this experience; also ground what they are experiencing; such that when this new experience is eventually employed, be able to embodied it as a distinction; and also be able to self-provoke random interpretations accounting for the effects of chance, leading to “misinterpretations” that could end up having positive effects on the performance of an agent, or the system as a whole. Moreover, this single agent inner world reconstruction model, when used in a multi-agent scenario could help scrutinize the transition from freewill-guided agents to rule-based interactions (i.e. cooperation and/or self-organization). Eventhough we do not provide detail specifications about how to implement the learning model, or put it into practice, we do give real-life perspective on what the outcomes of such an exercise could be in institutional terms (North, 1990) pointing to the evolutionary dynamics of experiences, distinctions and choices. This is done so as to contribute to the cognitive debate around agent-based learning models, which in our perspective should be about the methods for handling variation (inside learning algorithms, between algorithms, among agents, and for systems in general.)

Engineering Systems

*ALEX J. RYAN - Hybrid Complex Adaptive Engineered Systems: A Case Study in Defence
Author(s):
Alex J. Ryan, Defence Science and Technology Organisation (DSTO), Australia
Anne-Marie Grisogono, Defence Science and Technology Organisation (DSTO), Australia
Abstract:
Naturally arising Complex Adaptive Systems (CAS) display a common ability to adapt and thrive in the face of pressure, change and competition, although they can also fail, sometimes catastrophically. Previous work (Grisogono & Ryan, 2003) has demonstrated that a defence force exhibits the basic properties of a CAS. This work is extended to investigate adaptive mechanisms operating within a defence force at three scales analogous to naturally arising adaptation at the levels of organism, species and society. In complex sociotechnical systems such as a defence force, mechanisms and processes designed and engineered by humans coexist with, and interact with the naturally arising adaptive mechanisms to produce a hybrid complex adaptive engineered system. Exploring the implications of conscious human design decisions influencing the emergent functionality of a CAS, we illustrate firstly how spontaneously arising informal adaptive feedback can often undermine the intentional, formal adaptive processes in such systems. This occurs when a centralised, mechanistic and linear command and control model produces unintended consequences due to the unpredictable nature of the underlying CAS. Secondly we demonstrate the potential for hybrid systems to harness the benefits of the underlying adaptive systems while utilising CAS-informed design effort to shape the emergent system function effect. The results provide a new understanding for effective command and control of hybrid complex adaptive engineered systems and have implications for operations, systems design, capability development and force transformation within defence. Reference: Grisogono, A. & A. J. Ryan, Designing Complex Adaptive Systems for Defence, SETE 2003 Conference: Practical Approaches for Complex Systems, Canberra, 2003.
*MARK S. VOSS - Cellular Automata + GMDH = Emergent Programming: A New Methodology for Hardware Intelligence
Author(s):
Mark S. Voss, Montana State University - Northern, USA
Abstract:
The application of Cellular Automata with respect to machine learning algorithms is an area of active research [1] [2] [7]. This paper combines the Group Method of Data Handling [6] with Continuous Cellular Automata [9] (Figure 1.) resulting in a new Emergent Programming (EmP) algorithm [5] that is capable of machine learning while having many paths leading to an efficient and effective[3] hardware implementation. The proposed Emergent Programming methodology employs a self-organizing hierarchical inductive learning algorithm and can be considered an instance in the space of Complex Adaptive Functional Networks (CAFN). In particular the 3-dimensional cellular learning model is based on a diffusion metaphor (Artificial Physics – Physicomimetics) [8] that allows the EmP/CAFN to dynamically adapt to new information while forgetting old information. The Emergent Programs presented in this paper are competitive with traditional GMDH results (Figure 2.) while being based on concepts (von Neumann Neighborhoods) that can be theoretically implemented in hardware using traditional or quantum computing [4]. [1] Y. Bar-Yam. Dynamics of Complex Systems. Westview Press, Boulder, CO, 1997. [2] H. de Garis. CAM-BRAIN: The evolutionary engineering of a billion neuron artificial brain by 2001 which grows/evolves at electronic speeds inside a cellular automata machine (CAM). In E. Sanchez and M. Tomassini, editors, Towards Evolvable Hardware; The Evolutionary Engineering Approach, pages 76–98, Berlin, 1996. Springer. [3] R. P. Feynman. Feynman Lectures on Computation. Westview Press, Boulder, CO, 1996. [4] T. Gram, S. Bornholdt, M. Gro, M. Mitchell, and T. Pellizzari. Non-Standard Computation: Molecular Computation - Cellular Automata - Evolutionary Algorithms - Quantum Computers. John Wiley & Sons. [5] J. H. Holland. Emergence, From Chaos to Order. Adison- Wesley, 1998. [6] A. G. Ivakhnenko, V. V. Konovalenko, Y. M. Tulupchuk, and I. K. Tymchenko. The group method of data handling - a rival of the method of stochastic approximation. Soviet Automatic Control, 13:43–55, 1968. [7] M. Sipper. Evolution of Parallel Cellular Machines: The Cellular Programming Approach. Springer-Verlag, Heidelberg, 1997. [8] W. Spears and D. Gordon. Using artificial physics to control agents, 1999.
*VLADISLAV KOVCHEGOV - THE LINGUISTIC MODELS OF INDUSTRIAL AND INSURANCE COMPANIES
Author(s):
Vladislav Kovchegov, Horizon Blue Cross and Blue Shield of New Jersey, USA
Abstract:
In this paper we discuss methods of using the language of actions, formal languages, and grammars for qualitative conceptual linguistic modeling of companies as technological and human institutions. The main problem following the discussion is the problem to find and describe a language structure for external and internal flow of information of companies. We anticipate that the language structure of external and internal base flows determine the structure of companies. In the structure modeling of an abstract industrial company an internal base flow of information is constructed as certain flow of words composed on the theoretical "parts-processes-actions" language. "The language of procedures" is found for an external base flow of information for an insurance company. The formal stochastic grammar for the language of procedures is found by statistical methods and is used in understanding the tendencies of the health care industry.
*KATHARINE MULLEN - Human-Technology Integration
Author(s):
Katharine Mullen, Boston University, USA
Abstract:
Human-technology integration is the replacement of human parts and extension of human capabilities with engineered devices and substrates. Its result is hybrid biological-artificial systems. We discuss here four major categories of products furthering human-technology integration: wearable computers, pervasive computing environments, engineered tissues and organs, and prostetics. We introduce examples of currently realized systems from each category. We then formulate two different predictions regarding the observational consequence of the replacement of every biologically-based human part with an engineered substitute.
*SUMATHI SEETHARAMAN - Self-Organized Scheduling of Node Activity in Large-Scale Wireless Sensor Networks
Author(s):
Sumathi Seetharaman, University of Cincinnati, USA
Ali A. Minai, University of Cincinnati, USA
Abstract:
Advances in MEMS (Micro-Electro-Mechanical Systems) technology have enabled the development of extremely small multi-functional sensing devices. Due to their miniature size, these micro-sensors can blend seamlessly with the environment and sense intricate information. Some key advantages of such pervasive sensor networks are: a) Ubiquitous, non-intrusive nature; b) Random deployment with limited or no pre-established infrastructure; c) Minimal supervision since nodes can self-organize into a functional network through local collaboration, d) Cost effectiveness; e) Flexibility; f) Scalability; g) Simple expandability; and e) Robustness. Large-scale sensor networks are being envisioned and applied in a wide range of scenarios like sensors embedded in a bridge to monitor for cracks, deployed in a field to track enemy movements or scattered in a forest to detect a fire breakout at an early stage. Field coverage is a critical issue for such event monitoring wireless networks. Given the spatiotemporal nature of the phenomena being observed, the sensor network must be able to detect and report its occurrence as quickly and accurately as possible. This leads to the problem of coverage: Ensuring that no part of the field remains unsensed for more than a specified duration. The sensor nodes used in random networks have limited resources (on-board battery, processing power and storage) and are vulnerable to failure. As individual nodes are lost, coverage (and communication connectivity) drops, rendering the network unusable. One way around this is to "over-deploy" nodes, i.e., start the network off with a significantly higher node density than is needed, and only turn them on when necessary. This turns the coverage problem into one of scheduling. Since the network is assumed to be unattended, such scheduling must be done by the nodes themselves in a self-organized fashion. To conserve energy, as few nodes as possible must be switched on at a time, but the load across the nodes must also be balanced to avoid creating "holes" in the network when highly loaded nodes fail. This ensures than coverage is maintained while maximizing the network's lifetime and producing graceful rather than catastrophic degradation. In this paper, we compare several decentralized, self-organized methods for scheduling nodes to obtain effective coverage. The focus in on assessing how well individual nodes in the network can locally estimate and optimize their schedules in order to achieve complete global coverage and a longer network lifetime at a lower energy cost. The primary constraints are energy spent on information exchange for setup, the communicating radius which limits the extent of local information available to the node, and the latency in determining the optimal schedule. We also look at whether some scheduling methods provide greater robustness to random loss of nodes (due to events in the environment) than others.
KAMPAN SAINI - Artificial Neural NetworkBased Offline Hand written character recognition for Postal Services
Author(s):
Kampan Saini, Panjab University Chandigarh, India
kampan saini, Panjab University Chandigarh, India
shailendra Singh, Panjab University Chandigarh
Abstract:
The most common goal of finding address constants such as city state country from letters is the recognition of offline handwritten character information. However, in order to do this, it is necessary to use a reliable and efficient process not only for recognizing character but also for extracting characters from letters , removing noise in written characters , joining characters to make a word , recognizing the meaning of word . In this paper we present a process for removing noise from extracted information using various filtering technique including orientation checking , recognizing characters using Neural Net technique , word recognition using plug and play learning mechanism. The main advantage of this process as compared with other similar approaches is that these methods easier to implement , requires less memory and time and they are cost effective.
REZA MEHRABANI - Statistical Modeling of Creep Strength of Austenitic Stainless Steels
Author(s):
Reza Mehrabani, Tehran University, Iran
Abstract:
The creep rupture life and rupture strength of austenitic stainless steels have been expressed as functions of chemical composition ,test conditions, stabilization ratio, andsolution treatment temperature. This statistical models includes of Gaussin, Lognormal, Gamma, Weibull ,…. This models are more general probabilistic models than nueral networks , Genetic system and classical methods for prediction and assessment of creep life. In this paper we test this models for our database and found best statistic model. The outputs of the models have been assessed against known metallurgical trends and other empirical modeling approaches. The models created are shown to capture important trends to extrapolate better than conventional techniques.
REZA MEHRABANI - Statistical Modeling of Austenite Formation in Steels
Author(s):
Reza Mehrabani, Tehran University
Abstract:
The formation of most steels products depends on chemical compositions and operating heating rates. Therefore, the need to know temperature of formation a phase a necessity in order to decrease extra costs. In this work we use than statistical modeling of austenite formation in steels and the present investigation introduces the statistical processes models for the empirical modeling of the formation of austenite during the continues heating of steels. At the previous works has examined the application of neural networks and Guassian process model to this problem, but the Guassian and Gamma Inverse processes. Models are a more general probabilistic models and are somewhat more amenable to interpretation. It is demonstrated that the models lead to an improvement in the significance of the trends of the temperatures as a function of the chemical composition and heating rate. In some cases, these predicted trends are more plausible than those obtained with neural network and Guassian process analysis’s. Additionally it is shown that many of the trace alloying elements present in steels are irrelevant in determining the austenite formation temperatures
GUSTAVO A. SANTANA TORRELLAS - A Framework for Security Model Innovation using Knowledge Engineering
Author(s):
Gustavo A. Santana Torrellas, Instituto Mexicano del Petroleo, Mexico
Abstract:
The traditional organisational Security Model is driven by pre-specified plans and goals, aimed to ensure optimisation and efficiencies based primarily on building consensus, convergence and compliance must be updated. Organisational information systems – as well as related performance and control systems -- were modelled on the same paradigm to enable convergence by ensuring adherence to classical information processes routines built into formal and informal information systems. Such routinisation of Information Systems and the technology related goals for realising increased efficiencies was suitable for the era marked by a relatively stable and predictable Organizational and Information Systems and Security development environment. However, this model is increasingly inadequate in the e-Information Systems and Security era that is often characterised by an increasing pace of radical and unforeseen change in the Information Systems and Security and Organizational’s environments. The new era of dynamic and discontinuous change requires continual reassessment of information and organisational routines to ensure that decision-making processes, as well as underlying assumptions, keep pace with the dynamically changing Information Systems and Security and social environments. This issue poses increasing challenge as ‘best services’ of the gone yesterday - - turn into ‘worst practices’ and core competencies turn into core rigidities. The changing Information Systems and Security environment, characterised by dynamically discontinuous change, requires a re-conceptualisation of Information Security Knowledge management systems, as they have been understood in information system practice and research. One such conceptualisation is proposed in this article in the form of a framework for developing organisational Information Security Knowledge management system for Security Model innovation. It is anticipated that application of this framework will facilitate development of new Security Models that are better suited to the new Information Systems and Security environment characterised by dynamic, discontinuous and radical pace of change. The problems and caveats inherent in interpretations are then discussed. The subsequent section discusses the demands imposed by the new Information Systems and Security environments that require rethinking such conceptualisations of Information Security Knowledge management and related information technology based systems. One conceptualisation for overcoming the problems of prevalent interpretations and related assumptions is then discussed along with a framework for developing new Organisation forms and innovative Security Models. Subsequent discussion explains how the application of this framework can facilitate development of new Security Models that are better suited to the dynamic, discontinuous and radical pace of change characterising the new Information Systems and Security environment. The popular technology-centric interpretations of Information Security Knowledge management that have been prevalent in most of the information technology research and trade press are reviewed.

Evolution and Ecology / Population Change

*DAVID SAAKIAN - Exact error threshold for Eigen model with general fitness and degradation rate functions.
Author(s):
David Saakian, Yerevan Physics Institute, Armenia
Abstract:
We derive exact analytical formulas for the error threshold of Eigen model with rather general form of fitness and degradation rate functions. For the some case of degradation rate the known quasispecies error threshold formula is modified. For the neutral mutations we rigorously derive the degree of selective ability ("neutral fidelity"), revising the known formulas in neutral evolution. It is possible a mutation threshold phenomena for the neutral fidelity.
*MARGARETA SEGERSTAHL - Coupling sexual reproduction and complex multicellularity
Author(s):
Margareta Segerstahl, Helsinki University of Technology, Finland
Abstract:
Understanding why multicellular organisms reproduce predominantly by sexual means has remained a major problem in evolutionary theory. A related question is the evolutionary origin of germ cell dimorphism, oogamy being the most extreme example. By using the concept of facultative (optional) sexuality and the simple dualism of cellular reproduction versus functional cell differentiation, it became possible to combine multicellularity, sexual reproduction, and oogamy in a novel way. The result is a network model in which developmental and evolutionary information merge. The model provides a formal framework for logical integration of theory and experimetal data regarding both evolutionary and developmental biology of complex multicellularity.
*LEN TRONCALE - Using Systems Isomorphies to Explore the Feasibility of Giant Planet, Wetlab, Origins of Life Simulations
Author(s):
Len Troncale, California State Polytechnic University, USA
Abstract:
This poster will present an unorthodox possibility for the origins of life based on perhaps the most common astronomical body in the universe, usually thought incapable of supporting life -- confirmed reports of giant planets outside of our solar system. It will present the arguments for and against non-carbon-based life forms and suggest how we could test for complex macromolecules and chemical contexts quite foreign to earth experiences. This topic is firmly grounded in research on complex systems because it posits that the relationships between our common carbon forms and their key solvent, water, can be imitated, systems relationship for systems relationship, by wholly different combinations of non-carbon chemistries and solvents at very high, non-terran temperatures and pressures. These extreme environments would render our life systems completely non-functional, but could reasonably be expected to make components that do not participate in life at minimal terran temperatures and pressures capable of complex life-systems mechanisms on giant planets. While entirely different in specifics from our life forms and components, these non-terran systems would be isomorphic to ours in general systems processes. These similarities enable our prediction of key components, forms, and interactions. Some tools from systems theory (suggested earlier by the inimitable Fritz Zwicky) will be cited that might allow modeling, simulation, and wetlab testing of these non-carbon chemistries in our high tech engineering and robotics labs. The systems tools would reduce the search time needed to explore the vast potential space of chemical combinations, solvents, and energy sources offered by giant planets for non-terran origins of complex chemicals and thus life. While unconventional, these simulations are needed to complement the highly focused assumptions and presumption of current attempts to find life by searching only for carbon-based organics and water in our space exploration. We may simply be missing important signals because of our history of anthropocentrism.

Nonlinear Dynamics and Pattern Formation

*BURTON VOORHEES - Emergence of Cellular Automata Rules Through Fluctuation Enhancement
Author(s):
Burton Voorhees, Athabasca University, CANADA
Abstract:
We consider a model in which random sequences of binary digits are generated and tested to see if they could have followed from a cellular automata rule. In cases in which this is true, the probability of sequences following the particular rule is increased. This leads to the eventual appearence that the sequence generating process is following a deterministic rule, although it is in fact based on a random process with reinforcement. Probability distributions are computed and competition between different rules is studied.
*CHIH-HAO HSIEH - Regime shifts or red noise?
Author(s):
Chih-hao Hsieh, Scripps Institution of Oceanography, University of California-San Diego, USA
Andrew Lucas, Scripps Institution of Oceanography, University of California-San Diego, USA
Sarah Glaser, Scripps Institution of Oceanography, University of California-San Diego, USA
George Sugihara, Scripps Institution of Oceanography, University of California-San Diego, USA
Abstract:
Understanding decadal-scale variability of the North Pacific marine ecosystem has been an important issue because some strong environmental changes have occurred at this time scale. Studies of many physical and biological time series lead to a speculation that a regime or ocean condition may persist for 2 to 3 decades and then undergo a rapid shift to another state. Many nonlinear mechanisms exist that can cause a system to switch abruptly among multiple stable states. However, zero-mean Gausian red-noise time series have long runs without a zero crossing might be easily mistaken as regimes separated by abrupt shifts. The notion of regime shifts has had widespread influence on oceanographic researches although the debate continues between “regime shifts” and “red noise” schools. Therefore, there is a need to distinguish regime shifts from red noise in oceanographic time series with a quantitative method. One possible way to detect whether or not a “regime” signal exists as a product of an underlying dynamic instability is to see if the physical and biological time series contain a nonlinear signature. Uncovering a nonlinear signature in the time series is a “necessary” condition. The idea behind determining nonlinearity of a time series is to examine whether or not there is a significant improvement in out-of-sample forecast performance with an equivalent nonlinear versus a linear forecast model. In this research we applied nonlinear time series analyses on physical and biological time series in the North Pacific. Physical data include the Pacific Decadal Oscillation Index, North Pacific Index, and Scripps pier sea-surface temperature; biological data include the ichthyoplankton time series collected in the California Cooperative Oceanic Fisheries Investigations surveys, the diatom time series collected under the Scripps pier, and fish catch and recruitment data in the Northeast Pacific. We found that the physical time series cannot be distinguished from red noise. This is in contrast to the biological time series that show nonlinearity. We suggest that the biological response to external linear physical forcing and/or internal species interaction is nonlinear. This implies that biota do not simply track physical signals. To achieve better ecosystem management, an understanding of underlying dynamic instability is necessary.

Physical Systems, Quantum and Classical

*BHARAT KHUSHALANI - Vortex Analogue of Molecules
Author(s):
Bharat Khushalani, University of Southern California, USA
Abstract:
Kelvin's theory of vortex atoms, in which Kelvin considered knotted strings as atoms, has been debunked and considered to be a failure. A theory of atoms as vortices is incapable of explaining stability and vibrational properties of atoms. With electrons representing ethereal vortices, the vortex atom theory tries to explain the relation between magnetic field and electrical current. In recent years, this simple Kelvin model has been shown to bear resemblance to the superstring theory. Although the vortex atom theory is considered to be scientifically incorrect (in the sense of its being unable to explain atomic properties), it may still be valid in a dynamical sense as for an example considered in this paper. With atomic potentials of logarithmic type, a dynamically stable vortex buckyball is 'grown' here. If stability is considered only from point of view of Huckel theory and eigenvalues of adjacency matrix, it may not be a sufficient test. It will be shown that such a vortex molecule is stable when Floquet theory of periodic orbits is used as a test of stability.
*JONATHAN VOS POST - IMAGINARY MASS, FORCE, ACCELERATION, AND MOMENTUM
Author(s):
Jonathan Vos Post, Woodbury University, USA
Professor Christine M. Carmichael, Woodbury University, USA
Andrew Carmichael Post, California State University, Los Angeles, USA
Abstract:
This paper analyzes a possible emergent behavior of subatomic and astrophysical systems, which involves Complexity at four levels: (1) dynamic implications of assigning a Complex value to variables which, by tradition, were assumed real; (2) analysis of the related literature in Newtonian, Quantum Mechanical, Relativistic, and String Theory contexts, which have a social and conceptual complexity from their mutually different assumption; (3) the possibility of pattern formation shortly after the Big Bang, in high-energy events today, and in hypothetical dimensions beyond 4-D space-time; and (4) practical complexity in performing experimental tests of these hypotheses. This paper constitutes a preliminary discussion of a foundational question. Are imaginary mass, imaginary acceleration, imaginary force, and imaginary momentum under any conditions ever "Physical" (i.e. in principal observable by direct or indirect means) or "nonphysical" (i.e. theoretically amenable to calculation, but inherently unobservable in the real world)? The discussion begins by hypothesizing a particle or object of positive imaginary mass in a co-moving frame of reference, and considers some logical consequences. One unusual interpretation is that imaginary mass allows for objects to “disappear” from our ordinary space-time and “leave the brane” to go somewhere perpendicular to ordinary reality. The predictions in this paper are “far out” – even Science Fictional, yet they do not obviously violate Quantum Mechanics, Special Relativity, or General Relativity. They are in the broad context of the scientific literature. They may have both microphysical and macrophysical observability in the laboratory or cosmologically. We review the related literature on mass, in Quantum Mechanics and Special Relativity; return to a pseudo-Newtonian analysis; and then approach the complexity of modern theory and speculation. [This Abstract is of Draft 5.0 of 9 January 2004 (approx 10,100 words) [It is written specifically for the International Conference on Complex Systems, hosted by the New England Complex Systems Institute, Marriott Boston Quincy, 16-21 May 2004]
*JONGJIN LEE - Slow relaxations of randomly packed ball bearings
Author(s):
Jongjin Lee, Seoul National University, Republic of Korea
Chang Won Lee, Seoul National University, Republic of Korea
Insuk Yu, Seoul National University, Republic of Korea
Abstract:
We discovered that the electric resistance of randomly packed stainless steel ball bearings exhibit slow relaxation behavior. We find that this relaxation is related to structural change of packing. Simultaneous measurment of the bottom weight of the balls and electrical resistance showed one-to-one correspondence.
*ALESSANDRO PLUCHINO - Glassy dynamics hindering synchronization in a Hamiltonian system
Author(s):
Alessandro Pluchino, Universita' di Catania and Infn sezione di Catania, Italy
Vito Latora, Universita' di Catania and Infn sezione di Catania, Italy
Andrea Rapisarda, Universita' di Catania and Infn sezione di Catania, Italy
Abstract:
We present recent numerical simulations of a fully coupled Hamiltonian system of classical rotators. The system shows interesting dynamical anomalies before syncrhonization towards the equilibrium in an energy range before a second order phase transition. For out-of equilibrium initial conditions, the system rapidly cools down and gets trapped in a complex energy landscape, showing a metastable glassy dynamics. This situation seems quite generic and common to other nonextensive systems. Connections to Tsallis thermostatics and spin-glasses is addressed. A comparison with Kuramoto model is also discussed.

Learning / Neural, Psychological and Psycho-social Systems

MIHNEA MOLDOVEANU - The Economics of Cognition. III. A Weak Axiom of Revealed Cognitive Preference
Author(s):
Mihnea Moldoveanu, University of Toronto, Rotman School of Management, Canada
Abstract:
This paper models cognitive rationality and formulates a weak axiom of revealed cognitive preference (WARCP) for its study. It starts from the basic intuition that when an agent chooses to believe (as revealed by choices or betting behavior) one model, theory or prepositional belief over another, that agent does so on the basis of a set of reasons, adherence to which in the face of refuting or confirming evidence reveals (a) a preference for a certain inference logic over another and (b) a preference for a certain kind of justification logic over another. The space of inference and justification logics is characterized in the paper. Inference logics are shown to follow one of three basic patterns (inductive, deductive or abductive). Justification logics are shown to follow one of two basic patterns (justificationist and falsificationist) with sub-categories properly corresponding to each (circular, regressive and dogmatic justificationism and dogmatic or methodological (naïve or sophisticated) falsificationism). Experimental paradigms for the study of foundational cognitive choices are studied, and a Weak Axiom of Revealed Cognitive Preference (WARCP) is articulated.
*CHRISTOPHER NEWMAN - AN ANALYSIS OF THE COMPLEXITY OF THE SYSTEM EVERYTHING DEVELOPED FROM AND RAISED FROM HEAVEN AND EARTH IS IN ITS PROPER PLACE OF FORM MIND BOXING
Author(s):
Christopher Newman, Elgin Community College / Roosevelt University, USA
Abstract:
Xing Yi Quan (alternatively Hsing I Chuan—“Form Mind Boxing”) is a Chinese Internal Martial Art. Its origins are uncertain, but its lineage can be traced back more than a century. Xing Yi Quan is a system composed of five elements: Splitting Fist, Smashing Fist, Drilling Fist, Pounding Fist and Crossing Fist. The interrelations of the elements are formalized in the art in terms of Mutual Production (e.g. Splitting Fist generates Drilling Fist and is generated by Crossing Fist) and Mutual Conquest (e.g. Splitting Fist counters Smashing Fist and is countered by Pounding Fist.) The relations of generation by and countering by are completely stated by the systemic rules of behavior for each strike. The complete physical expression of the Five Fists is contained in the two person set An Shen Pao (“everything developed from and raised by heaven is in its proper place.”) This paper proposes to analyze An Shen Pao as a complex system emerging from the Five Fists. Because of the definitions of Mutual Production and Mutual Conquest fully define the interrelations of the component elements of the system those relations can be entirely measured. Similarly, the complexity of the set can be determined through examining each of the strikes of the two participants in terms of environment and action and charting the function of adaptation to the information received from the environment (opponent’s move.) As An Shen Pao is a closed and fully described system, a complete analysis of the complexity of this physical system can be done and an accurate conclusion as to its degree of complexity calculated.
S. HAGBERG - Human interaction and nonlinear oscillators
Author(s):
S. Hagberg, Brown University, USA
Abstract:
Human biology is a system of coupled oscillators interaction to produce and reproduce itself. Human interactions are one manifestation of the biology and both emerge out of and shape that underlying biology. This paper will describe relatively simple human interactions in terms of coupled or embedded oscillators and look at how known properties of such systems, like partial revivals, can be used to describe otherwise aberrant and irrational interactions.
*TETSUJI EMURA - A Coupled Oscillator Model for Emergent Cognitive Process
Author(s):
Tetsuji Emura, Kinjo Gakuin University, Japan
Abstract:
Numerous papers have been published concerning mathematical models of memory and learning of human brain activity. However, research proposing mathematical models for the creative cognition process such as intuition for linkage to creativity has seldom been conducted. In this paper for a description of the emergent cognitive process, the author proposes a new Lorenz model with two parameters, temporal coefficient c and spatial coefficient d. This spatiotemporal coupled Lorenz model is a device that has coupled three 1-dimensional information codes. This device can be used as an emergent device for three channels through control of on-off intermittent chaos as observed in this model with the c and d as parameters. The c and d control on-off intermittent chaos, although they have no direct effect on individual vectors, the c and d work as independent parameters without providing internal disturbance. The wandering on the three 1-dimensional information coded space in the burst phase with seeking and gathering of valuable information from this, synchronized stabilization on a point in the laminar phase can be modeled as a process that intermittently and irregularly repeats. For instance, this is a motivation of the research, when one hears a musical work, one is listening while perceiving mainly the elements comprising the musical work, one is listening the textures that these numerous elements weave. The subject of music perception and cognition is often experienced as a rapid and irregular change with time. (Note: Using an example of a visually perceived phenomenon, the cognitive process is where the figure and background change rapidly and irregularly in turns like Rubin’s vase or Necker cube. The proposed model may concern coincidence detector, e.g., Gray & Singer [1989], Koenig & Schilen [1991], Varela et al [1999].) Then, analyzing the musical work’s structures of Beethoven, Brahms, Wagner, for example, like Masterpieces, a melody is present here, although the melody and harmony are inseparable; there is absolutely no way to first have the melody and then harmonization with it. Moreover, the melody and harmony are allocated to individual instruments such as woodwinds, brasses and strings for respective sounds and with harmonic progression are changed to be extremely effective as melody; if melody and harmony do not exist simultaneously and if changes in both harmonic progression and timbre in the process of creation do not exist simultaneously in the brain of the composer as a sound image, then creation of a work like this would be close to impossible. That is, harmony, melody, and timbre are in a mode where they are blended into one another and creation must be interpreted to progress with simultaneous processing of these in parallel in the brain of the composer. However, the music theory proposed until now is only static system theory, e.g., GTTM [Lerdahl & Jackendoff, 1983], PCST [Forte, 1973], and dynamic system theory do not exist yet. In this presentation, the author discusses about proposed paper and mentions also a work: Emura [1999], "Les Papillons de Lorenz", Editions Billaudot, Paris.
*ROSEMARY WILLIAMS WRAY - Towards More Generative Evaluation, Research and Assessment (ERA) in Education
Author(s):
Rosemary Williams Wray, Education Consultant and Researcher, USA
Abstract:
Building on work conducted under an NSF Research Grant in Constructive Assessment. and a subsequent NSF Creativity Award, this paper advocates the application of a complex dynamic systems approach in education. The approach was developed as a result of research conducted on field practice and on transdisciplinary theoretical issues over a period of ten years. The Generative ERA Project acknowledges the complexity of the evaluation, research and assessment functions in education by a) exploring systemic patterns which have emerged across these common, necessary topics: Information, Decision-making, Expectations, Uncertainties, Questions, Validities, and b) generating possible systemic patterns which can be used to improve design in education.
*DONG-UK HWANG - Multistability of Coupled Neuronal Nets with Multiple Synapses
Author(s):
Dong-Uk Hwang, Chungbuk National University, South Korea
Sang-Gui Lee, Pohang University of Science and Technology, South Korea
Seung Kee Han, Chungbuk National University, South Korea
Hyungtae Kook, Kyungwon University, South Korea
Abstract:
The human brain is a dynamical system containing an extraordinary large number of units of neurons with even larger number of interconnections among them. Multistability may well be ubiquitous phenomena in such complex dynamical systems. Meanwhile, from the stereotypical behaviors of the brain it is apparent that a certain state is preferentially selected which is specific to an associated brain function. Therefore, it would be important to understand the existence of multistability of the system and the mechanism for the preferential selection, both of which should depend on the system parameters. The phase model reduction is the wellknown method for analyzing behaviors of dynamical systems with a reduced degree of complexity of the original systems. In this work we revisit a coupled neuronal net with multiple synapses that has been previously suggested as a model for the hippocampal CA1 area and attempt the phase model analysis on the phenomena of multistability of the synchronized rhythms in the system. Especially, we focus our attention on the functional roles of different types of the synapses on the stability of the gamma and the beta rhythms in the coupled neuronal net model.
*LEN TRONCALE - Science General Education As A Way to Attract More Students to Studying Complex Systems
Author(s):
Len Troncale, California State Polytechnic University, USA
Abstract:
Where in our current educational programs do students encounter direct study of systems? If they are not exposed to the joy and power of systems integrated science, how can they choose it as a career path? One of our obligations as an emerging new science is to design, test, and deliver new systems education programs. Are we fulfilling this obligation? Some NSF programs include systems tools and concepts for K-12 (e.g. Leroy Hood’s Systems Education programs or Jay Forrester’s Systems Dynamics Simulation-based Creative Learning Exchange). But there are few similar examples for the college curriculum. The SISGE (Systems Integrated Science General Education) program would offer a national collaborative alternative adaptable to virtually any community college or university curriculum. It fulfills the entire science general education requirement in a year of computer augmented study. The plan is to form an association of user-producers, called the S.I.S. (Systems Integrated Science) Alliance, who will cooperate to enrich existing ISGE systems-based computer modules and case studies. Development of this program was enabled by NSF, HHMI, and CSUS support. This poster will introduce the 40 obstacles that inhibit effective science general education and design of interdisciplinary, systems-based courses and the 25 special methods and frameworks embedded in the SISGE courseware for overcoming those obstacles. It will explain the basic idea behind the courseware and the several methods used to integrate study of the key phenomena, facts, and tools of seven sciences, not as separate disciplines but as natural systems with many significant similarities. Student performance and assessment data from seven test pilot runs of the courseware at three universities will be cited. The strategy of “stealth” systems science in GE service is to expose the student who has not yet considered science to take science and perhaps even to study complex systems. Edward Witten, who has revolutionized string theory, began college as a history major and expected to be a journalist. But after changing majors to science, he became one of our current leading physicists responsible for a dozen breakthroughs in theoretical research. If we could expose large numbers of college GE students to systems science, probability would favor a concomitant increase in students entering the “pipeline” that supplys educators and researchers for complex systems. For maximum impact, SISGE will first encourage adoptions across the California State University System of 23 universities and an enrollment of 375,000 students. 90% of these students are non-science, non-engineering majors eligible for taking SISGE. We would follow with a coordinated set of adoptions at our 109 California Community Colleges with an enrollment of 2.9 million students. Having the same course available to satisfy the year of required science general education in both systems would greatly ease the continuing problem of articulation between both systems and loss of student time to degree. Follow-on NSF proposals for national dissemination could allow exposure of additional large number audiences at systems like SUNY, CUNY, or Texas, and interested liberal arts colleges. The more students exposed to systems, the greater likelihood of increased numbers of students continuing in the study of complex systems.

Concepts, Formalisms, Methods and Tools

*AXEL G. ROSSBERG - A generic scheme for choosing models and characterizations of complex systems
Author(s):
Axel G. Rossberg, Yokohama National University, Japan
Abstract:
What is the relationship between a model of a complex system and the system itself? Are some models better then others? What is an "approximation" for a complex system. What does it mean for a property to be "characteristic" for a structure? The talk introduces a generic, systematic approach to these questions. It is based on the observation, that useful computational methods have both low computational and low algorithmic complexity. A mathematical formalism is developed that integrates these ideas with a realistic model of a computer controlled experiment. As a demonstration, the formalism, is applied to the problem of modeling and characterizing the spatio-temporally chaotic solutions of the Kuramoto-Sivashinsky equation. Compared to earlier work that applies concepts of computer science to structure in complex systems (often under the keyword of "emergence"), the approach used here considers both problems that of modeling and that of characterizing, and their relationship. As a result, approximate descriptions of complex systems can be obtained without having to reduce the information about the system state "manually" as the first step in the analysis. By taking the tradeoff in the cost of program length and execution time explicitly into account, the large variety of description that exists for typical complex systems is reproduced. By the inclusion of statistical tests and models into the formalism, the conceptual framework is expanded, so that many practical problems that naturally occur in the study of complex systems, are better modeled.
*SORIN BAICULESCU - Mathematical Aspects in Complexity of Biological,Neuro-Physiological and Psychological Systems
Author(s):
Sorin Baiculescu, Romania
Sorin Baiculescu, Cybernetics Academy "Stefan Odobleja", Romania,Washington Street 2A,Sector1,Bucharest
Abstract:
Mathematical Aspects in Complexity of Biological, Neuro-Physiological and Psychological Systems Keywords:complexity,bio-systems,network Abstract: This paper analyses the organizing of biological, neuro-physiological and psychological systems belonging to human being within the context of multidimensional hierarchical development networks(ANs),biostructural theory(MBt) and informational theory(I). The complexity of the multi-dimensional hierarchical development networks (ANs) varies horizontally, within the same level and vertically, from the inferior to the superior level.The hierarchical, evolutive and multidimensional character of the network (ANs) is generated by the rank of the component sets and subsets, the most comprehensive set also including the set of evolutive relations. Multidimensionality is understood as a number of criteria according to which the components of the same rank is classified. The set of the lower rank network makes up the subset of zero rank which be- longs to the set of immediately superior rank.The cooperative and hierarchical system of the multidimensional hierarchical evolution networks is characterized by non-liniar evolution processes having the mathematic general form,in which are considering the state parameters,vector associated to the spatial coordinates,time,non-linear function,integration(global character of the subsystems cooperation and enslaving),operator attached to the systemical (diffusions)flows,vector associated to the order parameters in diferential non-linear forme equations. Biostructural theory (MBt) considers the following hierarchical levels:I–system of the coexistant molecular matter (intracelular solution and non-dissolved chemical combinations); II–biostructure system (spongy mass and intracapilar spaces); III– noesis-structure system (struc- tured–noesis mass and coexistant biosical matter (the cortex and the cerebral hemispheres). The analysis of psychological system consider the entropy and informational energy of these, in deterministic and continuously aspects, in the context of multidimensional hierarchical development networks(ANs)and biostructural theory(MBt).
*RUSS ABBOTT - Emergence and Entities
Author(s):
Russ Abbott, California State University, Los Angeles, USA
Abstract:
Emergence and Entities Emergence is the appearance of a macro entity or property from micro components. Attempts to characterize emergence have been only partly successful. To date, emergence has been treated primarily in three ways: (a) as a vague concept that seems to point to something (e.g., swarm behavior) that we can't quite characterize; (b) as a spooky phenomenon (e.g., vitalism) that is beyond scientific investigation; (c) as little more than a fancy new term for entailment. In this talk I will discuss emergence as a physically real phenomenon. In addition, since the notion of emergence depends fundamentally on the notion of an entity (after all, it is an entity of some sort that one thinks of as emerging), I also plan to talk about entities and how they may be characterized. I will distinguish among a number of types of emergence. · Multi-process emergence: the emergence of a pattern of activities from the interactive performances of concurrent processes, e.g., swarm emergence. · Single process, temporal, or sequential emergence: e.g., the emergence of a melody from a collection of notes by sounding a collection of notes in a particular sequence or the emergence of a (single threaded) computer process by executing a collection of computer instructions in a particular sequence. · Syntactic or structural emergence: e.g., the emergence of meaning (in the formal semantics sense) as a structured collection of symbols or the emergence of an algorithm as a structured collection of operations. · Static emergence: e.g., the two dimensionality of cloth arising from the one dimensionality of thread. · Definitional emergence: e.g., the emergence of prime numbers within the context of the natural numbers. I will discuss (a) the parallel between rules of composition in emergence and fundamental forces in physics: the fundamental forces are, in effect, rules of composition that allow micro elements to combine to form macro elements and (b) the parallel between emergence in general and the concept of object instantiation in object-oriented programming. Emergence presupposes the notion of entity. (When one talks about emergence, one talks about the emergence of some thing.) I will argue that we have grouped four different categories of concepts under the general notion of entity. · Physical entities: aggregations of mass with the property that the sum of the masses of the components, were they considered separately, exceeds the mass of the aggregation, e.g., an atomic nucleus. This applies to physical entities at all levels and essentially means that degree to which some physical aggregation may be considered an entity may be characterized by the energy required to break it apart. A handful of wet sand is less of an entity than an atomic nucleus. · Attractor entities: e.g., a lake, which consists of the water that has gathered at a low-point. It is really the attractor and the structure it creates as a container, i.e., the lake bed rather than the stuff that is contained, that defines the entity. · Process entities: an environment within which a process operates as long as resources are available, e.g., a fire. The environment is the area hot enough to support combustion. The resources are the combustible materials. Another example is a glider in the game of life. The environment is the collection of cells that effect or are affected by the glider cells at any time step. In the game of life, the resources are not relevant since they are supplied by the rules that define cellular automata. As two gliders meet, their environments merge, leading to a merged entity, which differs from the two original separate entities. These are the most interesting kinds of entities in that they are what we often give as examples of emergence, e.g., a swarm. The issue in discussing these entities is to characterize (a) the processes that define them, (b) the environment that supports the continued performance of those processes, and (c) the resources the processes consume, if any. Most entities in this category are not physically fixed: the physical materials that participate in one of these entities, e.g., a fire or a human being, change as the entity’s processes proceeds. The entity is the process and environment, not its materials. · Definitional entities: whatever we define to be entities, e.g., a pair of socks. These are the least interesting entities since they are completely arbitrary. They are useful for creating a structure for process entities to inhabit, e.g., a corporation—although for a corporation to persist usually requires the payment of an annual fee, i.e., a minimal process and the consumption of minimal resources. We also use them to make mental models—which may or may not turn out to be useful.
*P. FRAUNDORF - Net surprisals ala Tribus: correlations from reversible thermalization
Author(s):
P. Fraundorf, University of Missouri - St. Louis, USA
Abstract:
The Bayesian vision of net surprisals underlying the connection between energy and information, put forward by Myron Tribus four decades ago, has new life today. One example is widespread application of mutual information to the study of correlated codes, quantum computing, and nonlinear dynamics. We show here that net surprisals can also help students quantify finite departures from the ambient in second law terms, and offer a framework for tracking hierarchical correlations in complex systems (along with the replicable codes used to nurture those correlations).
*DAVID H. WOLPERT - Adaptive Metropolis Sampling with Product Distributions
Author(s):
David H. Wolpert, NASA, USA
Chiu Fan Lee, Oxford University Physics Department, United Kingdom
Abstract:
The Metropolis Hastings (MH) algorithm is a powerful computational technique for sampling a provided high-dimensional distribution P(x) by generating a Markovian random walk X(t) that ergodically traces P. Its applications range from high-dimensional integration (Markov Chain Monte Carlo) to function optimization (simulated annealing). At each iteration of the algorithm, t, a proposal distribution T(x) is sampled to get a point x', and the next point on the random walk, X(t + 1), is either set to the current point X(t), or to x', depending on the ratio T(x') P(X(t)) / P(x') T(X(t)). In this paper we present theoretical and experimental investigations of the relation between MH and a new set of tools called Product Distribution (PD) theory. In particular, we investigate having T be a product distribution, T(x) = T1(x1) T2(x2) ... T(xN). Since such a T is relatively low dimensional, it can be adaptively updated, in a Markovian fashion based on the points visited during the walk, so that is becomes an increasingly accurate approximation to P. Doing this should ensure that X(t + 1) differs from X(t) more often than it does in conventional MH, and therefore that the random walk more efficiently traces out P. There are other benefits to such adaptive MH in addition to more efficient sampling. For example, by generating the product distribution T, adaptive MH provide an approximation to P amenable to data analysis and visualization. Conversely, gradient-based techniques for forming PD approximations to P can be used before the start of a conventional MH, to formulate the (fixed) proposal distribution of that application of MH.
MARK AVRUM GUBRUD - Definining "nano" in terms of complexity
Author(s):
Mark Avrum Gubrud, University of Maryland, USA
Abstract:
"Nanotechnology" and "nanoscience" have been defined in many ways, provoking many arguments. The issue not merely semantic when funding is at stake. I propose that spatial complexity density may be used to formulate a concise, general, technical definition that automatically locates systems with respect to the domains of nanoscience and nanotechnology. I argue that such a definition captures the conventional meaning and usage of these terms. Measures of complexity density include the number of bits required to specify a unit volume of structure, or the spatial spectral bandwidth of the matter distribution. While Shannon's theorem allows us to discount noise from bandwidth, spatial bandwidth and noise form a two-dimensional space within which we can locate crystals, chips, life, etc. Our control of these is separably measurable in the same terms. The objective of nanotechnology is the highest bandwidth and lowest noise in the channel connecting intentions to actual products.
GILBERT G. CHEN - On Simulated Time
Author(s):
Gilbert G. Chen, Rensselaer Polytechnic Institute, USA
Boleslaw K.Boleslaw K. Szymanski, Rensselaer Polytechnic Institute, USA
Abstract:
This paper introduces our new understandings on the semantics of simulated time. The new understandings enabled us to make two important progress in Discrete Event Simulation. First, we realized that simulation models handle simulated time in different ways, and classify them into three classes accordingly. This allows simulation s, especially large-scale complex ones, to be built in a more natural way. Second, we observed that simulated time is reversible, and came up with the concept of lookback, which greatly advanced the technique of parallel discrete event simulation.
*ROBERT CLEWLEY - Dominant-scale analysis for automatic reduction of high-dimensional ODE systems
Author(s):
Robert Clewley, Boston University, USA
Nancy Kopell, Boston University, USA
Abstract:
Systems of ordinary differential equations arise commonly as models in the natural sciences, often with multiple time-scales. State-dependent coupling can add to the complexity, introducing new time-scales. These time-scales may not be explicit in the equations. We have developed a computational technique that can be used to reduce the study of such networks evolving near a known trajectory, to a set of low-dimensional approximate models. The dominant-scale technique adds rigor to intuitive reduction techniques that are ubiquitous in modeling high-dimensional coupled systems, and is different to a center manifold reduction. In particular, it quantifies the robustness and parametric dependence of coherent temporal activity along the entire length of a known trajectory. It also provides a quantitative basis for rigorously defining intuitive concepts such as "emergent structure", "evolving sub-systems", etc., in spatially-extended physical models. We demonstrate our analysis software on an example network of Hodgkin-Huxley equations for biological membrane excitability.

Other Complex Systems Topics

GERARD S. LEMIRE - Formating Complex Systems and Aspects of Cross-disciplinary Research
Author(s):
Gerard S. LEMIRE, independent synthesis researcher (retired systems engineer), USA
Abstract:
This paper proposes and details the formating or structuring of four aspects/activities judged important if not critical to current trends in complex living systems and modern science: complex systems, various fields involved in interdisciplinarity, methodology for different types of science, and implementation approach to different levels of research. As an important complement to the systems approach to the evaluation of improvements, types of synthesis are described and discussed. The above was developped as part of personal research into a new science of Humanity Advancement and illustrations for the above proposed factors relate to this work. Comments are added on the possibility of establishing a new meta-science of complex systems and modern science.
*DANIEL POLANI - Defining Emergent Descriptions by Information Preservation
Author(s):
Daniel Polani, University of Hertfordshire, UK
Abstract:
The task of defining emergence in a suitable way is an important issue in the research of complex systems and its difficulty is apparent from the larger number of various of formal definitions brought forward to this purpose. Recently, e.g. in (Rasmussen et al. 2001), a promising formalization has been made based on category theory. One of the problems remaining with definitions based on meta-models like category theory is that, while precise, in practice they are often computationally inaccessible and they make it difficult to allow for a natural concept of emergence arising from the intrinsic structure of a system. Guided by the need to have a more generic, computationally accessible and useable mathematical model for emer-gence, here, we therefore propose a formal concept of emergent description by a decomposition of a stochastic dynamic system into approximately independent subsystems. Our approach is inspired by the natural decompo-sition of the collective dynamics in crystals into individual oscillatory modes, phonons (Born and Huang 1954). This case is limited by the fact that it requires the systems to be linear. In the field of synergetics, an approach to decompose also nonlinear systems in a natural fashion is undertaken (Haken 1983). The natural decomposition of dynamical systems near fixed points into stable, central and unstable manifolds is reinterpreted in a heuristic way, separating fast foliations and slow manifolds in the system (Mees 1981). The slow parameters (master modes) of the system dynamics are said to enslave the fast degrees of freedom (slave modes). The master modes can be construed as emerging from the system dynamics. This system decomposition into individual subsystems arises naturally from the system dynamics and is therefore not limited to the eye of the beholder (Harvey 2000) usually assumed to be needed with emergent phenomena. Also, decomposition is not limited to systems exhibiting different time scales. In fact, under certain conditions it is possible to decompose nonlinear dynamic systems canonically into weakly coupled subsystems even if they have no separate time scales (Winter 1997). Above decompositions can be further generalized using information theory. Haken (2000) takes a step towards an information-theoretical view of synergetics; it is well-known that dynamical systems can be well described using information theory (Wolf 1999; Deco and Schuermann 2001); the dynamics is translated into the information-dynamical language via an a priori choice of the space partition. However, it would be desirable to have an approach where the decomposition emerges naturally from the structure of the dynamical system and is not imposed upon it. Our approach to define emergent descriptions is based on taking up the decomposition principles derived from a close analysis of the aforementioned simplest linear dynamical systems (see second paragraph), generalizing them to nonlinear systems using information theory. We define emergent descriptions as a complete decomposition of the system into independent subsystems which are individually predictable (Polani 2002). These three aspects can be directly formulated in an information-theoretic fashion (and thus making them, at least in principle, com-putationally accessible): if 1. a general dynamical system (discrete or continuous, deterministic or stochastic) is decomposed in such a way that the totality of subsystems always provide complete information about the state of the total system, 2. these subsystems are informationally independent from each other s dynamics and 3. the indi-vidual subsystems maximally preserve information, we consider this decomposition a natural emergent description of the system. The plausibility, impact and usefulness of the concept is explored in several examples. The present concept is related, but goes beyond the related concepts of Independent Component Analysis and clustering by deterministic annealing by introducing a temporal dimension. The individual subdynamics can be interpreted as closed subsystems (or approximations thereof). In larger systems, we expect that one would obtain a hierarchy of subsystems with different degrees of independence. This would provide a useful perspective to model biological super-organisms, organisms, and organelles; it would allow to cover both the aspect of information exchange between different units and the structural organization of the system. Evolution is then just a specific form of system dynamics. Evolutionary transitions or important events will be reflected by structural changes in the identified hierarchy. A detailed study of the ability of the model to incorporate these effects is the topic of current research. References Born, M., and Huang, K., (1954). Dynamical Theory of Crystal Lattices. Oxford, England: Clarendon Press. Deco, G., and Schuermann, B., (2001). Information Dynamics: Foundations and Applications. Springer. Haken, H., (1983). Advanced synergetics. Berlin: Springer-Verlag. Haken, H., (2000). Information and Self-Organization. Springer Series in Synergetics. Springer. Harvey, I., (2000). The 3 Es of Artificial Life: Emergence, Embodiment and Evolution. Invited talk at Artificial Life VII, 1.-6. August, Portland. Mees, A. I., (1981). Dynamics of feedback systems. John Wiley & sons, Ltd. Polani, D., (2002). On Individuality, Emergence and Information Preservation. In Nehaniv, C. L., and te Boekhorst, R., editors, Proceedings of the Symposium on Evolvability and Individuality, 18-20 September 2002, St. Albans. University of Hertfordshire. Rasmussen, S., Baas, N., Mayer, B., Nilsson, M., and Olesen, M. W., (2001). Ansatz for Dynamical Hierarchies. Artificial Life, 7:329 353. Winter, S., (1997). Zerlegung von gekoppelten Dynamischen Systemen. Diploma thesis, Johannes Gutenberg-Universitaet Mainz. (In German). Wolf, F., (1999). Berechnung von Information und Komplexitaet in Zeitreihen - Analyse des Wasserhaushaltes von bewaldeten Einzugsgebieten. Dissertation, Universitaet Bayreuth. 2

TUESDAY, May 18

9:00AM-12:20PM EVOLUTION

*CHARLES GOODNIGHT - Evolution

*STUART KAUFFMAN - Understanding Genetic Regulatory Networks: the ensemble approach
*ALAN PERELSON - Immunological Evolution
*MARTIN NOWAK - Evolutionary systems
*LISA MARIE MEFFERT - Experiments on the escape from extinction: Lessons from the common housefly.
Author(s):
Lisa Marie Meffert, Department of Ecology and Evolutionary Biology, Rice University, USA
Abstract:
Conflicts and gaps in our understanding of the genetics of inbreeding call for comprehensive examinations of how populations can escape imminent extinction. Artificial selection projects, such as in agricultural programs, commonly reveal catastrophic effects of inbreeding. In theory and in practice, immigration should rescue such endangered populations. This basic assumption, however, has not been tested in an evolutionarily relevant context. Moreover, many natural populations appear to have adapted to high levels of inbreeding, without requiring such rescue processes. In lieu of tests on these glaring conflicts in inbreeding effects, conservation programs for endangered species must assume that maximum outbreeding promotes the genetic health of a population. This fundamental assumption influences irreversible policy decisions ranging from the design of migration corridors to the point of recommending the hybridization of different species. In general, the pervasive threat of inbreeding depression is well documented, but how natural or captive populations avoid or escape extinction trajectories remains unclear. I will give an overview of three large-scale experiments designed to investigate the intricacies of inbreeding and the escape from extinction, using the common housefly as a model organism. This work is critical for advancing evolutionary theory on inbreeding depression and population networks, with practical applications for agriculture and conservation biology.

2:00PM-5:00PM AFTERNOON BREAKOUT SESSIONS

*CHRISTINA STOICA - Social Systems

*DWIGHT READ - Change in the Form of Evolution: Transition from Primate to Hominid Forms of Social Organization
Author(s):
Dwight Read, UCLA, USA
Abstract:
The evolution of our species, Homo sapiens, involved a change from biologically based forms of social organization whose forms are the outcome of Darwinian evolution in its various modalities (individual fitness, inclusive fitness, kin selection, mate selection, reciprocal altruism, etc.) to social organization based on (culturally) constructed relations whose form is driven by a new kind of evolution decoupled from Darwinian evolution. Key to understanding this transition are the implications and consequences of increasing individuation among group members for the coherency of group formation. In this paper I will consider, on the one hand, the primate evidence for social organization becoming more problematic as phylogenetically correlated individuation becomes more pronounced and, on the other hand, a radically new form of social organization arising out of an elaboration of cognitive capacities that enabled the formation of conceptual relations among group members that, in turn, provided the basis for societal organization being constituted from “the set of all roles, including their relations, that can be taken by members of the according population” (Kluever, Juergen. 2002. An Essay Concerning Sociocultural Evolution. Kluwer Academic Publishers, p. 47)
*ROBERT G. REYNOLDS - The Role of Culture in the Emergence of Decision-Making Roles: An Example Using Cultural Algorithms
Author(s):
Robert G. Reynolds, Wayne State University, USA
Bin Peng, Wayne State University
Abstract:
In this approach we use Cultural Algorithms as a vehicle to explore the emergence of decision-making roles among an initially homogeneous population of agents. In the Cultural Algorithm we have a population of agents that can use one of several cultural knowledge sources in the Cultural Belief Space each time step to guide their search for the point of optimum yield in a two dimensional real-valued landscape. During the simulation groups of decision-makers with different roles emerge consistently from run to run. We describe the different roles and try to motivate why this emergence has taken place.
*CLAUDIO CIOFFI-REVILLA - A Canonical Theory of Origins and Development of Social Complexity
Author(s):
Claudio Cioffi-Revilla, George Mason University, USA
Abstract:
The puzzle of origins of government and social complexity in human and social dynamics -- arguably a characteristic feature of the emergence and long-term evolution of hierarchy and power in the history of civilizations -- has been an enduring topic that has challenged political scientists, anthropological archaeologists, and other social scientists and historians. This paper presents a new computational theory for the emergence of social complexity that accounts for the earliest formation of systems of government (pristine polities) in prehistory and early antiquity. The theory is based on a “fast process” of crisis and opportunistic decision-making through collective action. This core iterative process is “canonical”, in the sense of undergoing variations on a recurring theme of problem-solving, adaptation and occasional failure. When a group is successful in managing or overcoming serious situational changes (endogenous or exogenous to the group, social or physical) a probabilistic phase transition may occur, under a well-specified set of conditions, yielding a long-term (”slow”) process of emergent political complexity and development. A reverse process may account for decay. Formally, the canonical theory is being implemented through the ”PoliGen” agent-based model (ABM), based on the new Multi-Agent Simulator of Networks and Neighborhoods (MASON). MASON is a Java-based simulation environment (akin to Swarm or RePast) developed by George Mason University’s Evolutionary Computation Lab in collaboration with the Center for Social Complexity (CSC). Empirically, the theory is testable with the datasets on polities developed by the Long-Range Analysis of War (LORANOW) Project. This paper focuses on the theoretical concepts, social mechanisms, and basic formal structure underlying the simulation model.
*JUERGEN KLUEVER - The emergence of social order by the communicative generation of social types
Author(s):
Juergen Kluever, University of Duisburg-Essen, Germany
Abstract:
According to the classical study of Berger and Luckmann "The social construction of reality" social interactions are determined by the mutual construction of "types", i.e., classification of the respective other by applying social and personal categories. In the lecture I shall argue that these classifications can be modeled as the construction of certain vectors that consist of several components of social categories and other components of personal categories. The number of social categories is determined by the general structuration of society. The forming of such vector leads – in the actor – to certain rules of actions. Such rules are tested and stabilized in the process of communicative interactions. The respective importance of social and personal categories depends on the general structure of the society and vice versa, i.e., the structure of a certain society is the result of the particular vectors with different importance with respect to the social and the personal categories. The emergence of certain forms of social order by this communicative process is modeled and analyzed with a computational model. It can be shown that different relations between social and personal categories lead to significantly different forms of social structure.

Networks

*RICH COLBAUGH - ANALYSIS OF COMPLEX NETWORKS USING LIMITED INFORMATION
Author(s):
Rich Colbaugh, Department of Defense, New Mexico Institute of Mining and Technology, USA
Kristin Glass, National Center for Genome Resources, USA
Mauro Trabatti, National Center for Genome Resources, USA
Geert Wenes, National Center for Genome Resources, USA
Abstract:
Complex networks have attracted considerable attention in the scientific community, and in popular culture, in recent years. This interest is motivated in part because complex networks provide a natural framework for studying a wide range of systems in nature and society, and in part because the behavior of these networks is often quite surprising. For instance, it is by now well-known that many complex networks exhibit a “robust, yet fragile” character, so that small perturbations to otherwise reliable systems can lead to catastrophic failure; this phenomenon manifests itself as diversely as cascading failures in electric power grids and other infrastructure networks, crashes in financial markets, mass extinctions, and fads and social movements. Understanding this property of complex networks is clearly of great scientific and practical interest, and various analysis frameworks have been proposed to explain it. Complex networks have an additional property, much less studied but extremely important: despite their complexity, it is often possible to extract deep, quantitative information about them using only limited observations of their behavior. As an example of the basic idea, consider the problem of obtaining an understanding of the behavior of a large organization through the study of communication patterns of its members. Remarkably, studying only (the time series of) who contacts whom, without regard to the content of the communications, can provide deep insight into the objectives, strategies, activities, and efficacy of the organization, its subgroups, and even individual agents. While this example is only a sample of the broad class of analysis problems of interest to us, it possesses the main characteristics of these problems: the network is large, dynamic, and complex, the information sought is semantically rich and quantitative, it is desired to extract this information efficiently and accurately, and the available data provide only a limited view of the system. It is, of course, fair to ask whether it is really possible to learn much about the behavior of complex networks from only limited observations, i.e., whether the claimed “deep information from limited data” property is indeed possessed by a broad and important class of (complex) networks. The main contributions of this paper are to rigorously establish the existence of this property, to show that it is, in fact, the dual of the robust, yet fragile property, to develop a systematic methodology for exploiting it, and to demonstrate the power of the proposed information extraction process through implementation with “real world” problems. A key step in the development is the introduction of a new class of systems, termed complex additive systems (CAS), which result from incremental evolution of system configuration driven by (myopic) response to failures and adoption of innovations. It is shown that the CAS framework provides a natural environment within which to model a wide range of networks in nature and society. Moreover, we establish that CAS evolution leads to networks with considerable structure and important properties, including the robust, yet fragile and deep information from limited data properties, and propose a systematic, efficient process for exploiting the properties to extract information from CAS. An interesting aspect of the development is the demonstration that broad classes of CAS admit simple vertex dynamics models, which enable rigorous, yet tractable network analysis. We illustrate the power of this approach by applying it to several “real world” networks in a series of case studies. We begin by showing that it is possible to accurately and robustly identify important agents and collaborating agents in social networks by studying only communication “metadata” (e.g., who sends whom email); the particular application of these results to terrorist and criminal networks is also briefly explored. Next we analyze biological networks, and present algorithms which use only network topology data and basic evolutionary principles to accurately identify “lethal” genes in the gene regulatory network of the yeast S. cerevisiae and functional modules in the metabolic network of the bacterium E. coli. Finally, we consider the problem of understanding the susceptibility of complex networks to cascading failures and predicting their occurrence, and develop a systematic approach to conducting such analysis; the feasibility of the proposed methodology is indicated through a study of electric power grid failures and financial market crashes.
*STEVE KRONE - On the Evolution of Structure in Ecological Networks
Author(s):
Matt Labrum, University of Idaho, USA
Terry Soule, University of Idaho, USA
Aaron Blue, University of Idaho, USA
Stephen Krone, University of Idaho, USA
Abstract:
Previous research on simulated ecological networks has focused on things like the distribution of links between species, without generally categorizing the types of inter-species relationships that develop, unless those relationships are of some predesigned form (e.g., food webs). In this work we use a model system to examine the specific types and numbers of interactions between species and how these affect network stability and evolution. For example, we report on the numbers of predator-prey, competitive, symbiotic, and more complex cyclic relationships that develop in randomly initialized communities, with and without the introduction of novel species over the course of the simulation.
*NISHA MATHIAS - Small Worlds - How and Why
Author(s):
Nisha Mathias, Philips (India), India
Venkatesh Gopal, Northwestern University, USA
Abstract:
We investigate small-world networks from the point of view of their origin. While the characteristics of small-world networks are now fairly well understood, there is as yet no work on what drives the emergence of such a network architecture. In situations such as neural or transportation networks, where a physical distance between the nodes of the network exists, we study whether the small-world topology arises as a consequence of a tradeoff between maximal connectivity and minimal wiring. Using simulated annealing, we study the properties of a randomly rewired network as the relative tradeoff between wiring and connectivity is varied. When the network seeks to minimize wiring, a regular graph results. At the other extreme, when connectivity is maximized, a near random network is obtained. In the intermediate regime, a small-world network is formed. However, unlike the model of Watts and Strogatz (Nature {\bf 393}, 440 (1998)), we find an alternate route to small-world behaviour through the formation of hubs, small clusters where one vertex is connected to a large number of neighbours
*YING-CHENG LAI - Synchronization in complex networks
Author(s):
Ying-Cheng Lai, Arizona State University, USA
Takashi Nishikawa, Southern Methodist University, USA
Adilson E. Motter, Max-Planck Institute for Physics of Complex Systems, Germany
Frank C. Hoppensteadt, New York University, USA
Abstract:
Small-world networks are known to be more easily synchronized than regular lattices, which is usually attributed to the smaller network distance between oscillators. We recently discovered that networks with a homogeneous distribution of connectivity are more synchronizable than heterogeneous ones (e.g., scale-free networks), even though the average network distance is larger. Some degree of homogeneity is then expected in naturally evolved structures, such as neural networks, where synchronizability is desirable.
LJUPCO KOCAREV - Synchronization in complex network topologies
Author(s):
Ljupco Kocarev, University of California San Diego, USA
Abstract:
The study of complex systems pervades all of science, from cell biology to ecology, from computer science to meteorology. A paradigm of a complex system is a network where complexity may come from different sources: topological structure, network evolution, connection and node diversity, and/or dynamical evolution. Examples of networks include food webs, electrical power grids, cellular and metabolic networks, the World-Wide Web, the Internet backbone, neural networks, and co-authorship and citation networks of scientists. These networks consist of nodes which are interconnected by a mesh of links. The macroscopic behavior of a network is determined by both the dynamical rules governing the nodes and the flow occurring along the links. In this paper, we study synchronization properties of networks with regular, random and power-low topologies. We show that random networks are synchronizable.
*VALENTIN ZHIGULIN - Dynamical Motifs: Building Blocks of Complex Network Dynamics
Author(s):
Valentin Zhigulin, California Institute of Technology, USA
Abstract:
Spatio-temporal network dynamics is an emergent property of many complex systems which remains poorly understood. We suggest a new approach to its study based on the analysis of dynamical motifs - small subnetworks with periodic and chaotic dynamics. We simulate randomly connected neural networks and, with increasing density of connections, observe the transition from quiescence to periodic and chaotic dynamics. We explain this transition by the appearance of dynamical motifs in the structure of these networks. We also observe domination of periodic dynamics in simulations of spatially distributed networks with local connectivity and explain it by absence of chaotic and presence of periodic motifs in their structure.
*ROBERT PRILL - Modeling Network Motifs as Linear Dynamical Systems
Author(s):
Robert Prill, Johns Hopkins University, USA
Andre Levchenko, Johns Hopkins University, USA
Abstract:
The dynamic behavior of a system of interacting components is determined by the characteristics of the individual components, as well as the way in which they interact. From the system designer's point of view, there are two "handles" for adjusting dynamics: component parameters and connectivity. The idea that connectivity influences the range of possible behaviors of a system is a natural extension of the structure-function relation that is pervasive in biology. A certain network structure may favor particular dynamic behaviors, while precluding other behaviors. We present a method for characterizing possible dynamic behaviors of a system of interacting components when there is only information about connectivity. We model network motifs as linear systems, sampling the model parameters with random values. Using this Monte Carlo approach, we characterize a given connectivity in terms stability and oscillation. We find that certain motifs are inherently stable in the face of variation of parameter values. Other motifs are inherently oscillatory. These findings suggest a functional explanation for the observation that certain connectivities are frequently observed in real networks. A prevalence of stable sub-structures seems plausible. Among the most stable 3-node and 4-node motifs in our simulations are the feed-forward loop, the bi-fan, and the bi-parallel, identified by Milo, et al. in transcriptional networks, neurons, food webs, electronic circuits, and the World Wide Web (1). Additionally, linear chains, single-input, and multi-input motifs were identified by Lee, et al. in the S. cerevisiae transcriptional network (2). These motifs share a common structural feature: feedbacks are absent. Modeled as a linear system, motifs lacking feedbacks exhibit stable, non-oscillatory behavior. The most oscillatory 3-node motif in our simulations is the simple loop. In addition to the corresponding 4-node loop, we identified thirteen additional 4-nodestructures that are unstable oscillators, ten of which contained one or more feed-forward loops as a subcomponent of the 4-node structure. When the feed-forward loop is considered as an autonomous unit, it behaves a stable non-oscillator. Connected to a fourth node, no longer technically a FFL in many cases, the new motif may exhibit drastically different behavior. Of course, there are ways of extending the feed-forward loop such that the original behavior is preserved. Thus, the dynamic behavior of this motif depends on the context in which it is found and where the boundary of the motif is drawn. The question arises whether 3 and 4-node motifs are self contained functional units, or parts of larger functional structure. This modeling exercise suggests that consideration of motifs as autonomous functional units can lead to certain conclusions about their behavior. When considered part of a larger system, even if that new system is achieved by the addition of a single node, the original behavior may no longer be plausible. REFERENCES 1. R. Milo, et al. Network Motifs: Simple Building Blocks of Complex Networks. Science 298, 824 (2002). 2. T. Lee, et al. Transcriptional Regulatory Networks in Saccharomyces cerevisiae. Science 298, 799 (2002). 3. S. Mangan and U. Alon. Structure and function of the feed-forward loop network motif. PNAS 100. 11980-11985 (2003)
*GYORGY KORNISS - Extreme Fluctuations in Small-Worlds with Relaxational Dynamics and Applications to Scalable Parallel Computing
Author(s):
Gyorgy Korniss, Rensselaer Polytechnic Institute, USA
Hasan Guclu, Rensselaer Polytechnic Institute, USA
Abstract:
Synchronization is a fundamental problem in natural and artificial coupled multi-component systems. We investigate to what extent small-world couplings (extending the original local relaxational dynamics through the random links) lead to the suppression of the extreme fluctuations in noisy small-world-coupled systems. We use the framework of non-equilibrium surface growth to study and characterize the degree of synchronization in the system. In the absence of the random links, the surface in the steady state is ``rough'' (strongly de-synchronized state) and the average and the extreme height fluctuations diverge in the same power-law fashion with the system size (number of nodes). With small-world links present, the average size of the fluctuations becomes finite (synchronized state) [1,2] and the extreme heights diverge only logarithmically in the large system-size limit [3]. This latter property ensures synchronization in a practical sense in coupled multi-component autonomous systems with short-tailed noise and effective relaxation through the links. The statistics of the extreme heights is governed by the Fisher-Tippett-Gumbel distribution [3]. We illustrate our findings through an actual synchronization problem in the context of scalable parallel computing [1]. [1] G. Korniss, M.A. Novotny, H. Guclu, Z. Toroczkai, and P.A. Rikvold, ``Suppressing Roughness of Virtual Times in Parallel Discrete-Event Simulations'', Science 299, 677--679 (2003). [2] B. Kozma, M.B. Hastings, and G. Korniss, ``Roughness Scaling for Edwards-Wilkinson Relaxation in Small-World Networks'', Phys. Rev. Lett. 92, 108701 (2004). [3] H. Guclu and G. Korniss, ``Extreme Fluctuations in Small-Worlds with Relaxational Dynamics'', arXiv:cond-mat/0311575 (2003).

*GUY HOELZER - Evolution and Ecology

*JUSTIN WERFEL - The evolution of reproductive restraint through social communication
Author(s):
Justin Werfel, Massachusetts Institute of Technology, USA
Yaneer Bar-Yam, New England Complex Systems Institute, USA
Abstract:
The debate about group selection has traditionally focused on individual reproductive restraint. We demonstrate the evolution of conditional reproductive restraint based on an explicitly social mechanism, modulated by intra-population communication comprising signal and evolved response, in a spatially distributed predatory/parasitic/pathogenic model system. The predatory species consistently comes to exploit a signal implying overcrowding, individuals constraining their reproduction in response, with a corresponding increase in equilibrium reproduction rate in the absence of signal. This signaled restraint arises in a robust way for a range of model spatial systems; it outcompetes non-signal-based restraint, and is not vulnerable to subversion by non-cooperating variants. These results demonstrate a social system where communication is used to evaluate population density and regulate reproduction accordingly, consistent with central ideas of Wynne-Edwards, whose claims about the evolutionary importance of group selection helped ignite decades of controversy. Further, the simulations support the hypothesis that intercellular communication, and ultimately multicellularity, may have originated via the co-opting of an unrelated metabolite as a signal carrier. This quantitative simulational model accounts for a key evolutionary transition, the advent of cooperation through communication, relevant to intercellular communication as well as to organismal social organization.
*ERIK RAUCH - Diversity is unevenly distributed within species
Author(s):
Erik Rauch, NECSI, MIT, USA
Yaneer Bar-Yam, NECSI, USA
Abstract:
Global efforts to conserve species have been strongly influenced by the heterogeneous distribution of species diversity across the Earth. This is manifest in conservation efforts focused on diversity hotspots. The conservation of genetic diversity _within_ an individual species is also an important factor in its survival in the face of environmental changes and disease. Here we show that diversity within species is also distributed unevenly. Using simple genealogical models, we show that genetic distinctiveness has a power-law distribution. This property implies that a disproportionate fraction of the diversity is concentrated in small sub-populations. Small groups are of such importance to overall population diversity that even without extrinsic perturbations, there are large fluctuations in diversity due to extinctions of these small groups. We also show that diversity can be geographically non-uniform, potentially including sharp boundaries between distantly related organisms, without extrinsic causes such as barriers to gene flow or past migration events. Our theoretical results agree with experimental results on the distribution of diversity in global samples of Pseudomonas bacteria. The results suggest that conservation efforts that target specific highly unique groups and ensuring their continued reproduction can save much of the diversity, even after a large population loss.
*JOSH MITTELDORF - Selection in Ecosystems
Author(s):
Josh Mitteldorf, LaSalle University, USA
John W. Pepper, Univ Arizona, USA
Abstract:
The notion that natural selection acts above all on individual reproductive capacity has been a mainstay of evolutionary theory since the origins of population genetics nearly a century ago. However, this assumption has rarely been subject to field tests, and in those few instances where direct, quantitative tests have been undertaken, the results suggests that nature does not always maximize individual reproductive capacity. Four broad phenomena of the biosphere have persisted in inspiring controversy because they seem to require higher levels of selection than can be justified by traditional models. These are: the ubiquity of sexual reproductive, despite a twofold disadvantage (by some counts) in r. The persistence of high levels of genetic diversity in wild populations mocks the theoretical fiat that all such diversity much be selectively neutral. Senescence is maintained as a near universal characteristic of the eukaryotic genome, despite its negative contribution to individual fitness. Evidence of reproductive restraint and "prudent predation" is widely accepted by field ecologists, but mocked as nonsense by evolutionary theorists. We propose that evolutionary dynamics of ecosystems can resolve these dilemmas. We present a simple model of ecosystem evolution that shows promise at resolving each of these paradoxes. The model tracks individuals of 4 species in a toy ecosystem, co-evolving on a viscous grid. In preliminary model results, we find that (1) sexual reproduction is maintained, (2) diversity does not collapse, (3) senescence is selected as an adaptation, and (4) neither predation nor reproductive potential are maximized. A key to understanding the model's behavior is the local interdependence of species, which supports the efficient punishment of any population that expands at the expense of the ecosystem.
*KEI TOKITA - Diversity dynamics in large complex biological networks
Author(s):
Kei Tokita, Harvard University/Osaka Unversity, USA
Tsuyoshi Chawanya, Osaka University, Japan
Abstract:
One of the goals of ecology is to find a mechanism to stabilize large-scale complex ecological systems with many species interacting each other, universally observed on the earth, e.g. in tropical forrests and coral reefs. Field ecologists in 1960's suggested that complex ecosysmtems were stable because of their complexity itself. This view was challenged by theoretical studies on simple mathematical models in 1970's and the problem of the complexity and stability has been considered a paradox. Theoretical studies so far have supported that high diversity is never maintained and ecosystems with small number of species is barely survived if the interaction matrix is randomly asymmetric (no correlation between the matrix elements) or symmetric (mutualistic or competitive) in the limit of strong complexity. Here we show that half of initial diversity is maintained if the interaction matrix is skew-symmetric (prey-predator relationships). It is revealed that the survived subsystem is characterised by its hierarchically ordered interactions, e.g. pyramidal food web. The present study on the problem of the diversity and stability is not connected only to ecology but also to polymorphisms at major histocompatibility complex, large-scale metabolic networks and initial evolution of life.
*MADHUR ANAND - Quantification of Biocomplexity
Author(s):
Madhur Anand, Laurentian University, Canada
Abstract:
The problem of quantifying diversity has not been resolved to the satisfaction of most ecologists and thus merits investigation. I have been working to refine our definition of diversity to include components such as spatial structure and taxonomic diversity. We refer to the proposed set of measures as ‘biocomplexity’ measures, and have found that they can be more useful than previous measures in assessing ecosystem health, when applied to recovering pollution-impacted forest communities. I discuss how to find surrogates of biocomplexity using not only taxonomic and compositional information of ecological communities, but relationships between them. I will also examine the ability of power-law scaling behaviour (a hallmark of self-organization via the construction of fractal-like structures), and deviations from theoretical values of these power-laws, to detect ecological perturbation. Preliminary results suggest that disturbance destroys self-organizing network-like structures of ecosystems and that restoration efforts must attempt to rebuild these structures. If this is true, we will show, for the first time, that disturbance can be detected by deviations from power-law behaviour, something that has been suggested, but never shown before. Future work in this area will include determination of the universality class for disturbed ecosystems, which will aid in the assessment of ecosystem resilience.
*DANIEL S. FISHER - The rate of evolution: Is anything understood?
Author(s):
Daniel S. Fisher, Harvard University, USA
Abstract:
Framing good quantitative questions about aspects of evolution that involve multiple genetic changes is a major challenge. Answering them is not easier. Various issues associated with one of the most fundamental problems --- the rate of evolution -- will be discussed emphasizing the current state of understanding, key sets of questions, and potential for progress. Starting with problems relevant for laboratory experiments, this will range up to simple models for evolution on long time scales from which useful scaling results and understanding might be gleaned.

2:00PM-5:00PM AFTERNOON EXTENDED TALK SESSIONS

*LEN TRONCALE - Systems Biology

*BENJAMIN J DUBIN-THALER - Cell Motility: How Cellular Machines Generate Precise Responses in a Complex Environment
Author(s):
Benjamin J Dubin-Thaler, Columbia University, USA
Hans-Gunther Dobereiner, Columbia University, USA
Gregory Giannone, Columbia University, USA
Michael P. Sheetz, Columbia University, USA
Abstract:
Cell motility is a critical biological process. Axons of developing neurons crawl centimeters to make synapses in the brain. Fibroblast cells migrate and generate forces in order to heal wounds. Immune cells crawl through blood vessels to the site of infection. Regulation of cytoskeleton, molecular motors and extracellular adhesions by a protein mediated signaling network controls these processes. Our studies show that non-linear response to stimuli, periodicities and phase transitions are inherent to this network. Cells initiate attachment to a surface in an all-or-nothing manner, demonstrating a thresholded response to external chemical signals. Following initiation, spreading proceeds by one of two distinct mechanisms: isotropically, increasing its area in all directions at once or anisotropically, moving outward one step at a time. Following spreading, cells initiate a series of periodic membrane contractions creating a force-dependent positive feedback loop used to direct further motility. Fourier power spectra of membrane velocity and the scaling behavior of area growth both indicate dynamic phase transitions between these different cell states. These behaviors developed in the cellular machine to provide precise responses based on a wide variety of environmental inputs.
*KEITH AMONLIRDVIMAN - Mathematical modeling of planar cell polarity to understand domineering non-autonomy
Author(s):
Keith Amonlirdviman, Stanford University, USA
Narmada A. Khare, Stanford University, USA
David R.P. Tree, Stanford University, USA
Jeffrey D. Axelrod, Stanford University, USA
Claire J. Tomlin, Stanford University, USA
Abstract:
As the understanding of cellular regulatory networks grows, system dynamics and behaviors resulting from feedback effects of such systems have proven to be sufficiently complex as to prevent intuitive understanding. Often, only incomplete abstracted hypotheses exist to explain observed complex patterning and functions. The challenge has become to show that enough of a network has been understood to explain the behavior of the network. For example, only an incomplete understanding exists for the mechanisms that generate sub-cellular asymmetry along a tissue axis orthogonal to the apical-basal axis, termed planar cell polarity (PCP). Cell clones mutant for some PCP signaling components cause polarity disruptions of surrounding, wild-type cells, a phenomenon referred to as domineering non-autonomy. We propose that a contact dependent signaling hypothesis, derived from experimental results, is sufficient to explain domineering non-autonomy. However, intuition alone is insufficient to deduce that this model, which relies on a local feedback loop acting at the cell membrane, underlies the complex patterns observed in large fields of cells containing mutant clones, and others have argued that it cannot account for observed phenotypes. Here, we show, through reaction-diffusion, partial differential equation modeling and simulation, that the contact dependent signaling hypothesis can fully reproduce PCP phenotypes, including domineering non-autonomy, in the Drosophila wing. The sufficiency of this model argues that previously proposed diffusible factors need not be invoked to explain PCP signaling.
*CRAIG VAN HORNE - The Basal Ganglia as a Complex system as it Relates to Normal Movement and Movement Disorders.
Author(s):
Craig van Horne, Harvard Medical School, Brigham and Women's Hospital, USA
Abstract:
The purpose of this work is to explore a part of the brain known as the basal ganglia using the conceptual approach of complex system dynamics as it relates to both structure and function. Traditional approaches have modeled the basal ganglia as a simple circuit utilizing methods of reductionism to study the parts and connections. Studies of complex systems, on the other hand, focus not only on behavior of the individual parts but how they interact with each other to produce the behavior of the system as a whole. From this point of view, the basal ganglia can be described as a highly interconnected group of nuclei that integrates many inputs that function to allow the performance of purposeful movement. Our exploration will outline the components of the basal ganglia, the interactions between the components, the collective behavior as it relates to normal function, and the relationship between the basal ganglia system and its environment within the brain. In addition, changes in the states of the system will be described as they relate to both disease states, such as Parkinson’s disease, and therapeutic strategies, such as deep brain stimulation. Increasing our understanding of the basal ganglia as a complex system, rather than as a simple circuit, may provide greater opportunity to develop better treatments for movement disorders.
*MARKUS BREDE - Random Evolution of Idiotypic Networks: Dynamics and Architecture
Author(s):
Markus Brede, CSIRO, Australia
Ulrich Behn, University of Leipzig, Germany
Abstract:
The talk deals with modelling a subsystem of the immune system, the so-called idiotypic network. Idiotypic networks, a concept conceived by N.K. Jerne in 1974, are functional networks of interacting antibodies and B-cells. In principle, Jernes' framework provides solutions to many issues in immunology, such as immunological memory, mechanisms for antigen recognition and the question of self/non-self discrimination. Explaining the interconnection between the elementary components' local dynamics, network formation and architecture, and possible modes of global system function appears to be an ideal playground of statistical mechanics. We present a simple cellular automaton model based on a graph representation of the system. From a simplified description of idiotypic interactions rules for the random evolution of networks of occupied and empty sites on these graphs are derived. In certain biologically relevant parameter regimes the resultant dynamics lead to stationary states. A stationary state is found to correspond to a typical pattern of network organization. It turns out that even these very simple rules give rise to a multitude of different kinds of patterns. In the talk methods are presented to characterize such stationary state networks. Based on this description, `static' and `dynamic' network-patterns are distinguished. The observed types of stationary state networks are related to possible operational modes of real idiotypic networks. A type of `dynamic' network is found that displays many features of real idiotypic networks and could explain transitions in the network structure if changes in essential parameters occur, e.g., the influx of new idiotypes from bone-marrow.
*ERIC MJOLSNESS - Network Dynamics for Systems Biology
Author(s):
Eric Mjolsness, University of California, Irvine, USA
Abstract:
Networks in biological systems form a complex graph structure in which dynamics is local. This applies to the dynamics of both graph node variables, such as concentrations, and graph link variables, such as evolving or cell-state-specific regulatory relationships. Unlike most models of physical systems, the forms of the equations commonly used to model cellular network dynamics are very diverse. This is due to diversity both in biological mechanisms, e.g. those associated with membership in metabolic, signaling, transcriptional or mechanical networks, and in the common modeling approaches to each mechanism. Plausible models for these networks can be classified according to (a) their network structure and (b) their choice of node dynamics for each major biological mechanism. Such a taxonomy can be important for example in representing signal transduction networks involving the interaction of protein complex and transcriptional regulation networks.
*JENNIFER HALLINAN - Tunable Asynchrony in an Artificial Genome Model of a Genetic Regulatory Network
Author(s):
Jennifer Hallinan, The University of Queensland, Australia
Abstract:
Boolean models of genetic regulatory networks (GRNs) have been shown to exhibit many of the characteristic dynamics of real GRNs, with gene expression patterns settling to point attractors or limit cycles, or displaying chaotic behaviour, depending upon the connectivity of the network and the relative proportions of excitatory and inhibitory interactions. This range of behaviours is only apparent, however, when the nodes of the GRN are updated synchronously, a biologically implausible state of affairs. In this paper we demonstrate that evolution can produce GRNs with interesting dynamics under an asynchronous update scheme. We use an Artificial Genome to generate networks which exhibit limit cycle dynamics when updated synchronously, and describe the effect of implementing a parameterized form of asynchronous updating upon the dynamics of the network.
*BEN GOERTZEL - Integrative Artificial Intelligence as a Key Ingredient of Systems Biology
Author(s):
Ben Goertzel, Biomind LLC, USA
Abstract:
A full understanding of biological complex systems will only be achieved by human scientists working in collaboration with powerful AI software that can cognitively integrate the very broad range of biological data currently available. The Biomind AI Engine is a unique software system inspired by this vision. It aims to providing automated analysis of diverse biological data, and automated inference based on diverse biological information. Utilizing the Novamente "artificial general intelligence" framework, it integrates probabilistic reasoning with supervised learning (evolutionary programming, support vector machines), unsupervised learning (evolutionary programming, clustering) and automated text understanding. One distinctive aspect of the Biomind framework, in a data analysis context, is that "background knowledge" from biological databases (some of it drawn directly from the databases, some inferred from the databases using probabilistic inference) is used in the course of analyzing each particular experimental dataset. Currently the Biomind AI Engine is embodied in the commercial Biomind Analyzer product for gene expression data analysis, and is also being used by the author and his colleagues for broader-ranging research in bioinformatics and systems biology. Applications to date, conducted in partnership with several biological research teams, have involved inferring novel diagnostic rules for major diseases based on analysis of gene expression data and data regarding rare genetic mutations. The software has also been applied to the automated inference of genetic regulatory patterns from gene expression time series data; and to the inference of likely functions for as-yet unstudied genes. Current research focuses on automated inference of signal transduction pathways and other subtle patterns in genetic and proteomic systems.
*TAESIK LEE - A FUNCTION-BASED Approach to Systems Biology
Author(s):
Taesik Lee, Massachusetts Institute of Technology, USA
Jeffrey D. Thomas, Massachusetts Institute of Technology, USA
Nam P. Suh, Massachusetts Institute of Technology, USA
Abstract:
Systems biology research is currently dominated by multidisciplinary approaches that attempt to integrate the interactions of biological entities through modeling to create an understanding of system-level functions. Although important, these approaches cannot relate the higher-level functions to the behavior of lower-level molecular interactions. We describe here the use of the Axiomatic Design approach and a complexity theory to system modeling and illustrate their utility in the study of biological systems. Axiomatic Design relates functions at all levels to the behavior of biological molecules and uses a Design Matrix to understand these relationships. Such a modeling and analysis reveals that robustness in many biological systems is achieved through the maintenance of functional independence of numerous subsystems. When the interlinking (coupling) of systems is required, biological systems impose a functional period in order to maximize successful operation of the system, which reduces the complexity of the biological system. Ultimately, the application of Axiomatic Design methods and the complexity theory to the study of biological systems will allow the establishment of the biological functions to molecular level across many scales, identifying control points, and predicting system-wide effects of pharmacological agents.
*CHEOL-MIN GHIM - Assessing lethality in the genome-scale metabolic network of Escherichia coli
Author(s):
Cheol-Min GHIM, Seoul National University, South Korea
Abstract:
As a first step beyond the pathway analysis, we study the biochemical reaction network occurring in the genome-scale metabolic networks of Escherichia coli. To this end, we construct a directed bipartite graph by mapping metabolite and their associated reactions into alternating vertices. Applying various measures of centrality and introducing the concept of synthetic lethality, we identify lethal reactions involved in the whole-cell metabolism.

*WILLIAM SULIS - Concepts, Formalisms, Methods and Tools

*ABHIJNAN REJ - Multiscale Coordination and Dynamical Similarity
Author(s):
Abhijnan Rej, University of Connecticut, USA
Abstract:
Multiscale coordination can be defined to be the study of how complex biological systems explicitly utilize layers of physiological organization in the performance of a given task. In the recent years, a number of models have been proposed (by Frank, Daffertshoffer, et.al.) that address the issue of multiscale coordination from the perspective of non-extensive statistical mechanics and coupled oscillator models. The key pegs in their approach is morphological reductionism (the neural layer is seen to be more “privileged” than the effector layer) that leads to two very hard problems: first, coupled oscillator models seem to work only with external fine-tuning of free parameters, and second, the system/subsystem distinction is taken to be ontological. In this Paper, I, following Rosen’s work on representation and measurement of natural systems, present a new framework of approaching multiscale coordination that is non-reductionistic and one that does not seem to need external free-parameter fine-tuning. The abstract framework utilizes the notion of topological conjugacy (Rosen’s dynamical similarity principle) between models of different levels of organization. This means that the order parameter at any level obeys a dynamical model that is similar to the model that an order parameter of any other level satisfies. Thereby, formal similarity is postulated to be the key principle in understanding multiscale phenomena. This leads to some very novel implications for understanding complex systems in general from the foundational standpoint. Firstly, the system/subsystem distinction becomes, logically, impredicative and self-reflexive. Secondly and consequently, the new framework is observer dependent. Similar ideas (from a computational mechanical viewpoint) have been proposed by Crutchfield. Baas and Emmeche’s theory of “logical loops” in complex systems are also shown to lead to similar conclusions. I systematically review the foundational problems with existing models of multiscale coordination and, then, present, the new framework. I conclude by suggesting experimental tests that could confirm or refute the same.
*ROBERT CLEWLEY - Dominant-scale analysis for automatic reduction of high-dimensional ODE systems
Author(s):
Robert Clewley, Boston University, USA
Nancy Kopell, Boston University, USA
Abstract:
Systems of ordinary differential equations arise commonly as models in the natural sciences, often with multiple time-scales. State-dependent coupling can add to the complexity, introducing new time-scales. These time-scales may not be explicit in the equations. We have developed a computational technique that can be used to reduce the study of such networks evolving near a known trajectory, to a set of low-dimensional approximate models. The dominant-scale technique adds rigor to intuitive reduction techniques that are ubiquitous in modeling high-dimensional coupled systems, and is different to a center manifold reduction. In particular, it quantifies the robustness and parametric dependence of coherent temporal activity along the entire length of a known trajectory. It also provides a quantitative basis for rigorously defining intuitive concepts such as "emergent structure", "evolving sub-systems", etc., in spatially-extended physical models. We demonstrate our analysis software on an example network of Hodgkin-Huxley equations for biological membrane excitability.
*NED J. CORRON - Information Flow in Synchronization
Author(s):
Ned J. Corron, U. S. Army RDECOM, USA
Shawn D. Pethel, U. S. Army RDECOM, USA
Abstract:
Chaos synchronization is a well known mechanism that creates structure in complex dynamical systems, yet many issues remain concerning its limits and robustness. Importantly, chaotic oscillations are characterized by positive entropy; thus, synchronized oscillators must share a common information source. For unidirectional coupling, information generated by the drive must be encoded and transmitted through a coupling channel to a receiver, where it is decoded and used to entrain the response. We use information theory to explore this communication problem. A symbolic description for the oscillator dynamics is used to identify and tag the emerging information that must be communicated to maintain synchronization, and we show there is a minimum channel capacity that is necessary and sufficient to maintain synchronization to any precision. We also explore so-called "achronal" synchronization, in which the response lags or leads the drive by a fixed amount of time. We find fundamental tradeoffs between the precision to which the drive is detected, the quality of synchronization, and the delay or anticipation exhibited by the response. To illustrate these tradeoffs, we present experimental results using electronic circuits.
*DMITRY NERUKH - Statistical complexity of protein folding: application of computational mechanics to molecular dynamics
Author(s):
Dmitry Nerukh, Cambridge University, UK
George Karvounis, Cambridge University, UK
Robert C. Glen, Cambridge University, UK
Abstract:
Complex behaviour can emerge from inherently simple systems; a well-known and vitally important example is the ability of many proteins to spontaneously fold into a consistent and reproducible three-dimensional shape fundamental to their biological function. In a poorly understood manner the sequence of amino acids that make up the protein as well as their interactions with solvent encodes a highly reproducible folding process. Our approach consists of considering the protein as a dynamic, self-organizing system that exhibits an emergent behaviour. To understand the fundamental basis of the folding process we developed a methodology for the quantitative estimation of the dynamic complexity of an MD simulated peptide in explicit water. For our purposes we have adopted the approach by Crutchfield et. al. termed “computational mechanics” [1]. This approach combines and implements the ideas from Shannon entropy and Kolmogorov-Chaitin algorithmic complexity theories. It describes the system in terms of “symbolic dynamics”: a sequence of symbols from an “alphabet” of finite size. A symbolic sequence is used to reconstruct an algorithmic automaton that propagates the system from one state (the so called “causal state”) to the next one. “Computational” signifies that the complexity of the system is equal to the complexity of this automaton. Being well developed from the formal mathematical point of view this approach provides a practical algorithm for calculating the complexity of real systems. One of the advantages of this approach is that it is based on an informatic-theoretical analysis of the dynamical evolution of the system and opens up the possibility of quantifying the emergent behaviour of the system. We have demonstrated that this approach can be applied to low-dimensional projections of a molecular systems trajectory and provides new information about the system’s dynamics. Considerably different complexity of the orientational as well as the translational motion of water molecules in an electrolyte solution at different locations with respect to the ion has been found [2]. Additionally, a zwitterion which is made up of two oppositely charged groups separated by an aliphatic chain in vacuum has a specific “loop”-like conformation that the molecule, if allowed to dynamically evolve, takes regardless of the initial configuration. This system, being simple, nevertheless demonstrates a “folding” behaviour and elements of self-organization. Precisely at the moment of “folding” the complexity of individual atom three-dimensional trajectories shows a considerable drop, and then rises to a higher level when the molecule stabilizes in the “folded” conformation. We have now turned our attention to the whole 2N-dimensional trajectory in larger solvated molecular systems, where N is the number of atoms. Obviously, a straightforward calculation of the statistical complexity of this prohibitively high-dimensional signal (e.g. a protein in water) is impossible. However, the local character of interactions in molecular systems allows the calculation of the statistical complexity of the whole system by virtue of the copulas formalism. We have demonstrated that it is sufficient to estimate the local dynamics of small subsets of directly interacting degrees of freedom of the system to reconstruct (without any approximations) the complete 2N-dimensional trajectory properties. The limiting cases of a large number of identical molecules (for example, bulk water) are investigated and their complexity is analysed. For the more complicated case of a peptide in water, it is shown that the useful information is confined within a relatively small subset of atoms, consisting of the protein atoms and their immediate neighbouring water molecules. We have simulated the β-turn formation process in the pentapeptide leu-enkephalin in explicit water and applied computational mechanics analysis to the estimation of complexity of various aspects of the dynamics. A decisively important role of the water network has been recognized and attention has been concentrated on the complexity of water dynamics around the peptide before, at, and after the moment of turn formation. Analysis of various characteristics of the water dynamics support the hypothesis that a simplification of water reorientation takes place in the second solvation shell of the peptide at the moment of the turn formation. 1. J.P. Crutchfield, D.P. Feldman, and C.R. Shalizi, Phys. Rev. E, 62, 2996 (2000) 2. D. Nerukh, G. Karvounis, and R. Glen, J. Chem. Phys., 117(21), 9611-9617 (2002) and J. Chem. Phys., 117(21), 9618-9622 (2002)
*PRITHA DAS - Classification of Indian Songs in Context of Complexity Measure
Author(s):
Pritha Das, BE College, Howrah, India
Abstract:
Gross classification prevailing in Indian songs are like a) Classical b) Semi-classical and c) Light. The categorization is largely from popular perception of music- nothing very rigorous in nature. Classical songs are composed by following grammar of music more rigorously and consequently have frequent changes in frequency. These types of songs are quite difficult to learn and are supposed to be foundation of all sorts of songs. In light music, in contrast, more stress is given to lyrics and is sung more 'smoothly'. Semi-classical songs lie in between. Here, we shall attempt to find if there is any mathematically foundation of this classification. More particularly, the question is whether we can find a measure based on which Indian songs can be classified. We addressed this issue by exploring complexities associated with the songs with the help of nonlinear analysis of the signals that these songs present. This is in line with our previous work on nonlinear analysis of time series. We collected samples of well-known songs of all these categories for analysis ( Das et al., Complexity, 7: 3, 2002). With appropriate processing of the audio clips, we converted them to time series datasets. We applied nonlinear tools to show that classical songs have a larger fractal dimension than light songs while for semi-classical songs, fractal dimensions lie in between the former two. To the best of our knowledge, complex analysis of Indian songs of the nature presented here are being attempted for the first time. Although, these type of analysis have been applied to western classical- in both vocal and instrumental forms. We have reproduced some of the results of those studies. Finally, based on this conclusion we offer an on-line method for classification of Indian songs of unknown category. This will involve the steps that we have described in the paper- but on a more robust scale.
*MICHAEL BAYM - An analytical demonstration of adaptation to the edge of chaos
Author(s):
Michael Baym, Massachusetts Institute of Technology, USA
Alfred Hubler, University of Illinois / Santa Fe Institute, USA
Abstract:
Adaptation to the edge of chaos has long been conjectured in a range of dynamical systems. In this talk I describe the importance of this phenomenon generally and present the first analytic proof of its existence, within the framework of iterated maps.
*STEVEN H. BERTZ - The Complexity of Graphs and Digraphs
Author(s):
Steven H. Bertz, Complexity Study Center, USA
Christina M. Zamfirescu, Department of Computer Science, Hunter College and Graduate Center, CUNY, USA
Abstract:
THE COMPLEXITY OF GRAPHS AND DIGRAPHS Steven H. Bertz and Christina M. Zamfirescu Complexity Study Center, Mendham, NJ 07945 Department of Computer Science, Hunter College and Graduate Center, CUNY, New York, NY 10021 A graph is a collection of points together with a collection of unordered pairs of points, called edges. They can be used to model natural systems such as molecules, neural networks and ecosystems, e.g., in molecular graphs the points represent atoms and the edges represent bonds. In a directed graph or digraph the pairs of points are ordered, and they are called directed edges or arcs. Digraphs can also be used to model many real-world systems, from street maps to plans for sophisticated pharmaceutical syntheses. We have developed complexity measures for graphs and applied them to problems of molecular complexity, e.g., the change in complexity wrought by various chemical reactions. Our approach is to abstract the system under study as a graph or digraph and then characterize its complexity by using invariants, quantities that have the same values for isomorphic graphs. (Isomorphic graphs have the same adjacency matrix for some labelings of their points.) A subgraph of graph G is a graph that has all its points and lines in G. The most useful complexity measures for graphs that we have found are the total number of connected subgraphs, the number of kinds of connected subgraphs, the total number of biclique covers and the number of kinds of biclique covers. (For the definitions of these covers, see S.H. Bertz and C.M. Zamfirescu, MATCH–Communications in Mathematical and in Computer Chemistry 2000, 42, 39-70.) The total number of trees, graphs without cycles, and the number of kinds of trees are useful in some circumstances. We will describe the extension of these measures from graphs to digraphs and compare our results with those obtained by using the total walk count, a versatile complexity measure for graphs developed by Rücker and coworkers. We will also compare our approach to those based on information theory and algorithmic complexity.
*MANOJ GAMBHIR - Possible Steps Toward a Theory of Organization
Author(s):
Manoj Gambhir, RedfishGroup, USA
Stephen Guerin, RedfishGroup, USA
Stuart Kauffman, USA
Daniel Kunkle, RedfishGrouop, USA
Abstract:
A distinguishing feature of self-organizing dynamical systems is their evolution from unconstrained, high entropy states to constrained, low entropy states. These systems therefore spontaneously become more constrained as they advance through time, seemingly in contradiction to the second law of thermodynamics. However, it is possible to make the argument--and thereby bring back to bear the framework of thermodynamics--that self-organizing systems do work, in the sense of physics, upon themselves to construct the constraints leading to structure and low entropy. Once a system has formed a structure, and it is constrained, it needs to repeatedly do work on itself to maintain its low entropy, constrained state. The repetition of the performance of work is--in physics--represented by a work cycle. We show that a simple self-organizing ant-foraging model does indeed repeatedly do work during its structure maintenance phase and that this repeated process can be described as a work cycle.
*CLAIRE MARTINET-EDELIST - An experimentation strategy directed by kinetic logic
Author(s):
Claire Martinet-Edelist, CNRS, France
Abstract:
Kinetic logic appears as an interesting simplification of complex sytems with feedback loops. It takes time and thresholds of activity into account and allows to find all steady states stable and unstable as previously shown (Snoussi and Thomas 1993, Thomas 1993). The first description is a graph accounting for the different elements of the system, their interaction with a few strength levels, and sometimes environment, represented by external variables. Kinetic logic, a method easily accessible to biologists or physicians, with one unique hypothesis, the existence of threshold(s) of activity for each variable, allows to write semi-logical equations and to construct state tables. Then, it takes time into account through various on- and off- delays and leads to the prediction of the elements apparition/disparition. Therefore this method seems to be convenient for building simplified models related to infectious diseases, where several feedback loops occur. This implies usually qualitative predictions concerning the dynamics of such biological systems, leading to a movement back and forth between experimentation or observation and logical description. After a rapid description of the method, we will illustrate it and show how these elementary models applied to several viral diseases, allow predictions experimentally verified. Snoussi, E.H. & R. Thomas (1993). Logical identification of all steady states : the concept of feedback loop characteristic states. Bulletin of Mathematical Biology 55: 973-991. Thomas, R. (1993). Logical identification of all steady states. In: J. Demongeot and V. Capasso (ed.), Mathematics applied to Biology and Medicine, pp. 345-357. Winnipeg, Canada, Wuerz Publishing Ltd.

7:00PM-10:00PM EVENING BREAKOUT SESSIONS

*JEFF CARES - Social Systems

*IRENE CONRAD - Educational Reform at the Edge of Chaos
Author(s):
Irene Conrad, University of Pittsburgh, USA
Abstract:
Educational Reform at the Edge of Chaos Abstract The theoretical foundation that inspires educational theory, that in turn forms the systems structure of institutions of learning, is based on a linear structural model. The aim of this study is to explore the fallacies and inconsistencies in the resultant application of this “existing” linear education systems model and to further explore the framework of a hybrid complex adaptive systems theory model for education that would replace the current theoretical foundation. It is the purpose of this study, through constructive theorizing, and a retroductive and abductive research strategy, to establish complexity theory and complex adaptive systems as a coherent, valid, and verifiable systems’ framework that accurately aligns the education system with its goal as a student-centered complex adaptive system. The study is designed to provide educational practioners with a two-fold apparatus: 1st a concrete comparison between the existing “linear” structural, organizational models as a foundation for design and implementation of education vs. complex adaptive systems models for education systems design and implementation and 2nd a comprehensive theory that replaces the current and explains and enables learning for the 21st century. Previous research has not examined the possible implications for future school systems design as viewed through the adaptive complex systems model. The purpose of this study is to present: 1) the implications and validation from educational stakeholders through future scenarios, 2) as result of #1, identify implication in school structure design 3) draft a framework for implementation, and 4) implement a pilot.
CARLOS E. MALDONADO - Complexity and the Social Sciences
Author(s):
Carlos E. Maldonado, Universidad Externado de Colombia - South America, Colombia
Abstract:
Given that human social systems are non-ergodic (Kauffmann, Bar-Yam), and that the use of scales and maps is not so as useful as it is in purely biological or physical systems, this paper focuses on the problem of measuring the complexity of human social systems. The paper stresses the importance of recognizing time and temporal dimensions vis-a-vis the kind of systems this paper is concerned with. It is my contention to argue that human social systems bear the maximum possible complexity due precisely to the significance of time. Three arguments can be provided, thus: a) how to harness complexity (Axelrod and Cohen); b) how to manage complexity (De Rosnay), and c) how to participate in complexity and not just control it. These are the keystones that allow us to distinguish between any other mesure of complexity and the one concerned with human systems. Such are the specifics of interwoven complexity and social sciences. I shall argue that for human social systems the maximum complexity corresponds to their evolution, and that it is possible that the highest degree of complexity might have been either reached at some point in the past, be the actual situation, or any possible critical point at a given point in the future. This papers explores the plausibility and conceptual and philosophical consequences of these three possibilities.
*LOUISE K. COMFORT - Modeling Complexity in Disaster Environments
Author(s):
Louise K. Comfort, University of Pittsburgh, USA
Kilkon Ko, University of Pittsburgh, USA
Adam Zagorecki, University of Pittsburgh, USA
Abstract:
Designing an information system to support decision processes in emergency environments presents an extraordinarily complex set of challenges for both technical and organizational researchers. Emergency managers need to understand the performance of the response system at several levels of operation simultaneously. While they need to know the detailed requirements for performance at a local site, they also need to recognize the consequences of a failure at any one site for its neighboring sites in the system. Further, managers need to recognize the consequences of cumulative failure at one level of operation that may lead to potential failure at other levels. Without the capacity to understand the interdependencies of a complex technical system, managers cannot anticipate the destructive consequences of the aggregation of apparently minor failures at single sites and take adequate decisions to prevent cumulative damage to the system. Innovative information and simulation technologies to support this decision process are essential. Providing real-time, decision quality information to practicing managers in timely, graphic form that displays the operation of a complex infrastructure system at multiple scalar levels would enable managers at their respective positions within the system to monitor interactions among the components and adjust performance reciprocally to reduce risk. This capacity would generate a self-organizing approach to the management of risk that would use local information to achieve a global goal. It introduces a second dynamic, the system’s informed adaptation to changed conditions that serves to interrupt the spread of dysfunction from the disaster event throughout the entire system. It is the interaction between these two dynamics – spreading dysfunction and cascading adaptation – that measures the system’s performance – or fragility - under threat. This task can most effectively be performed computationally. Currently under development at the University of Pittsburgh, the Executive Dashboard for Crisis Operations (EDCO) will enable practicing managers at different levels of responsibility and in different locations to monitor the status of critical operating conditions and systems on a wide area basis. The Dashboard will enhance the existing Interactive, Intelligent, Spatial Information System (IISIS) prototype that is being tested at the University of Pittsburgh. The IISIS prototype, designed in accordance with national standards for the Incident Management System and data representation in visual and graphic display, provides managers with real-time decision support to mitigate, prepare for, respond to, and recover from, extreme events. The function of the Dashboard will be to integrate real-time information from multiple sources in the disaster environment into a more advanced metric for assessing the level of risk to the whole community. The need for such a DSS is increasing with growing exposure to risk in metropolitan regions, critical interdependencies among lifeline systems, and greater uncertainty regarding threats from natural, technical, and deliberate disasters. This research is supported by National Science Foundation grant, CNS ITR#0325353, Secure CITI: A Secure Critical Information Infrastructure for Disaster Management.
NANCY HAYDEN - Knowing Terrorism as a Complex Adaptive System
Author(s):
Nancy Hayden, Sandia National Laboratories
Abstract:
The fundamental principle of security and strategic warfare is to "know thy enemy." Since September 11, the national security community has been struggling to do just that with respect to terrorist threats. Yet the nature of the terrorist threat is that it is constantly changing. Furthermore, there is a dynamic, non-linear feedback between our responses to the threat – whether they be instantiated through military action, media coverage, social groups, or formal institutions —and its evolution. These properties are the hallmarks of a complex system. Is such a system ultimately “knowable? What does complexity science offer to help us understand the enemy we do not know in a way that can help us “win the war”? The problem of knowing what we know about terrorism was articulated recently by Jerrold Post, who stated that, with respect to the war on terrorism, our fundamental problem is that "We do not know what we know." Our society functions as a complex, interconnected "system of systems" of diverse natures— engineered structures, bureaucratic institutions, informal social groups, and natural ecosystems — developed over a wide range of timeframes and degrees of complexity to perform multiple and different functions depending on the context. Understanding terrorism demands an analysis framework that is valid within each of these diverse systems, yet can also be applied across the interconnected whole. To complicate matters, our gaze must go beyond our own borders—to systems operative across different political, cultural, socioeconomic, religious and geographic boundaries. Transcending the boundaries between these disciplines has, to date, been an insurmountable obstacle for researchers and analysts alike. This is due in no small part to the historical path of evolution of the social science disciplines, and the research methods adopted for their development. For the last half century, the social sciences have taken great pains to develop rigorous research methods that adhere to the scientific principles of investigation, yet, due to the nature of the systems they describe, these are not always satisfactory for investigating complex, cross-cultural issues. This is due not only to the diverse, evolutionary nature of the systems described within each, but also to the lack of common tools, vocabulary, and analysis paradigms that are able to translate domain specific results across domains of expertise and into a larger problem context. The tools and methods of complexity science that have evolved in the last twenty years begin to provide such capabilities. At the same time, social science research methods are putting more credence in theoretical investigations wherein the researcher is in a constant cyclical process of being guided in his/her queries by the data gathered in the field. Such methods have been termed "grounded theory." Using grounded theory as a process of social science investigation mirrors - at an abstract level - the systems engineering paradigm outlined by Garajedaghi for analyzing complex systems. So, is there hope for a meeting of minds and methods on the horizon? Some of the tools and concepts for modeling engineering and biological systems that have come out of complexity science are advancing research into social, pyschological, and cognitive phenomenon. For example, the spread of group ideas and movements may be modeled as processes of diffusion, revolution, co-evolution, epochal evolution, and punctuated equilibrium. Learning and reasoning processes can be simulated by methods of cellular automata, genetic algorithms, evolutionary algorithms, and neural networks. Rational decision-making can be modeled via decision trees, hierarchies, embedded networks, and rule-based agents. Methods for topographical analyses of networks and the dynamics that act upon them have developed to enable exploration of these systems as random, scale-free, or giant stars, and what that means about the stability, robustness and predictablilty of these structures. The beauty and power of complexity science, however, lies in our utilization of these methods to continue to generate theory and test it against what we see in reality, and finally, to work on solutions to the pressing problems our society faces – such as terrorism. Are we at a point in this field to develop a coherent capability for understanding such complex problems as the war on terrorism? If so, what might that capability look like? This paper proposes a framework for exploring this question and its solution space, based on purpose of analytic inquiry, degree of system complexity, granularity desired/required and methodologies available.
*EDWARD P. MACKERROW - Agent-Based Simulation of the Demand for Islamist Terrorist Organizations
Author(s):
Edward P. MacKerrow, Los Alamos National Laboratory, USA
Abstract:
Agent-Based Simulation of the Demand for Islamist Terrorist Organizations Speaker: Edward P. MacKerrow ABSTRACT: Understanding the social dynamics which provide a support base and public demand for radical Islamist organizations is important in reducing the acceptance of terrorism in the world. Empirical data show that conditions of relative deprivation alone are not sufficient for predicting militant terrorism . Complex political, religious, economic, and ethnic factors are associated with the demand for militant Islamist organizations. In order to gain insights into to these complex socio-dynamics we have developed a large agent-based simulation framework. This simulation examines the sources of grievance in the Middle-East based on input empirical data, surveys, and expert opinion. The simulation is coupled with a GIS system with resolution at the sub-national district level. The framework allows for scenario testing, what-if analyses, and vetting of different social theories behind terrorism. I will present our team's accomplishments of modeling social grievance in Muslim societies and our initial results for modeling the coupling of social grievance with established organizations, including HAMAS, Hizballah, and al Qaida. We have developed the simulation in close collaboration with Muslim social scientists and utilize constructs from Islamic Economic Theory. I will compare and contrast differences in Islamic and Western economic theory applied to terrorism. The end goal of this simulation is to increase anticipatory insight into different scenarios based on a socio-economic, political, military, and intelligence influences.
*MAJOR MARK T. CALHOUN - Complexity and Army Transformation
Author(s):
Major Mark T. Calhoun, School of Advanced Military Studies, Ft. Leavenworth, Kansas, USA
Abstract:
As a component of current Department of Defense initiatives to transform the United States Military, the U.S. Army is undergoing an innovative process termed "Army Transformation." This paper investigates whether the current methodology of Army Transformation embodies the characteristics of complex adaptive systems and the manner in which these systems best attain innovation. The study analyzes the degree to which change agents were distributed throughout military organizations during the Napoleonic era and the German interwar period prior to World War II, identifying the correlation between high degrees of distribution of change agents, and innovative success. This analysis serves as a means by which to determine whether Army Transformation demonstrates patterns of change agent distribution similar to those observed in the historically successful models.
*ROGER HURWITZ - Computing the Battle for Hearts and MInds: Lessons from the Vendee
Author(s):
Roger Hurwitz, Massachusetts Institute of Technology, USA
Abstract:
Predictions for the spread of revolution, political innovation or instability at the international level are often based on metaphors such as contagion and the domino effect. A current example is the Bush administrations belief that democracy in Iraq would lead to the rapid transformation of the Middle East; that it would be a decisive step in what, without apparent irony, it calls "the battle for hearts and minds." However, historical analogues suggest any pattern is more complex and not uniform. It depends on local conditions at various regions, such as degrees of discontent, pre-existent social networks and local leadership, as well as the revolutionary idea. Several classic works, like Eric Wolf's Peasant Wars of the 20th Century, and Charles Tilly's Vendee, a study of the counter-revolution in France, have examined local conditions with the aim of identifying patterns which supported/ inhibited revolution and counter-revolution in different areas. As a prelude to research that uses agent based modeling and social network analysis to analyze the spread of terrorism in response to globalization, we examine Tilly's results and their amenability to these modes of representations. We then discuss how the revealed interactions of ideas, traditions, social systems and economic conditions may be applied to the contemporary situations.

*JOEL MACAUSLAN - Nonlinear Dynamics and Pattern Formation

*TEEMU LEPPANEN - Morphological diversity and robustness of Turing structures
Author(s):
Teemu Leppanen, Helsinki University of Technology, Finland
Mikko Karttunen, Helsinki University of Technology, Finland
Rafael A. Barrio, Universidad Nacional Autonoma de Mexico, Mexico
Kimmo Kaski, Helsinki University of Technology, Finland
Abstract:
MORPHOLOGICAL DIVERSITY AND ROBUSTNESS OF TURING STRUCTURES The reaction-diffusion systems discovered by Alan Turing in 1952 have been shown to possess the ability to imitate natural patterns, e.g. animal coatings (mammals, fish, butterflies etc.). Conclusive evidence connecting the Turing mechanism to biology is still missing, however. The recent growth in computational resources has enabled extensive numerical studies, which have both brought a great deal of new insight complementing the knowledge obtained from chemical experiments and facilitated the development of complex biological growth models. We have numerically simulated the evolution of structures in two and three dimensions arising from the Turing instability [1]. The numerical approach has made it possible to study morphological transitions as the bifurcation parameter [2] or the spatial dimensions of the system [3] are varied. We have also studied the effect of additive Gaussian random noise on the developing structures (melting/stabilization) [4]. In addition to the numerical simulations, we have analytically investigated the pattern selection in a generic Turing model by approximating the chemical dynamics by a standard amplitude equation presentation obtained using center manifold reduction [5]. The parameters of our generic Turing model may be adjusted in such a way that three stationary states exist instead of only one. The relative stability of these states, and the type of the unstable modes combined with their nonlinear coupling, determines the complex spatio-temporal behavior of the system. The fact that a Turing model may exhibit oscillations, and these oscillations may couple with Turing patterns of fixed characteristic length scale, may be of great interest in biological context. For example, skin hair follicle formation, which is closely related to skin pigmentation, occurs in cycles. REFERENCES (available at http://www.lce.hut.fi/research/polymer/turing.shtml) [1] T. Leppänen, M. Karttunen, K. Kaski, R. A. Barrio, and L. Zhang, "A new dimension to Turing patterns", Physica D 168-169C, 35-44 (2002). [2] T. Leppänen, M. Karttunen, R.A. Barrio, and K. Kaski, "Morphological transitions and bistability in Turing systems", submitted to PRE, 2003. [3] T. Leppänen, M. Karttunen, K. Kaski, and R. A. Barrio, "Dimensionality effects in Turing pattern formation", Int. J. Mod. Phys. B 17, 5541-5553 (2004). [4] T. Leppänen, M. Karttunen, K. Kaski, and R. A. Barrio, "The effect of noise on Turing patterns", Prog. Theor. Phys. (Suppl.) 150, 367-370 (2003). [5] T. Leppänen, "The theory of Turing pattern formation", submitted 2004.
*MARTA IBANES - A gradient model for proximo-distal differentiation in vertebrate limbs
Author(s):
Marta Ibanes, The Salk Institute for Biological Studies, USA
Diego Rasskin-Gutman, The Salk Institute for Biological Studies, USA
Yasuhiko Kawakami, The Salk Institute for Biological Studies, USA
Ángel Raya, The Salk Institute for Biological Studies, USA
Juan Carlos Izpisúa-Belmonte, The Salk Institute for Biological Studies, USA
Abstract:
The development of a multicellular organism rises extremely interesting and challenging questions such as how are the highly reliable and robust processes of morphogenesis achieved and which mechanisms control pattern formation. The developing vertebrate limb bud is an excellent model to study both theoretically and experimentally pattern formation during embryogenesis. The limbs develop from small protrusions that arise from the body wall of the embryo and differentiation proceeds distally as the limb extends. There are several theoretical models for pattern formation in the developing vertebrate limb based on reaction-diffusion mechanisms, morphogens or mechano-chemical interactions. We present a model for proximo-distal differentiation in the limb bud based on a robust spatiotemporal gradient that allows the cells to progressively differentiate and which explains the phenotypes of several mutations and manipulations. The robustness of the gradient is analysed. Moreover, the existence of other genetic information specifying the proximo-distal fate is underscored both theoretically and experimentally. Finally, our model is compared with already existing models for the proximo-distal differentiation, the Progress Zone model and the early-specification model.
*DANIEL COORE - Towards a Universal Language for Amorphous Computing
Author(s):
Daniel Coore, University of the West Indies, Jamaica
Abstract:
This paper identifies several ``building-block'' activities for the amorphous computing model that can be abstracted and combined in various ways to create interesting patterns of non-local behaviour. The discussion is supported by demonstrating how some of the solutions to problems in amorphous computing could be expressed as suitable combinations of these building blocks.
*A. BRAD MURRAY - Pattern Formation from Emergent, Finite Amplitude Interactions: the Example of Sandy-Coastline Evolution
Author(s):
A. Brad Murray, Duke University, USA
Andrew Ashton, Duke University, USA
Abstract:
Researchers are increasingly studying morphodynamic instabilities and self-organization as possible explanations for pattern formation in nature. Often, such research concentrates on the initial instability; how a morphological perturbation can cause a change in the patterns of flow and sediment transport that in turn cause the perturbation to grow. These investigations often employ linear (or weakly nonlinear) stability analyses that address whether an instability exits, and if so what scales of perturbations will grow most rapidly, starting from a homogeneous 'basic state.' Numerical models designed to examine the effects of infinitesimal-amplitude perturbations on flow and sediment transport are also used to address these questions. Such investigations involve the explicit or implicit assumption that the wavelengths and shapes of the fastest-growing perturbations can be related to the characteristics of natural patterns. Examinations of how a pattern evolves after perturbations reach finite amplitude are less common. However, in many cases the interactions between finite-amplitude features cause a pattern to change both quantitatively and qualitatively after the initial stage of perturbation growth. For example, natural and modeled eolian-ripple patterns increase in wavelength and plan-view organization as smaller and faster ripples overtake and merge with larger and slower ones [e.g. Anderson, 1990; Landry and Werner, 1994]. Laboratory and model braided-stream patterns evolve from the initially developing, fairly regular checkerboard pattern of thalwegs and shoals predicted by linear stability analyses into a chaotic assemblage of fully developed and dynamic bars and channels with a wide range of scales [e.g. Murray and Paola, 1994; Murray and Paola, 1997]. In such cases, the characteristics of the fully developed patterns observed in nature are created by the interactions between emergent structures-features with scales and shapes not predicted by examinations of the equations governing slight variations from a simple basic state-and do not reflect the scale or arrangement of the initially fastest growing perturbations. Investigating a finite-amplitude mode of pattern formation cannot be accomplished analytically, but requires experimentation with physical models or numerical models designed to treat geometrically complex and evolving, finite-amplitude morphological features and associated boundary conditions. The dynamics of plan-view coastline patterns in a recently developed model [Ashton et al., 2001] provide an extreme example of self-organization resulting from interactions between emergent structures. Ashton et al. [2001] pointed out that considering alongshore sediment flux as a function of the relative angle between deep-water waves and local shoreline orientation leads to the conclusion that an instability in plan-view shoreline shape is a robust possibility; perturbations in an otherwise smooth shoreline will grow when waves in deep water approach from angles that deviate sufficiently from shore-normal. Treating the case of a single angle of wave attack, we perform a linear stability analysis. This analysis, while verifying that for wave-angle distributions weighted toward high-angle waves perturbations will grow, is not relevant to shoreline evolution in nature. It cannot be used to address: 1) the characteristics of perturbations growing to finite-amplitude; 2) the results of interactions between different growing features on an extended length of shoreline; or 3) the more-realistic case of waves approaching from different angles at different times. A simple numerical model designed to explore the long-term evolution of an extended shoreline domain [Ashton et al., 2001] shows several modes of interactions that lead to large-scale coastline features including cuspate forelands, cuspate spits, and alongshore 'sandwaves.' (This model is similar to '1-line' models commonly used in coastal engineering, but employs an algorithm designed to treat arbitrarily complex shoreline configurations and the full range of wave-approach angles. This model is also specifically designed to address large-scale morphodynamics; it implicitly averages over relatively small-scale and short-term variations in shoreline, surf-zone, and inner shelf morphology and the resultant variations in wave conditions.) Here we highlight the various ways that large-scale features can emerge and interact with each other in the model under various wave climates, leading to the organization of the different patterns. The changes in shoreline orientation resulting from the growth of large-scale features change the sediment fluxes into neighboring shoreline segments. In addition, a feature can alter the local wave climates felt in other shoreline regions. When features develop large cross-shelf amplitudes, this affect can extend great distances. The resulting self-organization of coastline shapes involves the interplay of local and non-local, nonlinear interactions, and could not be readily predicted by examining the equations governing local sediment flux or the initial growth of shoreline perturbations. References Anderson, R., Eolian ripples as examples of self-organization in geomorphological systems, Earth Sci. Reviews, 28 (29), 77-96, 1990. Ashton, A., A.B. Murray, and O. Arnoult, Formation of coastline features by large-scale instabilities induced by high-angle waves, Nature, 414, 296-300, 2001. Landry, W., and B.T. Werner, Computer simulations of self-organized wind ripple patterns, Physica D, 77, 238-260, 1994. Murray, A.B., and Paola, C., 1994, A cellular model of braided streams: Nature, v. 371, 54-57. Murray, A.B., and C. Paola, Properties of a cellular braided stream model, Earth Surf. Proc. Landf., 22, 1001-1025, 1997.
*ELENNA DUGUNDJI - Socio-Dynamic Discrete Choice: Analytical Results for the Nested Logit Model
Author(s):
Elenna Dugundji, MIT, USA
Abstract:
Suppose you have the possibility to choose to adopt one of a number of discrete behaviors or to choose to buy one of a number of different products. Moreover, suppose the choice is multi-dimensional or more generally, that there are common unobserved attributes of the choice alternatives. A classic approach to statistical prediction in such a situation given an observed sample of decision-making agents in a population is the nested logit model, pioneered by Ben-Akiva (1973). Now suppose your choice to adopt a discrete behavior or buy a discrete product is influenced by what choices your neighbors and/or members of your social network make, or by your personal general perception of percentages of segments of the population making these choices. Brock and Durlauf (2003) have proposed a variant of the nested logit model for handling multi-dimensional choice of group and behavior, noting that, “There has yet to be any analysis of (such) models... when self-consistency is imposed on the expected group choice percentages. Such an analysis should provide a number of interesting results.” It is my aim to fill this gap. I present benchmark analytical results for mean-field, steady-state corner solutions in parameter space derived drawing on techniques from the mathematics of dynamical systems and bifurcation theory. I also show that the nested logit model reduces to the well-known Potts model in statistical mechanics under various simplifying assumptions. I conclude highlighting limitations of the present study and my recommendations for future work.
E. LOZNEANU - Self-organized plasma blobs as possible precursors of life
Author(s):
E. Lozneanu, Complex System Laboratory of “Al. I. Cuza” University, Romania
M. Sanduloviciu, Complex System Laboratory of “Al. I. Cuza” University, Romania
Abstract:
Experimental results already described [1] prove that a spark, produced in a plasma is able, under certain prerequisite conditions, to initiate a self-organization process whose final product is a complex space charge configuration (CSCC) whose structure and behaviors satisfy all criteria for an operational definition of life. Thus the CSCC contains a nucleus bordered by a cell-like membrane assembled by self-organization, the membrane supports a potential drop sufficient to perform all operations required for the cell existence, the cell duplicates by division and is able to communicate information by emitting electromagnetic energy. Recently dubbed as plasma blobs these CSCCs were considered a hint for a new form of life [2]. In respect to the opinion that life is the result of an evolution over millions of years, related to chemical reactions initiated by random processes at microscopic scale [3], our experiments prove that life occurs through an instability taking place at mesoscopic scale governed only by nonlinear physical processes in a time span of a few microseconds. Investigation of the nucleus of the CSCC reveals the presence of an environment that is, in our opinion, the prerequisite condition for a further longtime biochemical evolution towards the contemporary cell. Concerning the central issue of complexity/SOC discussions, namely if natural phenomena like the emergence of life violates the second law of thermodynamics, our experiments prove that this is locally possible for a short time span. Thus the gaseous cell emerged in laboratory work as an “engine” whose duty cycle comprises a branch during which, for a short time span, thermal energy is directly converted in energy used for maintaining the existence of the CSCC in a “viable” state. This is a state in which a rhythmic exchange of matter and energy between the CSCC and the surroundings ensures its survival. For nanoscale science and technology the knowledge of the scenario of self-organization whose finial product is a CSCC in a state exclusively attributed to living beings is of great interest since it potential offers a new insight into the intrinsic mechanism able to explain the emergence and behaviors of biochemical assemblies [4]. The importance of the knowledge of the scenario of self-organization revealed by plasma experiments for other branches of science was pointed out in an invited paper recently presented by us (in collaboration with S. Popescu) at the XXVI Int. Conf. Phen. Ionized Gases, Greifswald, Germany, July 15 2003 (in press in Contr. Plasma Phys.). References: [1] E. Lozneanu & M. Sanduloviciu, Chaos Solitons & Fractals 18 (2003) 335. [2] New Scientist 20 September 2003 p. 16. [3] G. Nicolis, The New Physics ed. P.A. Davies, Cambridge 1999. [4] E. Lozneanu et al., J. Appl. Phys. 92 (2002) 1195 (reproduced in Virtual Journal of Nanoscale Science & Technology, volume 6, Issue 5, July 29, 2002.
*YING ZHANG - 3D Substitution Model for Limb Growth and Pattern Formation
Author(s):
Ying Zhang, Biocomplexity Institute, Physics Department, Indiana University, USA
James A. Glazier, Biocomplexity Institute, Physics Department, Indiana University, USA
Stuart A. Newman, New York Medical College, USA
Abstract:
Substitution models are iterative procedures for developing complex structures. While substitution models are common in bioinformatics, we have found that substitution models are also useful for describing development at the cellular level, agreeing well with experimental data on developmental system, including cell division, differentiation, cell-cell interactions, and cell movement in response to external fields. During development of the vertebrate limb, spatiotemporally regulated condensation of mesenchymal cells, followed by chondrogenesis, leads to a pattern of skeletal elements. We have devised a three-dimensional substitution model to describe limb growth, cell differentiation and cartilage pattern formation, incorporating mechanical constraints. We have validated the model by predicting cell behavior and comparing it to experimental data

7:00PM-10:00PM EVENING EXTENDED TALK SESSIONS

*JEFF SCHANK - Learning / Neural, Psychological and Psycho-Social Systems

*THEA LUBA - MediaMetro / Museocracy / SonicMetro: A New Complex Systems Model for Arts Education
Author(s):
Thea Luba, Virtual Lessons Company, USA
Abstract:
MediaMetro.org, Museocracy.org and SonicMetro.org provide fertile complex systems research environments and populations. MediaMetro Society is made up of constituents from the Educational, Scientific, Artistic, and Business communities. Its mission is to build our new complex systems model of arts education:SonicMetro. The mission of SonicMetro is to involve our young people in a peaceful, cooperative, egalitarian complex learning environment. Using the technology and interactivity of video games, SonicMetro is created moment by moment by vibrant, life-affirming musicians, dancers, artists, photographers, writers etc. who believe that their positive energy has the power to create a new model of arts education. The MediaMetro Society will determine the governing principles and practices of SonicMetro during an online Constitutional Convention at Museocracy.org. This presentation will include 1. The Complexity of our Constituency 2.Our relationship with Small Learning Communities (Small High School Initiatives) 3. The Complexity of SonicMetro Technology 4. The Complexity of Curriculum Development 5.The Complexity of Assessment and Accountability 6.The Complexity of Strategic Alliances 7. SonicMetro as an Economic Empowerment Zone MediaMetroans use online interconnectivity for sharing ideas from different disciplines, perspectives and approaches to arts education. Using the tools of complex systems analysis, MediaMetro, Museocracy and SonicMetro can help us understand the potential impact on the educational system of new technologies and efforts at systemic reform.
*WENJIE HU - Dynamics of Innate Spatial-Temporal Learning Process: Data Driven Education Results Identify Universal Barriers to Learning
Author(s):
Wenjie Hu, M.I.N.D. Institute, USA
Mark Bodner, M.I.N.D. Institute, USA
Edward G. Jones, University of California, Davis, USA
Matthew R. Peterson, M.I.N.D. Institute, USA
Gordon L. Shaw, M.I.N.D. Institute, USA
Abstract:
Spatial-temporal (ST) reasoning - thinking in patterns - data from computerized STAR (ST Animation Reasoning) video games designed to dynamically enhance students learning mathematical concepts (www.MindInstitute.net) are gathered over the internet. ST reasoning has been shown to be innate to the structured columnar cortex and to be highly trainable. The data containing fundamental ST information (learning curves) are analyzed and then sent back to teachers so that students can be trained efficiently in an interactive manner. Here we report the use of data mining techniques to examine the dynamics of the learning process - Data Driven Education (DDE). The learning curves for each STAR game, played a number of times on several days, are grouped into different categories according to contours, identifying the different phases of learning. We present our first DDE results from > 2,200 2nd graders on one STAR game showing plateaus in the learning curves. These plateaus are then identified with universal sharp barriers to learning related to specifics in the game design common to many computer games. Simple changes in the design of this game will be tested to see if these sharp barriers to rapid learning are removed. Further DDE studies will not only provide fundamental information on how learning occurs, but may also form the basis for a revolution in education.
*GOTTFRIED MAYER-KRESS - Multiple Time-Scale Landscape Models of Motor Learning
Author(s):
Gottfried Mayer-Kress, Penn State University, USA
Yeou-Teh Liu, National Taiwan Normal University, Taiwan, ROC
Karl Newell, Penn State University, USA
Abstract:
We present a multiple time-scales landscape model to describe motor-learning of human coordination tasks. We illustrate how the "universal power-law of practice" can be more effectiveely described and interpreted theoretically by assigning different time-scales to different aspects of the movement. Re-analysis of classical data from the early part of the 20th century using our new method reveal new structures that might have an impact on sports-coaching and more general learning strategies.
*ROBERT K. LOGAN - The Emergence of Language and Culture out of the Complexity of Hominid Existence
Author(s):
Robert K. Logan, Dept. of Physics - Univ. of Toronto, Canada
Abstract:
The Emergence of Language and Culture out of the Complexity of Hominid Existence Robert K. Logan - Dept. of Physics - University of Toronto logan@physics.utoronto.ca Abstract One of the difficulties in understanding the origin of language is the lack of empirical data. The thesis that will be developed in this paper is that historic data relating to the evolution of language after the advent of speech and beginning with the emergence of writing can shed light on the origin and evolution of human language. In The Sixth Language (Logan, Toronto: Stoddart, 2000a) language is assumed to be both a medium of communication and an informatics tool to show that speech, writing, math, science, computing and the Internet form an evolutionary chain of languages. Each new form of language emerged as a bifurcation and a new level of order to deal with the chaos and information overload that the previous forms of language could not handle. Exploiting this approach the origins of speech and the human mind are shown to have emerged simultaneously as the bifurcation from percepts to concepts and a response to the chaos associated with the information overload that resulted from the increased complexity in hominid life. Our ancestors developed toolmaking, controlled fire, and hence, developed manual praxic articulation. They lived in larger social groups which resulted in the development of social organization. And they engaged in large scale co-ordinated hunting which required mimetic communication. As a result of these developments their minds could no longer cope with the richness of life solely on the basis of its perceptual sensorium and as a result a new level of order emerged in the form of conceptualization and speech. Speech arose primarily as a way to control information and then was used as a tool for communication. Thought is not silent speech but rather speech is vocalized thought. The mechanism that allowed the transition from percept to concept was the emergence of speech. The words of spoken language are the actual medium or mechanism by which concepts are expressed or represented. Word are both metaphors and strange attractors uniting many perceptual experiences in terms of a single concept. Spoken language and abstract conceptual thinking emerged together at exactly the same point of time as a bifurcation from alingual communication skills and the concrete percept-based thinking of pre-lingual hominids. (Logan 2000b) The transition from percept-based thinking to concept-based thinking represented a major discontinuity in human thought. Language extended the brain which hitherto served as a percept processor into the human mind capable of conceptualization and planning (mind = brain + language). We use our dynamic systems model of the mind to understand the connections between technology, commerce, artistic expression, narrative and science and to generate what we have playfully called the Grand Unification Theory of Human Thought. Manual praxic articulation evolves into technology, social intelligence into commerce and mimetic communication into artistic expression. A synthesis of the Extended Mind model with the work of Christiansen (1994), Deacon (1997) and Donald (1991) is made showing an overlap of these four approaches in which a parallel is drawn respectively between conceptualization, sequential learning and processing, symbolic representation and mimetic culture as a pre-adaptations for spoken language. Christiansen's notion of treating language as an organism is generalized to the consideration of culture as an organism also with the result that a notion of Universal Culture emerges in parallel with the notion of Universal Grammar. References Christiansen, Morten. 1994. Infinite languages finite minds: Connectionism, learning and linguistic structure. Unpublished doctoral dissertation, Centre for Cognitive Studies, University of Edinburgh UK. Deacon, T. W. 1997. The Symbolic Species: The Co-evolution of the Brain and Language. New York: W. W. Norton & Co. Donald, Merlin. 1991. The Origin of the Modern Mind. Cambridge, Ma.: Harvard University Press. Logan, Robert K. 2000a. The Sixth Language: Learning a Living in the Internet Age. Toronto: Stoddart Publishing. Logan, Robert K. 2000b. The extended mind: understanding language and thought in terms of complexity and chaos theory. In Lance Strate (ed), 2000 Communication and Speech Annual Vol. 14.
*BRIAN D. JOSEPHSON - How we might be able to understand the brain
Author(s):
Brian D. Josephson, University of Cambridge, UK
Abstract:
The neurosciences can account for many basic processes, but processes such as those associated with language present a more serious problem, the difficulty in understanding the brain being in essence a difficulty in seeing the 'trees' (logically coherent units) for the 'wood' (the whole nervous system), and in understanding how the 'trees' work together. In this paper the effectiveness of the design is attributed to three factors: mathematical underpinnings of the components of the design, observational emergence, and representational redescription. As is the nature of design, the physical structure is accompanied by a notional explanatory structure, but in the case of the brain this has special features, viz. observational emergence, providing, in accord with the hyperstructure scheme of Baas, for the accumulation of structures fitting the prescribed mathematical schemes notwithstanding variations in context, while the representational redescription process of Karmiloff-Smith makes possible a developmental progression to working at more abstract levels. The proposed scheme should be able to impose a clear structure on a very unclear problem. For example, a specific process such as language can be seen to utilise specific abstractional schemes, and integration of the present proposals with analyses such as those of Jackendoff and Arbib should make possible a precise theory of language. The significance of a specific aspect of language, that of syntax, is discussed in this light.
*CHRISTIAN MACHENS - A push-pull model of prefrontal cortex during a sequential discrimination task
Author(s):
Christian Machens, Cold Spring Harbor Laboratory, USA
Carlos Brody, Cold Spring Harbor Laboratory, USA
Abstract:
Sequential discrimination tasks are widely used in psychophysical studies. In a typical such task, a subject is presented with a first stimulus (f1), and then, after a delay of a few seconds, with a second stimulus (f2), after which the subject must make a decision based on a comparison of the two (f2 > f1?). Sequential discrimination thus requires at least three components: loading a stimulus with a particular value (f1) into the working memory system, storing that value over a few seconds, and then comparing the memory of f1 to f2 when the second stimulus f2 is presented. For a somatosensory variant of this task, the neurophysiological pathway has been largely identified. In particular, neurons that participate in all three components of the task have been found in the prefrontal cortex of macaques [Romo et al., 1999; Brody et al., unpublished observations]. Neurons that exhibit activity as a function of the first stimulus (f1) fall into two classes: those with firing rate proportional to f1 (positive tuning) and those with firing rate proportional to -f1 (negative tuning). Based on these findings, we set up a simple neural network model for the prefrontal cortex. The network consists of oppositely tuned neurons that form a line attractor and are therefore able to store the value of a single stimulus (such as f1). We show how the input of neurons from somatosensory cortex S2 manipulates the attractor dynamics of the network model so that stimulus f1 can be loaded or that stimulus f2 can be compared to the memory of f1. The model agrees with key aspects of the electrophysiological evidence and is able to carry out all three components of the task (i.e., loading, storing, comparing) within a single, integrated, framework.
*KARL YOUNG - Global MRI Diagnostic Tools Via Statistical Complexity Measures
Author(s):
Karl Young, University of California at San Francisco, USA
John Kornak, University of California at San Francisco, USA
Yue Chen, Northern California Institue for Research and Education, USA
Andrew Maudsley, University of Miami, USA
Norbert Schuff, University of California at San Francisco, USA
Abstract:
Magnetic resonance imaging (MRI) data, which are available in a large variety of modalities, has led to challenges regarding how to best utilize and interpret combined information for diagnostic purposes. For example, a MRI study of the brain may involve structural, spectroscopy, perfusion, and functional MRI in the same session, providing anatomical, metabolic, physiological, and functional information. Furthermore, great progress has been made in registering different MRI modalities via the use of brain atlases, so that regional information is also maintained. However, a major problem of this approach is identification of relevant information for diagnosis from the huge amount of regional and multimodal information. This research explores a complimentary, global approach that attempts to to utilize entropy and statistical complexity measures applied to multimodal data to obtain global measures of brain function. As a demonstration of the methods we use simulated brain data from an extension of the BrainWeb Simulated Brain Database to generate simulations for which e.g. levels of brain metabolites in different brain regions can be controlled. We then track changes in entropy and statistical complexity as a function of variation in simulated disease states such as loss of the neuronal marker N-acetylaspartate in gray matter that is widely thought to occur in Alzheimer's disease.
*CAROLINE YOON - Emergent Mathematical Ideas from Complex Conceptual Systems
Author(s):
Caroline Yoon, Purdue University, USA
Richard Lesh, Purdue University, USA
Abstract:
In many classrooms, mathematics is taught as a set of rules or theorems that need to be mastered through exercises of increasing difficulty, before they can be used in real world problems. In contrast, this paper suggests that mathematical ideas are emergent properties of holistic conceptual systems that students develop while mathematizing problematic situations. These ideas cannot be learned in isolation of the conceptual systems from which they derive their meanings. Instead, they develop as students notice relations and patterns in systems involving transformations on mathematical (structural) elements, such as rates, proportional quantities, sequences, and measures of chance. Furthermore, the evolution of these ideas in a classroom environment resembles the evolution of communities of complex adaptive systems, struggling for survival in the presence of diversity, selection, reproduction and communication. In this paper, we give some examples of such mathematical ideas and their associated conceptual systems, and we describe features of learning environments that foster their development.
*LIQIANG ZHU - Impacts of homo- and hetero- synaptic plasticities on neuronal networks
Author(s):
Liqiang Zhu, Arizona State University, USA
Ying-Cheng Lai, Arizona State University, USA
Frank Hoppensteadt, New York University, USA
Jiping He, Arizona State University, USA
Abstract:
Learning novel experiences is an important function of central nervous systems. Accumulating evidence indicates that synaptic plasticity forms the cellular basis of learning and memory. Recently, two new types of plasticities were observed in experiments, i.e., spike timing-dependent plasticity (STDP) and hetero-synaptic plasticity (HSP). An important question is how STDP and HSP interact with each other and modify neural networks. Here we study the impacts of STDP and HSP on randomly connected neuronal networks. Based on results from theoretical analysis using Fokker-Planck theory and numerical simulations, we find that STDP and HSP complement each other and produce more realistic network activities.

Evolution and Ecology

*MANUEL MENDOZA-GARCIA - How Communities Evolve
Author(s):
Manuel Mendoza-Garcia, Brown University, USA
Abstract:
This work presents an overview of our empirical and theoretical research on the trophic organization of biological communities (Mendoza et al., 2004). Some regularities are observed in the analysis of the relationship between the trophic structure (i.e., the distribution of species among feeding groups) in a number of African large mammal communities and the type of ecosystem. Different ecosystem types (e.g., arid communities with sparse vegetation, wooded savannas and evergreen forests) are characterized by specific patterns in the trophic structure of their mammalian communities. Those communities from the same type of ecosystem occupy a specific region of the n-dimensional trophic space defined by the number of species of n feeding groups (i.e., grazers, mixed-feeders, browsers and frugivores among herbivores; flesh-eaters, omnivores and bone crackers among carnivores). In order to explain the origin of these patterns, we propose a theoretical model based on the distribution of energy flows through a three trophic level food chain, with a topological organization based on observed mammalian communities. The main aim of this study is to show that it is possible to obtain a fully dynamic explanation of those patterns. The model is shown to spontaneously define different types of macroscopic structures in community organization, closely related to those observed in mammalian communities. In summary, we suggest that communities are unitary structures with coherent properties that result from the self-organizing dynamic of the whole system. The initial flux of energy into the system is partitioned among different classes of primary producers, on which herbivores with distinct feeding preferences depend, followed by carnivores that again fall into distinct classes according to prey size. The dynamic properties of the whole community reflect constraints on the trophic organization of the system, resulting in stable points that can be grouped in different types of organization despite continuous environmental gradients that determine overall energy flux through the system. This type of dynamic structure fits observed ecosystem organization, and provides a basis for identifying communities as self-organizing units that undergo evolutionary change. The proposed model constitutes a theoretical base for explaining the correlation between different environmental factors and the abundance or diversity of herbivores (Olff et al., 2002). In addition, this model establishes a general mechanism that makes it possible to understand how and why some rules constrain the assembly of the communities (Fox, 1999). In short, the model proposed leads us to see how biological communities can operate in an integrated way, which allows for the acceptance of their changes on large time-scales as evolutionary (Eldredge, 1996). The results of the simulation, moreover, support the view that the discrete nature observed in the structure of mammalian communities arises as a result of bifurcations in parameter space. References Eldredge, N., 1996. Hierarchies in Macroevolution In: Evolutionary Paleobiology (Jablonski, D. Douglas, H. E. and Lipps, J.H. eds.) 3: 43-61. Chicago University Press; Chicago and London. Fox, B.J., 1999. The genesis and development of guild assembly rules. In: Ecological assembly rules: Perspectives, advances, retreats. (Weither, E. & Keddy, P. eds.) 1: 23-57. Cambridge University Press. Mendoza, M., Goodwin, B., Criado, C. Emergence of Community Structure in Terrestrial Mammal-Dominated Ecosystems. Journal of Theoretical Biology (In press). Olff, H.; Ritchie, M.E. and Prins, H.H.T, 2002. Global environmental controls of diversity in large herbivores. Nature 415: 901-904.
*HIROKI SAYAMA - Self-Protection and Diversity in Self-Replicating Cellular Automata
Author(s):
Hiroki Sayama, University of Electro-Communications, Japan
Abstract:
The concept of "self-protection", a capability of an organism to protect itself from exogenous attacks, is introduced to the design of artificial evolutionary systems as a possible method to create and maintain diversity in the population. Three different mechanisms of self-protection are considered and implemented on a cellular automata based evolutionary system, the evoloop. Simulation results imply a positive effect of those mechanisms on diversity maintenance, especially when the self-protection is moderate so that it conserves both the attacker and the attacked. We will present the newly derived self-protecting evoloop models and discuss implications of the results obtained using these models for broader contexts.
KOJI OHNISHI - Autopoietic learning-neural network-like bio-machinogenesis via semeiogenesis: A unified theory on the cognitive genesis and evolution of biosystems
Author(s):
Koji Ohnishi, Center for Interdisciplinary Research, and Department of Biology, Faculty of Science, Niigata University, Japan
Yuka Ishimoto, Graduate school of science and technology, Niigata University, Japan
Naotaka Furuichi, Center for Interdisciplinary Research, and Graduate school of science and technology, Niigata University, Japan
Masaki Goda, Faculty of Engineering, Niigata University, Japan

Abstract:
Genetic codes were found to have emerged as semeiotic culture of hierarchical tRNA-riboorganismic society in early intracellular micro-environment ("semeiotic culture theory" or "poly-tRNA theory" on the origin of genetic codes)(1, 2). Well-made biomachines such as bee super-organism (= bee eusociety consisting of queens and workers), animal body (= super-organism consisting of germ-line (queen) and somatic-line (worker) unicell diploid animals), and genetic apparatus were found to have evolved by neural-network-like machinogenesis via queen-and-worker-type hierarchical sociogenesis (1). Every of these socio-machinogenesis depends on the society's own specific semiotic system which is considered to be the society's "semiotic culture". Origins and evolution of cognitive and autopoietic characters of various biosytems were discussed, with special emphases on intracellular riboorganismic societies (RNA societies), multi-cellular societies (= multicellular animal body), and multi-individual iso-species society (= Mayr's interbreeding population or "specia"), as well as on various semiotic cultural systems including genetic codes, bee-dance language, hormones (in multi-cellular society = animal body, etc.), pheromones (in "iso-species society"), and human language systems. In case of multicellular animals, worker-cells were found to select a "gene set" (inherited from previous generation's gametes via fertilization) by their active altruistic behaviors to genetically closely related queen (germ-line) cells, whereas the queen cells make "the selected gene set" inherited to the next generation. This division of work constitutes the basis of autopoietic natural selection of gene-sets by the self biosystem in every generation, which will result in active/autopoietic evolution of multicellular animals. Lamarck's use-disuse phenomenon is elegantly explained by this autopoietic natural selection, because somatic cell's behaviors (= "use of organ or somatic cells") can actively select adaptive evolutionary changes (via autopoietically selecting "gene sets" ). We have now reached a new concept that every life is, most plausibly, some kind of autopoietic (self-improving) cognitive system, which can well explain "why organisms behave and evolve 'actively' or 'autopoietically'. References: (1) Ohnishi,K. et al. (2002) Viva Origino 30: 63-78, 2002 (http://www.origin-life.gr.jp/3002/3002en.html). (2) Ohnishi et al., Genome Informatics, 13:71-81, 2003 (http://hc.ims.u-tokyo.ac.jp/JSBi/journal/GI13.html).
*AXEL G. ROSSBERG - Holling cycles in simulations of complex food webs
Author(s):
Axel G. Rossberg, Yokohama National University, Japan
Takashi Amemiya, Yokohama National University, Japan
Kiminori Itoh, Yokohama National University, Japan
Abstract:
Challenging the idea of ecosystem stability, C. S. Holling (1986) introduced the concept of the adaptive cycle into ecology. This process, which has been describe in similarly form for economical, technical, social, and physiological complex systems, is briefly sketched like this: Starting with a situation of weak internal organization and low recourse exploitation, the components of the system organize themselves to make exploitation of the system possible (alpha phase). This is followed by a phase of rapid growth and resource exploitation (r phase) that goes along with increasing internal organization and dependencies, until a saturation is reached (K phase). This highly organized state is very fragile. Upon a sufficient perturbation, the system collapses (Omega phase), its internal organization is destroyed and the cycle starts with a new alpha phase. Such a behavior is here reported to be reproducible in dynamical simulations of the food webs (predation networks) of complex ecosystems that are subject to a constant invasion pressure of new species. The simulations allow an investigation of the subtle mix of randomness and determinism involved in Holling cycles, which is inaccessible by the purely heuristic analysis.
*JOHN W. PEPPER - Emergent segregation and over-mixing from symmetric movement rules
Author(s):
John W. Pepper, University of Arizona, USA
Michael Lachmann Tamarlin, Max Planck Institute for evolutionary anthropology, Germany
Eric Smith, Santa Fe Institute, USA
Abstract:
In complex systems the interactions of multiple agents with one another and their environment can generate system-level patterns that are quite surprising even when the behavior of the individual agents is simple and well understood. Here we present a paradigmatic example of such a system involving segregation and mixing in a heterogeneous population of agents This model is of interest for several reasons: 1) there is a striking incongruity between the symmetry of the agents’ rules of motion and the asymmetry of the emergent system-level patterns, 2) the cuasal connection s between the local rules and the emergent system-level patterns are understood in unusual detail, and 3) the emergence of segregation and mixing exemplified by this model have important implications in both the biological and social sciences.
*WILLIAM SILVERT - Speciation through Bifurcation
Author(s):
William Silvert, INIAP-IPIMAR, Portugal
Abstract:
Speciation is usually considered a consequence of differences in habitat, ecological interactions, or other environmental conditions. This paper presents a mechanism for speciation even when all members of the original species encounter identical environmental conditions, through bifurcation in the optimal fitness strategy. Several examples fit this model, including chaetognaths and cottid fish.
ANATOLY BRILKOV - MATHEMATICAL AND EXPERIMENTAL MODELING OF BIOLOGICAL EVOLUTION BY THE EXAMPLE OF RECOMBINANT BACTERIA AT CONTINUOUS CULTIVATION
Author(s):
Anatoly Brilkov, Krasnoyarsk State University, Russia
Ivan Loginov, Institute of biophysics SB RAS, Russia
Elena Morozova, Institute of biophysics SB RAS, Russia
Alexey Plotnikov, Krasnoyarsk State University, Russia
Abstract:
MATHEMATICAL AND EXPERIMENTAL MODELING OF BIOLOGICAL EVOLUTION BY THE EXAMPLE OF RECOMBINANT BACTERIA AT CONTINUOUS CULTIVATION Brilkov А.V., Loginov I.A., Morozova E.V., Plotnikov A.V. Krasnoyarsk State University, Institute of Biophysics SB RAS, Krasnoyarsk, 660036, Russia; phone: (3912)494455; fax: (3912)433400; e-mail: bril@ibp.krasnoyarsk.su Continuous cultivation is analogous to the majority of natural situations: chemostat is similar to the situations of growth limitation by the deficiency of nutrient materials, elements and microelements; turbidostat corresponds with the conditions of maximum possible growth at limitation of population density. From the point of view of open systems functioning chemostat and turbidostat are thermodynamic systems able to maintain stable stationary state. At that, according to M. Eigen classification, chemostat conforms to the constant flows case, turbidostat – to the constant organization case (or constant reacting forces). Thus, experimentalists possess open systems of two major types of evolution both for biology and thermodynamics. If evolutionary changes or transfer from one steady state to another in the result of changing qualitative properties of the system (e.g. after the processes of mutation or selection) take place in such systems, the main characteristics of these genetic reorganizations in populations or evolution steps can be measured without losing the community of approach from the point of view of both biology and physics. By now this has not been realized from the point of view of methodology, though a lot of data on the work of both types of “evolutionary machines” has been collected. In our experiments we used the Escherichia coli strains, containing in plasmids the cloned genes of marine photobacteria bioluminescence and genes of green fluorescent protein (GFP), which expression level can be easily changed and controlled. In spite of the apparent kinetic diversity of evolutionary transfers in two types of open systems, the general mechanisms characterizing the increase of used energy flow by bacterial populations can be revealed at their study. According to the energy approach, at spontaneous transfer from one steady state to another (e.g. in the process of microevolution), heat dissipation characterizing the rate of enthropy growth should increase rather then decrease or maintain steady as М. Eigen, G. Nikolis and I. Prigozhin believed. The results of our observations of experimental evolution of recombinant bacterial strains require further development of thermodynamic theory of open biological systems and further study of general mechanisms of biological evolution.
*MICHAEL LEVANDOWSKY - Complexity Measures for Ecological Assemblages
Author(s):
Jyoti Champanerkar, New Jersey Institute of Technology, USA
Denis Blackmore, New Jersey Institute of Technology, USA
Michael Levandowsky, Pace University, USA
Abstract:
Three approaches to an ecosystem measure of complexity are explored, all based on the general notion of connectivity. Two species or other taxa are said to be connected or linked ecologically if they occur together more often than expected by chance, whether they are linked trophically, auxotrophically, or simply have similar basic requirements. (1) The first measure P(k) is defined as the ratio of the number of vertices connected to k other vertices divided by the total number of vertices, where taxa are the vertices. (2) A second, dimC (S), is based on a box fractal dimensional approach, in which a sample S is partitioned into n equal subsamples, or equal aliquots. This complexity dimension also gives a way of counting the connections among taxa. (3) Finally, let A be the matrix with entries counting the number of connections between the taxa in X, and define B = exp(A), which essentially contains all connections of all lengths among the taxa. Another measure of complexity, F(X), is the logarithm of the sum of all non-diagonal entries in B divided by logn. Note that is an approximation of a box fractal dimension defined by the scale 1/n. Applications to assemblages of soil amoebas are used to illustrate these measures
*HANNELORE BRANDT - Indirect Reciprocity and the Evolution of Morals
Author(s):
Hannelore Brandt, University of Vienna, Austria
Karl Sigmund, University of Vienna, Austria
Abstract:
Indirect reciprocity serves as a framework for understanding large-scale human cooperation and is based on the idea that a player who cooperated gains the return back not from the beneficiary, as in direct reciprocity, but from a third person, who knows of his good deed through reputation. This can work if the cost of an altruistic act if offset by a raised 'score' or status, which increases the chance to subsequently become the recipient of an altruistic act. Cooperation is channeled towards the 'valuable' members of the community, and involves reputation and status. Ever since image-based models for indirect reciprocity were introduced, the relative merits of scoring vs. standing have been discussed to find out how important it is to differentiate between justified and non-justified defections. This is analogous to the question whether punishment can sustain cooperation even when it is costly. We show that an answer to this question can depend on details of the model, for instance concerning the probability distribution of the number of interactions experienced per player. We use extensive individual-based simulations to compare scoring, standing and other forms of assessing defections, and show that several forms of indirect reciprocation can robustly sustain cooperation.

WEDNESDAY, May 19

9:00AM-12:20PM SOCIAL SYSTEMS

*MIRIAM HELLER - NSF - Social Systems

*THEODORE BESTOR - Tracking global sushi
*THOMAS HOMER-DIXON - Complexity of global systems
*SCOTT E PAGE - Diversity: Aggregation or Perspectives and Heuristics
Author(s):
Scott E Page, University of Michigan, USA
Abstract:
In this talk, I will discuss complex systems models of diverse agents. The topic of diversity is of increasing importance in the study of organizations, polities, and economies. Diversity is commonly thought of as differences in identity and standpoint. I show how these theoretical concepts can be formally translated and embedded into complex systems models. In this talk, I will focus on three primary results: 1. Why diversity trumps individual ability 2. The emergence of diverse cultures within firms and countries 3. How diverse information aggregates The talk will be introductory and relatively speaking non technical. By that I mean, I will employ diagrams with some simple states like cooperate or defect but I will not delve into specifices of the non time homogenous markov processes that underpin a particular model.
*STEVEN ALAN HASSAN - Strategic Interaction Approach: Complex Systems and Undoing Cult Mind Control
Author(s):
Steven Alan Hassan, Freedom Of Mind Resource Center Inc., USA
Abstract:
Abstract for presentation at NECSI conference Destructive mind control is a systematic social influence process that typically includes deception, hypnosis and behavior modification techniques to subvert an individual’s identity in order to create a new pseudo-identity in the image of the leader. A mind control model will be presented that demonstrates how the control of: behavior; information; thoughts and emotions are used by destructive cults (pyramid structured, authoritarian regimes) to make cloned identities, obedient and dependent to its authority. Furthermore, the presentation will include how a complex system model called the Strategic Interaction Approach can be used to mobilize social networks to empower impacted individuals to reassert their own identity and independence and break free from the pseudo-identity.

2:00PM-3:30PM AFTERNOON BREAKOUT SESSIONS

Special Session

*MAYA PACZUSKI - Scale Free Networks of Earthquakes and Aftershocks
Author(s):
Maya Paczuski, Imperial College London, UK
Marco Baiesi, Department of Physics, University of Padua, Italy
Abstract:
Earthquakes are a complex, hierarchically correlated process in space and time. We propose a new metric to quantify the correlations. The metric consists of a product involving the time interval and spatial distance between two events, as well as the magnitude of the first one. According to this metric, events typically are strongly correlated to only one or a few preceding ones. Thus a classification of events as foreshocks, main shocks or aftershocks emerges without imposing predefined space-time windows. To construct a network, each earthquake receives an incoming link from its most correlated predecessor. The number of aftershocks for any event, identified by its outgoing links, is found to be scale free with exponent $\gamma = 2.0(1)$. The original Omori law with $p=1$ emerges as a robust feature of seismicity, holding up to years even for aftershock sequences initiated by intermediate magnitude events. The broad distribution of distances between earthquakes and their aftershocks suggests that aftershock collection with fixed space windows is not appropriate.
*EVE MITLETON-KELLY - An Integrated Methodology to Facilitate The Emergence of New Ways of Organising
Author(s):
Eve Mitleton-Kelly, London School of Economics, UK
Abstract:
If organisations are seen as complex evolving systems (CES), then the approaches, methods and tools that we use to study them and to help them evolve need to be appropriate - for example, they need to take the characteristics of organisations as CES into account; they need to track changes over time; and they need to address both the qualitative and the quantitative aspects of the organisation under study. The Complexity Group at the London School of Economics has been working collaboratively with organisations since 1995 to develop such a methodology and the paper will describe the different qualitative and quantitative tools and methods that make up the integrated methodology. The Group has also been developing a theory of complex social systems that underpins the methodology. Both the methodology and the theory have been developed and tested in practice in a series of projects looking at real problems faced by our business partners and the paper will illustrate the use of the methodology using a specific case. The tools provide rigor by triangulating the data and the findings and by testing against interpretation bias. They also provide different but complementary information about the organisation and offer a very rich and deep understanding. The findings can then be used as an informed basis for co-creating the enabling infrastructure. We work with a core team to identify the social, cultural and technical conditions that together will help the organisation create the kind of environment conducive to change and the emergence of new ways of organising.

*HELEN HARTE - Healthcare

*FRANK FUNDERBURK - Organizational Culture from a Complex Dynamic Systems Perspective: Moving from Metaphor to Action in Healthcare
Author(s):
Frank Funderburk, In*Compass Systems
Abstract:
Organizational Culture from a Complex Dynamic Systems Perspective: Moving from Metaphor to Action in Healthcare Frank R. Funderburk In*Compass Systems frankfunder@in-compass.org As noted by Boan and Funderburk (2003), people in healthcare organizations work, relate, and generally behave in ways that are guided by the norms of their organization and the healthcare industry as a whole. Organizational culture is often called upon as a descriptive, organizing and/or explanatory construct to characterize the shared beliefs, perceptions, expectations, norms and acceptable behaviors of individuals as they interact within and between the various groups and organizations that they represent. The construct has been linked in the literature to various aspects of organizational performance including profitability, quality, customer and employee satisfaction, safety, and innovation. Organizational culture influences how outcome information is used in organizational decision-making (e.g., Hodges & Hernandez, 1999) and thus impacts efforts to implement and sustain clinical quality improvement, assure patient safety, and improve the quality of life of individuals while meeting the challenges of a changing competitive marketplace. Improved psychometric development procedures have produced reliable and valid measures of various dimensions of organizational culture. Denison and colleagues (Denison, 1984; Denison & Mishra, 1995; Fisher & Alford, 2000) for example have developed a quantitative multidimensional assessment tool that rates an organization on four key cultural traits (involvement, consistency, adaptability, and mission). Their work notes that high performing organizations are able to simultaneously cope with seemingly contradictory or paradoxical goals and demands (i.e., are both flexible and stable; simultaneously maintain an internal and external perspective). However, strategies to change organizational cultures in a positive manner are less well documented. Many recent discussions of organizational culture and change have contrasted mechanistic management theories, stressing hierarchical command and control mechanisms, with the more holistic view of the organization as complex adaptive systems, stressing decentralized flexibility and continuous learning (e.g., Zimmerman et al., 1998). Such discussions help to illustrate the pervasive role of our own mental models, descriptive linguistic conventions, and belief systems as we strive to develop successful and responsive business enterprises (e.g., Lissack, 1999; Weick, 1995) but the “complexity sciences” offer more than just a convenient conceptual framework (with a new set of metaphors) to the leaders of healthcare organizations as they strive to bridge what has become known as the “Healthcare Quality Chasm,” as described in the recent Institute of Medicine report (2001; Berwick, 2002). This paper presents a survey of several formal dynamic models that have relevance for healthcare policy development and evaluation giving special attention to the class of social phenomena typically subsumed under the broad category of organizational culture. After this overview, the focus will be on the practical implications of findings derived from this methodological approach. How might this knowledge be put to use to improve healthcare systems today? How might existing models be refined further to guide future improvements? The models being considered include the exploratory policy methods discussed by Bankes (1993, 2002), the computational organizational modeling approach of Carley (1996, 2002), agent-based models of cooperation (Axelrod, 1997), conflict resolution (e.g., Klein et al., 2000), and approaches that aim to diagnose and suggest systemic interventions for troubled organizational cultures (e.g., Schein, 1996; Senge, 1990, Sterman, 2000, Wolsterholme, 2003). Each of these approaches has developed practical knowledge that can be used by researchers, policy-makers, and healthcare leaders to improve the quality of healthcare, and the quality of life, for people in the communities that we serve.
*ANITA PATIL - Modeling Safety Outcomes on Patient Care Units
Author(s):
Anita Patil, University of Arizona College of Enginnering, USA
Judith Effken, University of Arizona College of Nursing, USA
Kathleen M. Carley, Carnegie-Mellon University, USA
Ju-Sung Lee, Carnegie-Mellon University, USA
Abstract:
In this paper, we describe how we used OrgAhead; a computational program designed for modeling organizations to model patient care units in acute care hospitals. OrgAhead models complex organizations, which change and adapt over time due to restructuring and learning. Specifically, we use OrgAhead to create 32 virtual units, corresponding to 32 actual units, on the basis of data collected for a large research study exploring the impact of workplace characteristics on patient outcomes. Correspondence between virtual and actual units was validated by comparing the performance, measured in terms of patient safety and quality outcomes, for the actual and virtual units. Validation was accomplished using OrgAhead’s static version since the actual unit data were taken at a single point in time. We then set up virtual experiments involving dynamic simulations to find the kinds of changes the unit must undergo to improve its safety and quality outcomes maximally. We will report the results of a pilot study of 6 patient care units in which we used dynamic simulations to generate hypotheses about the specific kinds of interventions managers could initiate that would be expected to improve patient safety and quality outcomes on their units and the relative improvement that would be generated with each intervention. We expect that the data from the virtual units will be helpful to managers in conceptualizing the changes that would effect improvement in their units, prior to implementing them.
*SALIL H. PATEL - Complex Medical Information Systems: A Social Context
Author(s):
Salil H. Patel, Johns Hopkins School of Medicine, USA
Abstract:
Diverse knowledge systems play central roles in the delivery of health services. Medical data collection, archival, and analysis are all increasing in both rate and volume; massively-linked datasets are emerging and present novel challenges to informaticians, healthcare workers, and the lay public. Factors driving this increasing level of data complexity include new methods of diagnosis and therapy, evidence-based practice, safety concerns, and increased consumer demand for personalized services. The development and maintenance of a public dialogue addressing these phenomena must include an assessment of the ethical and legal implications of complex information systems in healthcare.

*LISA MARIE MEFFERT - Origins

*BRUCE WEBER - Complex Systems Dynamics and the Emergence of Life and Natural Selection
Author(s):
Bruce Weber, California State University Fullerton and Bennington College
Abstract:
Making progress on understanding the emergence of life is important not only in its own right but also as a case study for developing insight into processes of emergence needed for developing a more general theory of emergence. Though some argue that the origin of life is an intractable problem, a perspective of complex systems dynamics (CSD) can provide clues for progress. Rather than look for an event, an origin, we should explore the types of processes and sequences of processes that could lead to the emergence of systems with the types of properties seen in contemporary living entities. Nor should we elevate one aspect of living systems, such as self-replication, as the defining trait of life. We need to be equally concerned with conditions of closure and constraint (physical, chemical, catalytic, thermodynamic), metabolic transformations (energy capture, transduction, utilization, building of structure, order, organization), epigenesis and development (including whole life cycles as part of replication and reproduction) information processing (from signals to infodynamics and biosemiotics) and functional behavior (teleodynamics). While one or the other of these might arise before others, it is unlikely, from the perspective of CSD, that they would be perfected serially before interacting to produce something that could count as a living cell. Rather, it is proposed that rudimentary aspects of these various properties would cohere at an early stage and that an ensemble of crude proto-cells could show the kind of weak heritability and variation that Stuart Kauffman has envisioned for catalytically-closed, autocatalytic sets of peptides (or catalytic RNAs). Such a system, even in the absence of a robust template/memory molecule, would change over time as a result of the interplay of self-organizational and selective processes. Initially such selection would be more for catalytic and thermodynamic efficiency, but with the development of macromolecules dedicated to stabilizing information about useful sets of catalysts, there would be the emergence of true biological or natural selection of the reproductively fit. Thus, natural selection, as well as life, itself is seen as emergent phenomena. With natural selection the type of emergence possible in evolution arises. Following Terry Deacon, evolutionary emergence emerges from the self-organizational emergence that led to life. If there are deep natural laws and processes that lead to life, then it is not surprising that life could have arisen within a couple of hundred million years of the minimal permissible physical and chemical conditions obtaining, as current geological evidence suggests. The CDS perspectives also suggests that our goal should not be to find the trajectory that led to life, but rather to search for as many plausible scenarios as possible and then to see how many ways these might be combined, how transitional forms might function for a period before being superceded by better processes or structures. Indeed, there may not be a single common ancestral cell arising from pre- and proto-biotic processes any more than current molecular research can point to a clear-cut common ancestor of contemporary organisms, due to the extensive lateral gene transfer in the early years of true lineages of living entities. Thus reformulated, the problem of life's origins is not intractable but rather is an open scientific question for which the tools provided by the development of the sciences of complex systems dynamics are ripe for deployment.
*TERRENCE W. DEACON - Minimal conditions for natural selection
Author(s):
Terrence W. Deacon, University of California, Berkeley, USA
Abstract:
Living organisms and their constituents are not fully described with respect to just their component structures and interactions. Living processes remain incompletely described without reference to such teleological concepts as functions, information, self, adaptation, etc., even if the underlying molecular-cellular relationships can be completely described. The necessity of teleological terminology derives from the fact that the existence of these structures and their dynamics can only be fully understood in the context of those variants that are no longer present in the environment due to selection processes, so that what exists is a reflection of these absent alternative forms and the environment that provided this selective retention. This teleological framing cannot be exorcised by describing these processes using teleonomic vocabulary analogized to human artifactual mechanisms (e.g. regulation, information, computation), since this merely passes the explanatory buck to the bracketed human agency and intensionality. In both life and mind the explanatory buck that separates the "teleodynamical" from the merely thermodynamical ultimately traces back to the evolutionary and self-organizing dynamics that gave rise to them. In order to investigate the emergence of teleological dynamics from non-teleological antecedents, then, it is useful to try to specify the minimal requirements for a physical system to constitute a process of evolutionary selection. I describe a reciprocally interdependent self-organizing molecular complex-essentially a self-reconstituting hierarchically recursive hypercycle-that can spontaneously form and reconstitute itself if disrupted. Its constituents include only an autocatalytic ensemble of molecules that produce as a waste product of catalysis other molecular units that tend to self-assemble into sheets that tend to enclose a volume (such as in a viral protein coat or a lipid bilayer). An autocatalytic process with these characteristics will tend to enclose itself, and reconstitute its form after disruption. This is because autocatalysis and self-assembling containment each contribute an essential boundary condition for the maintenance of the other (local continuous production of sheet-forming elements and proximity maintenance of the critical catalysts). This general molecular configuration can probably be spontaneously achieved by a wide range of middle-sized polymeric molecules. Its configuration is sufficient to initiate a process of limited natural selection, despite lacking either metabolism or self-replicating molecules, by virtue of the tendency toward self-reconstition of the dynamical topology of the whole in response to break up. This demonstrates that the minimal conditions for spontaneous evolutionary selection dynamics do not necessarily include these two properties found in living organisms, but do require a second-order reciprocal self-organizing dynamic. A simple system with these characteristics can be shown to exhibit simple exemplars of the properties characteristic of teleological and semiotic systems in general, and show these to be inextricably linked with the process of natural selection.

2:00PM-5:00PM AFTERNOON PARALLEL SESSIONS

*SUI HUANG - Systems Biology

*MUNEESH TEWARI - SYSTEMATIC INTERACTOME MAPPING AND GENETIC PERTURBATION ANALYSIS OF A C. ELEGANS TGF-BETA SIGNALING NETWORK
Author(s):
Muneesh Tewari, Dana-Farber Cancer Institute and Harvard Medical School, USA
Patrick Hu, Massachusetts General Hospital and Harvard Medical School, USA
Jin-Sook Ahn, Dana-Farber Cancer Institute and Harvard Medical School, USA
Nono Ayivi-Guedehoussou, Dana-Farber Cancer Institute and Harvard Medical School, USA
Pierre-Olivier Vidalain, Dana-Farber Cancer Institute and Harvard Medical School, USA
Siming Li, Stuart Milstein, Chris M. Armstrong, Mike Boxem, Maurice D. Butler, Svetlana Busiguina, Jean-Francois Rual, Nieves Ibarrola, Sabrina T. Chaklos, Nicolas Bertin, Philippe Vaglio, Mark L. Edgley, Kevin V. King, Patrice S. Albert, Jean Vandenhaute, Akhilesh Pandey, Donald L. Riddle, Gary Ruvkun, and Marc Vidal
Abstract:
To initiate a system-level analysis of C. elegans DAF-7/TGF-beta signaling, we combined interactome mapping with single and double genetic perturbations. Yeast two-hybrid (Y2H) screens starting with known DAF-7/TGF-beta pathway components defined a network of 71 interactions among 59 proteins. Co-affinity purification (co-AP) assays in mammalian cells confirmed the overall quality of this network. Systematic perturbations of the network using RNAi, both in wild-type and daf-7/TGF-beta pathway mutant animals, identified 9 novel DAF-7/TGF-beta signaling modifiers, 7 of which are conserved in humans. Our results reveal a new level of molecular complexity in DAF-7/TGF-beta signaling. Integrating interactome maps with systematic genetic perturbations may be useful for developing a systems biology approach to this and other signaling modules.
JOHANNES SCHUCHHARDT - Peptide Binding Landscapes
Author(s):
Johannes Schuchhardt, MicroDiscovery GmbH, Germany
Liying Dong, Humboldt-University Berlin, Charite, Medizinische Immunologie, Germany
Achim Kramer, Humboldt-University Berlin, Charite, Medizinische Immunologie, Germany
Jens Schneider-Mergener, Humboldt-University Berlin, Charite, Medizinische Immunologie, Germany
Hanspeter Herzel (3), Humboldt-University Berlin, Institute for Theoretical Biology, Germany
Abstract:
A set of experimental peptide affinity data is investigated employing the theory of neutral molecular networks. Membrane based large scale substitution analysis was applied to a set of immunologically relevant epitope derived-peptides for data generation. The data set is investigated by statistical methods for the number of neutral neighbours belonging to each peptide. Results are used to infer the possible existence of large neutral networks in the space of peptide sequences. Explicit construction and synthesis of three molecular pathways is in support of this hypothesis and gives deeper insight into the character of the fitness landscape. Limits of the theory and relation to concepts in theoretical immunology are discussed.
*WILLIAM SILVERT - Complexity and Allometry
Author(s):
William Silvert, INIAP-IPIMAR, Portugal
Abstract:
Allometric relationships between metabolic rates and body size are well established and widely used, but depend on the range of taxonomic groups included. By including some measure of organismic complexity in the allometric relationship it may be possible to derive a more general equation that would cover a wide range of organisms. The major problem is defining a measure of complexity - it seems clear that animals with temperature regulation, central nervous systems and guts are more complex than amoebas, but quantifying the complexity is a difficult challenge.
CRISTIAN I. CASTILLO-DAVIS - Where the earth meets the sky: understanding cis-regulatory evolution through genomics
Author(s):
Cristian I. Castillo-Davis, Harvard University, USA
Jun S. Liu, Harvard University, USA
Shane Jensen, Harvard University, USA
Abstract:
It is likely that regulatory changes play a key role in generating the morphological diversity of multicellular species. However, little is known about the evolution of gene regulation or the gene networks that control morphogenesis. This deficiency is due in part to the lack of a biologically relevant measure of cis-regulatory evolution that relates directly to gene expression. Since the identity of cis-acting regulatory motifs is generally unknown and such motifs are sparsely scattered within non-coding DNA that is under little or no selective constraint, understanding what constitutes a relevant "change" has been difficult. Here we develop a method to quantify functional changes in the regulatory regions of homologous genes that does not depend on precise knowledge of experimentally characterized DNA binding sites and use it to test hypotheses of developmental constraint.
*PAULI RAMO - Evolution of Gene Regulatory Networks: Growth and Dynamics
Author(s):
Pauli Ramo, Tampere University of Technology, Finland
Juha Kesseli, Tampere University of Technology, Finland
OIli Yli-Harja, Tampere University of Technology, Finland
Abstract:
We investigate how dynamic gene regulatory networks emerge in evolution. Our model combines network growth and dynamical genotype-phenotype mappings in complex environments. We unify ideas from complex networks, nonlinear dynamics and evolution in fitness landscapes to show the emergence of scale-free topology, canalizing functions, and self-organized criticality in gene regulatory networks. To examine this, we constructed a biologically justified simulation model that utilizes artificial genomes, Boolean dynamics, and NK-fitness landscapes. The genome encodes a Boolean network, whose attractors (transcribed proteins) are used to determine the organism fitness. The simulations show that gene regulatory networks self-organize to a stable regime close to the critical threshold. Canalizing functions, especially those that can form forcing structures, are dominant and govern the dynamic behaviour. The network output distribution is scale-free, while the inputs follow the exponential distribution. With our model we are able to find support for existing theories on the topology and dynamics of gene regulatory networks. In addition, we could find patterns of self-organization that would need further biological verification. In conclusion, we found that growth and dynamics are inseparable in the development of regulatory networks.
*VADIM KVITASH - Games Systems Play
Author(s):
Vadim Kvitash, School of Medicine, University of California at San Francisco and Personal Health Response, Inc., USA
Abstract:
In that Plenary Lecture, the following will be presented: 1) Complexity, a resourceful ally, not an enemy 2) The need for Systems-Specific Technology 3) The reasons why Systems-Specific Technology is lacking 4) Operational definitions of Complex versus Super-Complex Systems 5) Systems Ontology 6) Systems Epistemology 7) Systems Methodology 8) Systems-Specific Structural Language and its own Logic 9) Systemicity a) Meaning b) Features c) Multiple Dimensions 10) Systems-Specific Measurements: a) Scaling b) Ranges c) Natural Systems Equivalent Units 11) Universal Systems Relationships: a) Relons (a Normal Systems Relationships) b) Reloms (five distinct types of Abnormal Systems Relationships) 12) Orders and Disorders 13) Taxonomy of Orders 14) Taxonomy of Disorders a) Disorders which increase Type-Level-Degree of Complexity b) Disorders which decrease Type-Level-Degree of Complexity c) Disorders which do not affect Type-Level-Degree of Complexity 15) Balance vs. Imbalance vs. Dysbalance 16) Symmetry vs. Asymmetry 17) Infinite Complexity of Systems Relationships 18) Magic Number 3 and Bang of Infinite Complexity 19) Properties of Infinite Systems Complexity: a) Stable Infinite Complexity b) Fluctuating Infinite Complexity c) Reversible Infinite Complexity 20) n-D Deep Network Structures, Meta-Networks and their Patterns 21) Mining Systems Patterns 22) Systems Patterns Cognition 23) Systems Patterns Visualization and Recognition 24) Systems Dynamics 25) Systems Trajectories 26) Systems Hierarchies 27) Lozitsky-Kvitash Pathway 28) Spectrum of Systems-Specific Tools 29) Systems-Specific Technology Generic Platform for Knowledge Discovery 30) Application for Early Diagnosis, Monitoring and Categorical Predictions of Outcome in Human Diseases 31) Future Developments
*CHRIS WIGGINS - Information-theoretic measures of biological network modularity
Author(s):
Chris Wiggins, Columbia University, USA
Etay Ziv, Columbia University, USA
Manuel Middendorf, Columbia University, USA
Abstract:
Much of recent systems biology has been motivated by discussions of modularity in biological networks. In order to provide a parameter-free algorithm for quantifying ``modularity," we present an information-theoretic definition applicable to biological networks. This definition draws on methods developed by Tishby, Pereira, and Bialek in the context of clustering. In the case of regulatory networks inferred from gene expression data, this allows a measure of the extent to which these networks may be described in terms of transcriptional modules, a type of dimensionality reduction useful both for suggesting novel experiments and for simplifying the number of parameters which must be inferred. We apply these techniques to protein-protein interaction data, genetic regulatory networks, and neuronal networks.
*JEONG SEOP SIM - Transcription Factor Binding Sites Prediction based on Sequence Similarity
Author(s):
Jeong Seop Sim, Electronics and Telecommunications Research Institute, Korea
Myung Eun Lim, Electronics and Telecommunications Research Institute, Korea
Myung Geun Chung, Electronics and Telecommunications Research Institute, Korea
Soo-Jun Park, Electronics and Telecommunications Research Institute, Korea
Sun Hee Park, Electronics and Telecommunications Research Institute, Korea
Abstract:
The availability of the whole genome sequences of human due to the Human Genome Project makes it possible to study gene function much more efficiently. We can find or predict the functions and positions of genes by analyzing the transcriptional regulation. In this paper, we propose an algorithm of predicting TF binding sites from a given set of sequences of upstream regions of genes using suffix array and local alignment algorithm. Our algorithm based on following two assumptions: i) there tends to be a common TF that binds with each of the upstream regions of the functionally related genes, ii) a TF binds with similar DNA sequences.
*JORGE DE BARROS PIRES - Clothing Earth with Mind
Author(s):
Jose Wagner Garcia, Petrobras, Brasil
Jorge de Barros Pires, Petrobras, Brasil
Jorge Vieira, Petrobras, Brasil
Lauro F. B. da Silveira, Petrobras, Brasil
Fernando Pellon de Miranda, Petrobras, Brasil
Lucia Santaella, Petrobras, Brasil, Richard Garrat, IF USP, Brasil Patrick Spencer, IPEN, Brasil, Raquel Kely Bortoleto Bugs, Petrobras, Brasil, Flavio Henrique da Silva, UFSCar, Brasil, Aristides Pavani, Cenpra, Brasil, Roberto Tavares, Cenpra, Brasil, Moacir Carnelos Filho, Petrobras, Brasil,
Abstract:
Regulation of gene expression by many transcription factors is controlled by specific combinations of homo- and heterodimers through a short alpha-helical coiled-coil known as a leucine zipper. Many other transcription factors alike, the leucine-zipper containing transcription factors bind DNA as dimers. A leucine zipper is formed by two alpha helices, one from each monomer. The helices are held together by hydrophobic interactions between leucine residues, which are located on one side of each helix. This article is devoted to the problems of the zipper conduct in microgravity. The major task is to establish a wider knowledge regarding the environment characteristics and leucine zipper conduct. We attempt to explain the mechanisms of sign fields and identify the structure of habit exchanges. Thus, for a research that intends to reach the philosophical dimension of the question about the leucine zipper conduct in microgravity, the density and the depth of Charles S. Peirce's considerations surely will be of the most inestimable value. It’s our goal to introduce and discuss the concept of Organisation in a systemic approach. We believe that such a view helps and clarify the study of auto-organisation and its applications in protein-protein interaction, since this concept has been developed in theories in the domain of a General Systems Theory as a Ontological approach.
*HAO XIONG - Sensitivity Analysis of Optimal Production of Biomass in Metabolic Networks
Author(s):
Hao Xiong, Texas A&M University, USA
Abstract:
Metabolic analysis at system level is essential for understanding biological system. Mathematic models and computational algorithms are a key to the success of metabolic analysis at system level. Flux-balance analysis (FBA) is a powerful tool for developing mathematic model of genome-wide metabolic analysis. Operating principle of metabolic networks is to seek optimal distribution of metabolic flux under constraints of available molecular resources, which determines metabolic phenotypes. In this report, we will use linear programming and nonsmooth optimization to study how metabolic networks respond environmental perturbation and assess network stability.
*MOMIAO XIONG - Generalized Circuit Analysis of Biological Networks
Author(s):
Momiao Xiong, University of Texas Health Science Center at Houston, USA
Jonathan Arnold, University of Georgia, USA
Abstract:
Comprehensive knowledge of a living individual requires understanding the biological networks. Although network is not a new concept but biological network has many unique features and poses a significant challenge because of its scale and unprecedented complexity. It is therefore important to have a novel conceptual framework to quantitatively describe a network’s properties, and new methods for integrating experimental data and theories. To accomplish these tasks, we develop linear circuit theory as a general framework for genetic network analysis. We first introduce concepts of transcriptional potential, current and resistance that can be used to quantify the operations of the genetic networks. Kirchhoff’s current law and Ohm’s law are fundamental to electric circuits. Applications of linear circuit theory to genetic networks require developing similar Kirchhoff’s current law and Ohm’s law in genetic network analysis. Therefore, we extend Kirchhoff’s current law and Ohm’s law to genetic networks. The technologies for measuring resistance of the transcriptors have not been developed. To overcome this, we will develop algorithms to estimate transcriptional resistance based on structural equation model for genetic networks. Finally, to illustrate the generalized circuit analysis of genetic networks, the proposed models and algorithms will be applied to a part of apoptotic networks and TGF-β pathway.

3:30PM-5:00PM AFTERNOON PARALLEL SESSIONS

Social Systems

*TAKESHI ARAI - Estimation of the Functions Describing Transition Potentials of Land Use at Cells Applied to Cellular Automata Based Models of Land Use
Author(s):
Takeshi Arai, Tokyo University of Science (TUS), Japan
Hiroyuki Masuda, Tokyo University of Science (TUS), Japan
Noriaki Anzai, Tokyo University of Science(TUS), Japan
Masahiro Kato, Tokyo University of Science(TUS), Japan
Abstract:
In constructing any CA based models of land use dynamics, definition and estimation of land use transition potentials are essential but difficult parts of the work. Although 'transition potentials' can not be observed directly, in some precedent studies, for example White, Engelen & Uljee (1997) and Arai & Akiyama (2003), transition potential functions were estimated empirically so as to replicate land use dynamics in their study areas. However, we have not yet accumulated the findings about the transition potential functions. In this study, we estimated the function which explains transition potential of land use to residential use at each cell through the statistical analysis of the detailed land use data observed in 1974, 1979, 1984, 1989 and 1994, in the Tokyo Metropolitan Region. We assumed the transition potential to residential use at a cell as a function of the distance from the nearest railway station to the cell, the number of cells used for commercial activities in the neighborhood of the cell, the number of residential cells in the neighborhood of the cell, the number of cells used for roads in the neighborhood of the cell and the number of cells used for parks in the neighborhood of the cell. Additionally the function can be approximated by the weighted sum of five non-linear functions of individual factors. We picked out three study areas in the suburbs of Tokyo, each of which is five kilometers square. Each area has about 10000 cells. According to the results, the cells which were converted to residential use for five years' duration had significantly higher transition potentials. In conclusion, the model presented in this study will contribute to our understandings about the transition potential functions.
*SANTA LA ROCCA - Strategy emergence.A journey through modernism, postmodernism, complexity and nonmodernism
Author(s):
Santa La Rocca, University of Bergamo, Italy
Abstract:
The idea of Modernism as a drive for scientific innovation is currently is currently being challenged. Alternative ways of thinking are explored to deal with scientific innovation. The process often starts with deconstructing Modernism as a premise for the search of perspectives on change and transformation. Rationales for such turnaround are addressed from many sciences, such postmodernism (Derrida), complexity (Stacey) and anthropology (Latour). By deconstructing the metaphysics of presence, Derrida overturns the idea that science is rooted in the observations of facts, as simple, homogeneous and self-conscious origins of truths. Besides, by asserting the very impossibility of language to represent reality, he advances that are continuously re-created by way of differance. In management theory it is possible to show that deconstruction is a sound way to move from a perspective of continuity to a perspective of transformation, which considers continuity as epiphenomenon of transformation. Such turnaround also shows that research on change very often is biased from the paradox of exploring change from an epistemology of stability and order. By asserting that “Nothing exists beyond texts, however, Derrida ignores individuals and society, as well as nature. This is why we introduce also the basic dynamics of communication. Such an approach can be found in the works of Stacey et al.(2000) e Stacey (2001) inspired by the sciences of complexity. These works find in the complexity sciences a compelling analogy for explaining the emergence of organizations as joint relation between individuals, rooted in both self-organization and the assumption that future is unknown. Deep understanding of the complexity approach to the emergence of novelty shows a way to superimpose the natural perspective upon the social. As a result innovation loose sight of the role that individual, society and meaning may play as independent sources of innovation. The possibility of this ample mediation is instead suggested by Latour nonmodernism. His distinction between specialization (purification) and mediation Nature-Society-Discourse may become the starting point to conceive innovation as a perpetual construction of hybrids and networks. In this perspective purified sciences become ex-transcendences, objects of mediation. The statement of a strategy also becomes one of these objects, which looses his ontology in the process of emergence of hybrids object-subject. Strategy, in fact, becomes a process itself, one that involves the co-evolution of discourse-nature-individual and society.
*PHILIP VOS FELLMAN - The Nash Equilibrium Revisited: Chaos and Complexity Hidden in Simplicity
Author(s):
Philip Vos Fellman, Southern New Hampshire University, U.S.A.
Abstract:
The Nash Equilibrium is a much discussed, deceptively complex, method for the analysis of non-cooperative games. If one reads many of the commonly available definitions the description of the Nash Equilibrium is deceptively simple in appearance. Modern research has discovered a number of new and important complex properties of the Nash Equilibrium, some of which remain as contemporary conundrums of extraordinary difficulty and complexity. Among the recently discovered features which the Nash Equilibrium exhibits under various conditions are heteroclinic Hamiltonian dynamics, a very complex asymptotic structure in the context of two-player bi-matrix games and a number of computationally complex or computationally intractable features in other settings. This paper reviews those findings and then suggests how they may inform various market prediction strategies. A COMPLETE COPY OF THIS PAPER IS AVAILABLE FROM THE AUTHOR VIA E-MAIL.
*SERGE HAYWARD - An Artificial Neural Network for Simulating the Complex Dynamics of Financial Assets Prices
Author(s):
Serge Hayward, Ecole Superieure de Commerce de Dijon, France
Abstract:
Considering a stock market as a complex socio-economic system, this paper examines the evolutionary artificial neural network settings for price forecasting and trading strategies’ development. After learning financial time series dynamics, economic agents search for optimal predictions, exploiting existing temporal correlations of the data. This paper is a step towards the econometric foundation of computational intelligence in finance. Financial time series modeling and forecasting are addressed with an artificial neural network, examining issues of its topology dependency. Structural dependency of results is viewed not as a model’s weakness, but as a (current) limitation to explain existent relationships. Simulations of price forecasts and trading strategies development reveal optimal network settings, considered further as nonlinear generalizations of the ARMA processes. Optimal settings examination demonstrates weak relationships between statistical and economic criteria. The search for a statistical foundation of computational intelligence in Finance calls for a parallel search for its economic basis. The choice of evaluation criteria combining statistical and economic qualities is viewed as essential for an adequate analysis of economic systems. Our research has demonstrated that fine-tuning the artificial neural network settings is an important stage in the computational model set-up for results’ improvement and mechanism understanding. Genetic algorithm is proposed to be used for model discovery, making technical decisions less arbitrary and adding additional explanatory power to the analysis of economic systems with computational intelligence.
*BILL MACMILLAN - Modeling spatial economics with an agent-based approach
Author(s):
Bill Macmillan, University of Oxford, UK
He Qing Huang, University of Oxford, UK
Abstract:
The complex behavior of spatial economic systems is studied with an agent-based modeling approach. In this model, each agent is endowed with capability of finding an economical pathway to a new development area and of making reasonable plans for production (and land use), consumption, and marketing in a spatial setting at each time interval. With the application of Java technology, this model performs simulations on a platform at which the patterns of the complex behavior of agents can be directly visualized. Most importantly, this model provides functions of capturing and reflecting in quantitative forms the variations with time in society’s demography, agents’ decision on trading prices, and the emergent patterns of transport system and agricultural land use around markets. With the quantitative information, insights can be gained on how and in what form environmental constraints, typically the size of physical environment, the accommodating capability of shelter area, and the variability of climate and landscape, exert influences on the sustainability of spatial economic systems. At this stage, this model can be used to visualize the evolutionary process of agriculture development that is in the main forms of primitive settles, gathering, transporting and trading at a single market. Further study is under way for exploring the complex behavior of spatial economic systems in which agriculture, towns and cities are interconnected.
*BRIAN RUBINEAU - Job Sex Segregation As A Complex System: Exploring a Simulation Approach
Author(s):
Brian Rubineau, MIT Sloan School of Management, USA
Roberto Fernandez, MIT Sloan School of Management, USA
Abstract:
Job sex segregation is a pervasive international phenomenon with significant implications for male-female inequality. Despite the topic’s importance within sociology and organization studies, and despite the considerable attention and time devoted to its elucidation, few scholars would claim an understanding of job sex segregation sufficient to confidently give recommendations for its reduction. A number of scholars have described job sex segregation as the result of a complex and over-determined system. We agree, and begin the effort to craft a formal, explicit, and integrative expression of job sex segregation as a complex system based on data and insights gained from a detailed empirical case documenting many segregation processes. We implement and integrate seven segregating mechanisms in an agent-based computational simulation. We explore the appropriateness and potential of this approach to segregation research.

*FRED M. DISCENZO - Engineering Systems

*SARJOUN DOUMIT - Cartography application for autonomous sensory agents
Author(s):
Sarjoun Doumit, University of Cincinnati, USA
Ali Minai, University of Cincinnati, USA
Abstract:
This paper proposes a cartographic application for the rapid and exact identification of topological characteristics of a deployment area by a group of mobile sensory agents. It is assumed that the deployment area’s features are completely unknown and that the deployed agents are capable of limited computational and processing power and to communicate wirelessly. A region’s topography reveals information about its elevations, surface types and gives an idea about its prevailing environments. This information amassed by those ‘scout’ agents is crucial for follow-up exploratory missions that use this information to maximize the energy consumption and prevent redundancy and the loss or damage of agents as with the case of some recent Mars probes. Every agent needs to maintain a representation of the world around it in a useful context that can be used in conjunction with other agents’ maps. The cartographic application is autonomously executed based on sensory input from the agents. The agents are able to summarize their collective information into an optimal structure like a “trail” that allows consequent agents to maximize their coverage of the area. A fundamental principle in our system is that the agents must spread out efficiently in a diffusion-inspired mobility pattern that is determined by the environment itself. The knowledge amassed in this initial operation can be re-used for better topographic information on the area for consequent or explorations of the same or nearby areas. The advantage of this technique is that it can be used in tandem with other functionalities like projecting the timing of occurrences of events or their possible locations simply by knowing the terrain’s topology and discovering the relationship between them.
*MARINA A. EPELMAN - A fictitious play approach to large-scale complex systems optimization
Author(s):
Marina A. Epelman, Univerisity of Michigan, USA
Robert L. Smith, University of Michigan, USA
Theodore J. Lambert, Truckee Meadows Community College, USA
Abstract:
We describe the use of algorithms based on the fictitious play paradigm, familiar from game theory, for problems of optimal management of complex engineering systems. Examples of problems we consider include dynamic traffic routing, situation awareness problem in military applications, etc. From a classical engineering perspective, the design and operation of these systems is oriented towards the optimization of a performance criterion, usually overall or system-wide efficiency. However discovering designs and/or operational policies that optimize the performance criterion is an extremely difficult task in many practical problems. Classical procedures for resulting optimization problems rely on regularity properties, like linearity or convexity of the performance criterion, that are seldom met in most of these complex systems where many interacting agents or decisions influence the value of the criterion. Moreover, the performance criterion itself usually does not posses a closed-form expression, but instead comes from, in effect, a ``black box,'' such a simulation model, and thus requires a significant computational effort for each evaluation. The algorithmic paradigm we propose to seek solutions of such optimization problems is to engage in off-line "fictitious play." The fictitious play paradigm arises from an analogous notion in game theory, and will be used as an algorithmic procedure. At each step of this iterative process, the "players" compute their best replies (with respect to their individual utilities) based on the assumption that opponents' decisions follow a probability distribution in agreement with the historical frequency of their past decisions. In the context of system optimization, we view the decision variables of the system as "players" in a game, with each player using the system-wide performance measure as its utility. The algorithm thus uncouples the large dimensional nature of the system optimization problem by performing a collection of independent, simpler, optimizations for each decision variable. For games of common utility, convergence to an equilibrium can be established. Thus we are using the metaphor of competition to animate the individual elements of a system to evolve into a more efficient overall configuration. In our talk we will discuss computational advantages of applying this procedure to complex system optimization problems with “black-box” performance criteria lacking special structure: 1) the algorithm updates all decision variables independently and in parallel, thus making the approach scalable in the number of variables; 2) typically, only one computationally-intensive evaluation of the performance criterion is needed per iteration; 3) a type of convergence is ensured. We propose a computationally efficient implementation of the traditional fictitious play algorithm in which best replies are computed based on samples from historical distributions of players’ actions. We establish convergence results for this method. We also present insights into limiting behavior of the sequence of algorithm iterates. Finally, we demonstrate the performance of the algorithm in applications. These include a problem of finding system-optimal vehicle routes in congested dynamic traffic networks, and a problem of designing a communication protocol for mobile units keeping track of each other’s position during a military exercise.
*BENJAMIN KOO - Architecting Systems Under Uncertainty with Object-Process Networks
Author(s):
Benjamin Koo, MIT, USA
Annie-Pierre Hurd, MIT, USA
David Loda, MIT, Technion, USA, Israel
Dov Dori, MIT, Technion, USA, Israel
Edward F. Crawley, MIT, USA
Abstract:
Architects of complex systems and products, such as an aircraft power system must routinely make tradeoff decisions given limited resources, incomplete information, and evolving stakeholder needs. Their decisions ultimately require a certain amount of subjective judgment, as a result of which system architecting involves not just science and engineering, but also art. By embedding Bayesian belief propagation algorithms in Object-Process Methodology (OPM), we show that architects of large-scale complex systems can better describe, quantify and communicate system attribute tradeoffs under uncertainty. This new framework, Object-Process Network (OPN), is instrumental in formulating and preserving the rationale behind tradeoff decisions, thereby offering a systematic approach to system-related decision making. Object Process Networks achieve the following objectives: 1. Graphically compose component-level knowledge to help visualize complex interrelationships among variables in a system, 2. Model the space of architectural options constrained by deterministic rules or conditional probability functions between system variables, and 3. Compare and select preferred architectural options under uncertainty. To demonstrate the use of OPN in complex system architecting, we have applied this framework to an architectural tradeoff case study of an aircraft power system. A simplified architectural OPN model and its model construction process are presented and discussed in this paper.
*DANIEL D. FREY - Effect Sparsity, Heirarchy, and Inheritance: How System Structure Affects Robust Design of Engineering Systems
Author(s):
Daniel D. Frey, MIT, USA
Xiang Li, MIT, USA
Jagmeet Singh, MIT, USA
Effect sparsity, hierarchy, and inheritance are structural properties of engineering systems often discussed in the literature on statistical design of experiments and robust parameter design. Evidence is presented that these properties are common in engineering systems. A mathematical model is presented and used to explore the influence of these properties on the effectiveness of various strategies for robust parameter design.
Abstract:
Effect sparsity, hierarchy, and inheritance are structural properties of engineering systems often discussed in the literature on statistical design of experiments and robust parameter design. An investigation is described which quantifies these properties across a large sample of engineering systems. A mathematical model is presented and used to explore the influence of these properties on the effectiveness of various strategies for robust parameter design.
*JONATHAN R. A. MAIER - Understanding the Complexity of Design
Author(s):
Jonathan R. A. Maier, Clemson University, USA
Georges M. Fadel, Clemson University, USA
Abstract:
The powerful concept of complexity can be applied to help us understand not only modern engineering systems, but also the design of those systems, and artifacts in general. In this paper we attempt to establish a two-pronged theoretical framework for understanding the complexity of design. By design we mean the activity of designing artifacts in general, not any specific class of artifact. The first route to understanding the complexity of design is based on a fundamental exploration of what it means for a system to be complex. This avenue is essentially mathematical in character, and for it we rely heavily on the works of Robert Rosen, Nicholas Rashevsky, and Peter Wegner. Having discussed briefly the foundations of this approach, it is then applied to the science of design. In particular, the goal is to show that design in general is a member of the class of systems that are formally described as open and complex, and not a member of the class of systems that are formally described as closed and algorithmic. This amounts to theoretical validation for adopting a paradigm for using an open relational concept, such as affordance, as a basis for design, rather than a closed algorithmic concept such as function. This approach also suggests abstract affordance based descriptive models of design as alternatives to the current function based models of design. The second route to understanding the complexity of design lies in the study of systems that are in some obvious way complex. This approach is essentially empirical in character. Accordingly, the goal here is to show that design exhibits similar characteristics to other complex systems, in particular, as will be shown, a class of complex systems known as Complex Adaptive Systems (CAS). This constitutes more validation for using a relational as opposed to an algorithmic concept as a basis for design. Also, this suggests that design may be modeled in the same way as other CAS, i.e., in accordance with a cycle in which other CAS are known to operate In place of algorithms, what is needed for complex systems are structures which are semantically rich and open to interactions. For biology, such a formalism, once he realized such a thing was necessary, was invented by Rashevsky in the form of relational models [Rashevsky 1960], to replace the earlier simplistic machine metaphor dating back to Descartes. For computer science, such a formalism was invented by Wegner in the form of interaction machines [Wegner 1997, 1998], to replace the much more restrictive and simplistic Turing machines (e.g., [Turing 1959]). For design, we propose that the appropriate formalism is that of affordance [Gibson 1979; Maier and Fadel 2001a, 2002, 2003], rather than the much more restrictive and simpler concept of function, which is very similar to that of algorithm, as explained in this chapter. The concept of affordance, thus grounded theoretically in the first approach to design complexity (the relational approach), agrees very well with the empirical model for design complexity suggested by the CAS-type approach. The integration of the concept of affordance into the CAS-inspired model for design thus concludes this paper.
PELIN GUVEN - SEPARATION OF REAL WORLD MIXED ACOUSTIC SIGNALS BY USING FREQUENCY DOMAIN APPROACH
Author(s):
Pelin Guven, Anadolu University, Turkey
Emin Germen, Anadolu University, Turkey
Abstract:
Blind Source Separation (BSS) aims to estimate the original source signals using only the information that are observed from each input channels as mixed signals. In acoustics, cocktail-party problem is a well known problem where the multipath effect and reverberation that occur during the propagation of source signals should be considered in data model. Taking these into account, received signals at an array of microphone are real-world superposition of audio source signals by a mixture of delayed and filtered versions of source signals. So in this model mixtures are convolutive mixtures of sources.In this paper we present a method to separate and deconvolve real-world mixtures recorded in reverberating environment.In this work, while updating the unique frequency bins using off-line algorithm, the Amari’s updating rule has been used. However while using this rule, the updating scheme has been modified so that, after updating a filter weight for specific bin, this weight is also used to update those bin itself.

*DAN COORE - Nonlinear Dynamics and Pattern Formation

*BHARAT KHUSHALANI - Polyhedral Pattern Formation
Author(s):
Bharat Khushalani, University of Southern California, USA
Abstract:
The problem of stable configurations of N electrons on a sphere minimizing the potential energy of the system is related to the mathematical problem of extremal configurations in distance geometry and to the problem of densest lattice packing of congruent closed spheres. Arrangement of points on a sphere in three-space leading to equilibrium or periodic solutions has been of interest since 1904 when J. J. Thomson tried to obtain stable equilibrium patterns of electrons moving on a sphere and subject to electrostatic force inversely proportional to square of the distance between them. The geometric problem of determining largest diameter of n equal non-overlapping circles on a sphere (Tammes problem) is related to the dynamic problem of periodic orbits on a sphere. Coulomb energy for three dimensional configurations with charges partitioned among vertices of regular polygons parallel to the equatorial plane shows optimal trends when polygonal faces are triangular, a result which is identical to the solution of densest packing of equal circles on a sphere. Observation of stable vortex patterns in rotating superfluid He has led to the numerical study of stationary vortex patterns in two dimensions using point vortex theory. Utilizing this point vortex theory, we will show that the problem of such vortex patterns on a sphere is related to the problem of extremal configurations and obtain polyhedral patterns and periodic vortex orbits on a sphere using numerical methods.
*ILAN HARRINGTON - Design and robustness of delayed feedback controllers for discrete systems
Author(s):
Ilan Harrington, Duke university, USA
Socolar Joshua E. S., Duke university, USA
Abstract:
We study a matrix form of time-delay feedback control in the context of discrete time maps of high dimension. In almost all cases where standard proportional feedback control methods can achieve control, time-delay feedback controllers containing only static elements can be designed to achieve identical linear stability properties. Analysis of an example involving a ring of coupled maps that can be controlled at only two sites demonstrates that the time-delay controller equivalent to a standard optimal controller can be equally robust in the presence of noise, except at special points in parameter space where the uncontrolled system has a mode with Floquet multiplier exactly equal to 1. Numerical simulations confirm the results of the analysis.
*SATISH T.S. BUKKAPATNAM - Complex Nonlinear stochastic dynamics of precision grinding operations: Implications for health monitoring and control
Author(s):
Satish T.S. Bukkapatnam, University of Southern California, USA
Abstract:
Precision grinding is an important manufacturing process in aerospace industries as many of the industries' core technologies such as air bearings and electromechanical systems rely on this process. Owing to the distributed thermomechanics and deformation mechanisms coupled with the complex interactions between these mechanisms and the dynamics of various components of the grinding machine, controlling the process performance is challenging. Aerospace industries call this process a "black magic." Our research has characterized the nonlinear stochastic dynamics underlying the various behaviors of the process from sensor signals measuring structural vibrations and acoustic emission. We have derived a novel appraoch to reconstruct a nonlinear stochastic differential equation model from a multitude of machine structure-mounted vibration sensor signals. The model can capture the various observed behaviors of the process, and it was computationally tractable for real-time control. A 62% improvement in the control of performance was achieved by tracking features extracted based on this model.
REZA RASTEGAR - Chaotic Dynamics of Cellular Learning Automata
Author(s):
Reza Rastegar, Amirkabir University of Technology, Iran
Mohammad Reza Meybodi, Amirkabir University of Technology
Abstract:
In this Paper, we study Chaotic Dynamics of Cellular Learning Automata(CLA). CLA is a mathematical model for dynamical complex systems that consist of large number of simple components. the simple components, which have learning capability, act together to produce complicated behavioral patterns. A CLA is a Cellular Automata in which a Learning Automaton(LA) is assigned to its every cell. The Learning automaton residing in particular cell determines its state on basis of its action probability vector. Like Cellular Automata, there is a rule that CLA operate under it. The rule of CLA and the actions selected by the neighboring LAs of any particular LA determine the reinforcement signal to the LA residing in that cell.In CLA, the neiboring LAs of any particular LA constitute its local enviroment, which is nonstationary because it varies as action probability vectors of neighboring LAs vary.
*DAVID GOMEZ MIGUEZ - Directional grow in chemical and biological pattern formation systems
Author(s):
David Gomez Miguez, Universidade de Santiago de Compostela, Spain
Milos Dolnik, University of Brandeis, USA
Alberto Pérez Muñuzuri, Universidade de Santiago de Compostela, Spain
Abstract:
The great variety of natural systems display spontaneous symmetry breaking behaviour. Currently, one of the most extensively studied process, due to its apparent simplicity and capability of direct visualization in live organisms, is the pigmentation skin of fishes. Microscopic studies and mutation experiments reveal the importance of wave-like manner in which pigment cells travel through the embryo in the first days of development. The pattern exhibited by the skin of the fish may adopt very diverse spatial configurations. Even in some cases, the same species may exhibit different spatial arrangement of the patterns depending on the environmental conditions. This process results, essentially, in an elongating tissue covering the fish dermis at the expense of a pigment cells-free zone. A simple chemical reaction-diffusion system may mimic the diverse spatial arrangement of the fish skin pigmentation. This paper presents experimental results with a purely chemical reaction-diffusion system demonstrating that the velocity of a growing media can determine the spatial configuration in a pattern forming system. It provides an explanation for recent biological studies in the process of fish skin patterning.
*ANDREW WUENSCHE - Self-reproduction by glider collisions
Author(s):
Andrew Wuensche, Discrete Dynamics Lab, Univ. of Sussex, and Univ of the West of England, USA and UK
Abstract:
We present a 3-value cellular automaton which supports self-reproduction by glider collisions. The complex dynamics emerge spontaneously in both 2d and 3d according to the 6-neighbor, k-totalistic, ``beehive'' rule; the 2d dynamics on a hexagonal lattice is examined in detail. We show how analogous complex rules can be found, firstly by mutating a complex rule to produce a family of related complex rules, and secondly by classifying rule-space by input-entropy variance. A variety of complex rules opens up the possibility of seeking a common thread to distinguish those few rules from the rest: an underlying principle of self-organization? A paper and further results are on line at http://www.cogs.susx.ac.uk/users/andywu/multi_value/self_rep.html
*MADALENA DAMASIO COSTA - Multiscale entropy analysis of complex physiologic time series: Information loss with aging and disease
Author(s):
Madalena Damasio Costa, Beth Israel Deaconess Medical Center, Harvard Medical School, USA
Ary L Goldberger, Beth Israel Deaconess Medical Center, Harvard Medical School, USA
C-K Peng, Beth Israel Deaconess Medical Center, Harvard Medical School, USA
Abstract:
There has been considerable interest in quantifying the complexity of physiologic time series, such as heart rate. Measurements of complexity have potentially important applications both with respect to evaluating dynamical models of biologic control systems and to bedside diagnostics. For example, a wide class of disease states, as well as aging, appears to degrade physiologic information content and reduce the adaptive capacity of the individual (1). Loss of complexity, therefore, has been proposed as a generic feature of pathologic dynamics (1,2). However, traditional algorithms indicate higher complexity for certain pathologic processes associated with random outputs than for healthy dynamics exhibiting long-range correlations. This paradox may be due to the fact that conventional algorithms fail to account for the multiple time scale inherent in healthy physiologic dynamics. We introduce a method to calculate multiscale entropy (MSE) for complex time series (3). We find that MSE robustly separates healthy and pathologic groups and consistently yields higher values for simulated long-range correlated noise compared to uncorrelated noise. Our finding is compatible with the unifying concept that physiologic complexity is fundamentally related to the adaptive capacity of the organism, which requires integrative, multiscale functionality. In contrast, disease states, as well as aging, may be defined by a sustained breakdown of long-range correlations and loss of information (2-4). Finally, we note that the MSE method has potential applications to studying a wide variety of other physiologic and physical time series. 1. Lipsitz LA, Goldberger AL. Loss of "complexity" and aging: potential applications of fractal and chaos theory to senescence. JAMA 1992;267:1806-1809. 2. Goldberger AL, Peng C-K, Lipsitz LA. What is physiologic complexity and how does it change with aging and disease? Neurobiol Aging 2002;23:23-26. 3. Costa M, Goldberger AL, Peng C-K. Multiscale entropy analysis of complex physiologic time series. Phys Rev Lett 2002;89:068102. 4. Goldberger AL, Amaral LAN, Hausdorff JM, Ivanov PCh, Peng C-K, Stanley HE. Fractal dynamics in physiology: alterations with disease and aging. Proc Natl Acad Sci USA 2002;99[suppl 1]:2466-2472.

Evolution and Ecology

*CHIN-KUN HU - Quantum Spin Systems as Models for Biological Evolution
Author(s):
Chin-Kun Hu, Academia Sinica (Taipei), Taiwan
David Saakian, Yerevan Physics Institute, Armenia
Abstract:
In this paper, we will give a brief review about our recent work on the dynamics of biological evolution . Based on the connection between the Eigen model and the Crow-Kimura model of biological evolution and quantum spin systems, we study both static and dynamics of the evolution models and find that relaxation in the parallel scheme of the Crow-Kimura model is faster than that in the connected scheme of the Eigen model.
*JAVIER A. ALCAZAR - A Multi Agent Based Approach to the Multi-Predator Multi-Prey Pursuit Domain
Author(s):
Javier A. Alcazar, Cornell University, USA
Ephrahim Garcia, Cornell University, USA
Abstract:
We present a different approach to a class of pursuit games: the Multi-Predator Multi-Prey domain. In the typical game, a group of predators tries to capture a group of preys, and all the agents have perfect knowledge of the prey and predator positions. In our problem definition the prey-agent and the predator-agent have only local information provided by its vision range, each predator-agent independently tries to capture a prey-agent in a one-predator-one-prey-pair way. The predator-prey-pair capture is not known in advance and both the predators and the preys are moving in the environment. We show that simple greedy local predator-agent rules are enough to capture all the prey-agents.
*MARGARETA SEGERSTAHL - Coupling sexual reproduction and complex multicellularity
Author(s):
Margareta Segerstahl, Helsinki University of Technology, Finland
Abstract:
Understanding why multicellular organisms reproduce predominantly by sexual means has remained a major problem in evolutionary theory. A related question is the evolutionary origin of germ cell dimorphism, oogamy being the most extreme example. By using the concept of facultative (optional) sexuality and the simple dualism of cellular reproduction versus functional cell differentiation, it became possible to combine multicellularity, sexual reproduction, and oogamy in a novel way. The result is a network model in which developmental and evolutionary information merge. The model provides a formal framework for logical integration of theory and experimetal data regarding both evolutionary and developmental biology of complex multicellularity.
*DANIEL SOLOW - Mathematical Models for Explaining the Emergence of Specialization in Performing Tasks
Author(s):
Daniel Solow, Case Western Reserve University, USA
Joe Szmerekovsky, North Dakota State University, USA
Abstract:
In an evolving community consisting of many individuals, it is often the case that the individuals tend, over time, to become more specialized in performing the tasks necessary for survival and growth of a community. This work presents a collection of linear and nonlinear models that provide insights as to when and why this "functional specialization" emerges in general, rather than specific, settings. The results from these models, which are based on an evolutionary approach, apply to communities in which individuals allocate their time in the best interest of the community as a whole.
*ERIK RAUCH - Long-range interactions and evolutionary stability in predator-prey systems
Author(s):
Erik Rauch, NECSI, MIT, USA
Abstract:
It is now becoming recognized that spatial separation is crucial to many ecological and evolutionary processes. At the same time, many natural systems also have some long-range mixing (that is, they are "Small-World networks"). This raises the question of how evolutionary systems are able to persist, since long-range interactions tend to counteract the effects of spatial separation. We illustrate the effect of long-range interactions on evolving systems using a simple model of predators evolving to avoid overexploiting their prey. In the model, even though overexploiting predator strains have a short-term advantage, they go extinct over time, due to the local depletion of prey, before dominating the system. The evolutionary steady state in which predators are prevented from overexploiting their prey can be disrupted by the addition of long-range links. However, spatial behavior can remain even with a high density of long-range links. This is clarified using recent results from network theory. The results suggest that the addition of long-range interactions may destabilize an evolutionary system, even if that system already contains a significant density of such interactions. Systems with larger spatial scale are more sensitive to the addition of long-range links.

*YING-CHENG LAI - Physical Systems

*PABLO I. HURTADO - Escape from Metastable States in a Nonequilibrium Environment
Author(s):
Pablo I. Hurtado, Boston University, USA
Joaquin Marro, Universidad de Granada, Spain
Pedro L. Garrido, Universidad de Granada, Spain
Abstract:
We review in this paper some results on metastability in a two-dimensional Ising ferromagnet relaxing toward a nonequilibrium steady state. Nucleation in this case may be understood in terms of a nonequilibrium free energy, which predicts Noise Enhanced Stability of the metastable state for low temperature in a nonequilibrium environment. This is a consequence of the anomalous, non-monotonous temperature dependence of the nonequilibrium surface tension. In addition, when subject to both open boundaries and nonequilibrium noise, the metastable system decays via well-defined avalanches. These exhibit power-law size and lifetime distributions. We expect some of these results to be verificable in actual (impure) specimens.
*WM. C. MCHARRIS - Chaos as a Bridge between Dewterminism and Probability in Quantum Mechanics
Author(s):
Wm. C. McHarris, Michigan State University, USA
Abstract:
Ever since the Einstein-Bohr debates about the completeness of quantum mechanics, various physicists (Bohm, de Broglie, i.a.) have tried to reconcile the probabilistic aspects of quantum mechanics with their desire for a fundamentally deterministic view of nature. In so doing, they have toyed with and skirted around ideas that we now know to arise naturally from nonlinear dynamics and chaos [Wm.C. McHarris, J. Opt. B: Quantum Semiclass. Opt. 5, S442 (2003); Chapter in Progress in Quantum Physics Research, Nova Science Publ., to be publ. (2004); and refs. therein]. Not having access to chaos theory, they mostly came up with somewhat contrived “hidden variable” notions such as pilot waves and the theory of the double solution, forced upon them by basically linear formulations. Recently a number of quantum mechanical “imponderables,” such as attaining an exponential decay law for quantum systems of identical particles or reconciling Bell's theorem with classical mechanics, have beem found to have parallel mock-ups or explanations in terms of the novel idea that chaotic effects could possibly be fundamental to quantum mechanics. Perhaps chaos could provide a bridge between the determinism do dear to Einstein’s heart and the probability of the Copenhagen interpretation of quantumn mechanics. And it could achieve this without having to resort to artifices such as hidden variables. Conceivably Einstein and Bohr both could have been right in their interpretations of quantum mechanics. ˇ
*PIERRE EVESQUE - Efficient reduction of complexity and non ergodicity due to dissipation: the case of a single ball in a vibrating box
Author(s):
Pierre Evesque, CNRS, Ecole Centrale de Paris, FRANCE
Garrabos Yves, CNRS, ICMCB, FRANCE
D. Beysens, CEA, FRANCE
F. Palencia, CNRS, ICMCB, France
Abstract:
We report observations on balls, vibrated in a box, showing a coherent behavior along a direction parallel to the vibration. This behavior causes a significant reduction of the phase space dimension from 11-d to 2d of this billiard-like system. We believe this is because the lowest dissipation process along a non ergodic orbit that eliminates ball rotation and freezes transverse velocity fluctuations. From a two-ball experiment performed under zero-g conditions, we introduce a ball system moving coherently as a prototype of a new dynamical model for a non-interacting dilute granular gas. This behaviour is completely different from the one without dissipation, that is named Fermi case.
*DAVID SAAKIAN - Universality classes of complexity
Author(s):
David Saakian, Yerevan Physics Institute, Armenia
Abstract:
We calculate the free energy of the Random Energy Model at the transition point between ferromagnetic and spin glass phases. At this point, equivalent to the decoding error threshold in optimal codes free energy has finite size corrections proportional to the square root of the number of degrees. The response of the magnetization to the external ferromagnetic is maximal at the values of magnetization equal to half. We give three criteria of complexity and define different universality classes. According to our classification at the lowest class of complexity are: Markov Models, maps, Hidden Markov Models. In a higher level are critical theories, percolation, self organized criticality (SOC). The next higher class involves: HOT design, error threshold in optimal coding, language and, maybe, financial market. Perhaps alive systems are also related with the last class.
*RICHARD METZLER - Information flow through a chaotic channel : prediction and postdiction at finite resolution
Author(s):
Richard Metzler, NECSI, MIT, USA
Yaneer Bar-Yam, NECSI, USA
Mehran Kardar, MIT, USA
Abstract:
Amplification of small perturbations to macroscopic scales is a fundamental property of chaotic systems, but other processes like phase space shrinkage and folding also play a role if one wants to make statements about the future or past of a dynamic system. Using the logistic map as an example, we calculate the contributions of each process in an information-theory framework as a function of the amplification parameter, the number of iterations and the resolution of measurement. We also discuss the consequences for communication through nonlinear channels.

6:15PM-7:45PM BANQUET

6:50PM-7:30PM Global Social Systems and the Prevention of Tragic Conflict and Human Suffering

*ROBERT S. MCNAMARA - Interview: Lessons from Experience

7:45PM-9:30PM BANQUET SESSION

*KEITH CAMPBELL - Cloning Dolly

THURSDAY, May 20

9:00AM-12:20PM ENGINEERING & INNOVATION

*ERIC BONABEAU - Engineering & Innovation

*JERRY SUSSMAN - Engineering complex systems
*DOUGLAS O. NORMAN - Complexity in the engineering of the Air and Space Operations Centers: Enterprise Engineering
Author(s):
Douglas O. Norman, The MITRE Corporation, USA
Abstract:
This paper discusses the observations, implications, and actions taken in the formulation and evolution of the United States' Air & Space Operations Centers by those responsible for its engineering. We've come to understand that the Air & Space Operations Centers (AOCs) are complex systems which have more in common with enterprises than with simple systems. In this context enterprise doesn’t mean “big organization,” and complex doesn’t mean “difficult to understand.” In the face of monumental development failures, today’s engineers with responsibility to engineer for, or fit into, an enterprise are struggling to scale-up their traditional system engineering approaches to deal with the Enterprise. Poorly understood and unable to be specified, the notion of the enterprise and the implications for its engineering are actually causing yet more failures. An enterprise is a complex system [Liles 95; Norman 03; Kuras 03] which requires a different way to think and to act in order to engineer it successfully; and it’s not through our traditional Systems Engineering methodologies. To paraphrase Holland [95]: with a careful configuration management plan, detailed understanding of requirements, and well-crafted contracts in place, we can expect complex systems to do pretty much as they damn well please. This is not the environment to apply traditional systems engineering. Rooted in linear systems theory and requiring detail and stability, traditional systems engineering can not come to grips with the non-linearity and continuous change which characterizes Enterprises; and things promise to get more, not less, complex. The world is undergoing substantive, fundamental changes as it moves from an industrial age (focusing on engineering issues of higher scales – repeatability, manufacturability, justifiability) to an information age (focusing on engineering issues of connectivity, discovery, emergence, fitness, evolution, innovation, adaptation, and specialization). We interpret Complex Systems Theory and apply it to the AOC as the fundamental approach to Enterprise Engineering.
*BUD MISHRA - VALIS or VANISH... (a survivor's guide to computational and systems biology?)
Author(s):
Bud Mishra, Courant Institute, & Cold Spring Harbor Lab, USA
Abstract:
I will discuss several computational tools within our Valis environment that have been developed to answer questions arising in computational and systems biology. I will illustrate power of these tools through examples based on models of genome evolution and biochemical networks. In particular, I will focus on the challenges in systems biology, algorithm design and mathematical modeling that make these problems interesting to biologists, biotechnologists, computer scientists and applied mathematicians. I will also introduce the concept of algebraic model checking systems to reason about biological circuits (natural or engineered), and how we use them to interpret experimental data and as design rule checkers.
*CARLISS BALDWIN - Design rules

2:00PM-5:00PM AFTERNOON BREAKOUT SESSIONS

*DAVID H. WOLPERT - Collectives

*BILL MACREADY - Experimental tests of product distribution theory
Author(s):
David H. Wolpert, USA
William Macready, NASA, USA
Abstract:
This paper presents product distribution theory, a powerful new framework for analyzing and controlling distributed systems. There are many ways to motivate product distribution theory. It is the information-theoretic extension of conventional full-rationality game theory, to the case of bounded rational players. It can also be viewed as a modification of the Lagrangian formulation of statistical physics in which the variables of the system are required to be independent. It also provides an alternative to conventional optimization, in which rather than use probability distributions to help in a search for an optimal point in a space, one reformulates the problem as a search for an optimal probability distribution over that space. The final motivation of the framework is as the way to optimally approximate a provided probability distribution with a lower-dimensional distribution. This framework has potential applications in many engineering domains: (constrained) optimization, distributed adaptive control of multi-agent systems, sampling of probability densities, density estimation, numerical integration, information-theoretic bounded, and reinforcement learning. It also has many purely scientific applications, for example in the sciences of rational game theory, population biology, and management theory. After introducing product distribution theory this paper presents computer experiments validating its utility for several of the engineering applications listed above.
*NEIL F. JOHNSON - Network Engineering and Evolution Management: theory and practice
Author(s):
Neil F. Johnson, Oxford University, UK
Sehyo C. Choe, Oxford University, UK
Sean Gourley, Oxford University, UK
David Smith, Oxford University, UK
Pak Ming Hui, Chinese University of Hong Kong, Hong Kong
Abstract:
We review our group's efforts in understanding the functional and dynamical role of network connections in populations of agents with bounded rationality. Each agent represents a node and hence takes actions based on both global and local information. This work takes three forms: (i) analytic, centering around the Crowd-Anticrowd Theory which accounts for multi-agent (i.e. multi-node) correlations, (ii) numerical simulations, and (iii) empirical studies in biological and socio-economic domains. An recent example of the analytic and numerical work has focused on the effect of network connections within a frustrated system where heterogeneous, adaptive agents compete for limited global resources. Whether the network connections turn out to be beneficial or detrimental, is found to depend quite dramatically on the global resource level.
*JEFF SHAMMA - Multiagent Repeated Games and Convergence to Nash Equilibria
Author(s):
Gurdal Arslan, UCLA, USA
Jeff S. Shamma, UCLA, USA
Abstract:
Consider a scenario in which multiple decision making agents repeatedly play a matrix game and adjust their strategies according to observations of each other's actions. The game is noncooperative in that each agent may have its own objective/utility function, and these objectives are not shared among agents. A central issue is whether agent strategies will converge to a Nash equilibrium. Prior work shows how convergence to a Nash equilibrium in this setting may or may not occur. This talk presents new strategic update mechanisms that can lead to convergent behavior in previously nonconvergent cases, such as the Shapley and Jordan counterexamples, through the use of fundamental feedback control concepts.
*ILAN KROO - Collectives, Optimization, and Distributed Design
Author(s):
Ilan Kroo, Stanford University, USA
Abstract:
This paper addresses some of the issues and requirements for distributed engineering design, summarizes current approaches, and explores the application of collectives to this problem. Examples in aerospace engineering are used to highlight the opportunities and challenges for distributed optimization.
*STEFAN BIENIAWSKI - Using Product Distributions for Distributed Optimization
Author(s):
Stefan Bieniawski, Stanford University, USA
David H. Wolpert, NASA Ames Research Center, USA
Abstract:
With connections to bounded rational game theory, information theory and statistical mechanics, Product Distribution (PD) theory provides a framework for performing distributed optimization. This paper will present the mathematical foundations and the details of the algorithm used to perform optimization. The focus will be on understanding the mechanisms which are achieving the coordination and on highlighting the flexibility of the approach. The technique will be demonstrated on a variety of unconstrained and constrained optimization problems. The talk will also include comparisons with two forms of distributed reinforcement learning inspired optimization approaches, parallel and serial Brouwer updating. The inter-relationship of the techniques will be discussed.

*MARLENE WILLIAMSON - Engineering Systems

*DAVID ALDERSON - The Role of Design in the Internet and Other Complex Engineering Systems
Author(s):
David Alderson, California Institute of Technology, USA
Lun Li, California Institute of Technology, USA
Walter Willinger, AT&T Labs - Research, USA
John Doyle, California Institute of Technology, USA
Abstract:
The Internet offers an attractive case study of a complex network, since our understanding of the underlying technology together with the ability to perform detailed measurements means that most conjectures about its large-scale properties can be unambiguously resolved, though often not without substantial effort. A fundamental challenge in the study of the Internet and other complex systems is to identify and understand the relationship between large-scale features and their underlying mechanisms. One popular approach leverages the tools of statistical physics to emphasize emergent properties of random ensembles, typically with constraints on macroscopic statistics. The main focus is on generic configurations whose construction is governed by randomness and are likely to occur by chance. A recent example of this perspective when applied to the Internet are the popular scale-free models of network connectivity graphs. These models claim that the emergence of power laws in the distribution of node degrees in these graphs is essentially explained by a random process that involves preferential attachment and reveals what has become known as the ``Achilles' heel of the Internet''--the presence of a few highly connected hubs within the core of the network that, when disabled by targeted attacks, will bring the Internet to its knee. An alternative perspective, motivated by engineering, suggests that nonrandom design rather than randomness plays a primary role in the construction and evolution of complex systems. The emphasis here is on non-generic, highly engineered configurations that are extremely unlikely to occur by chance, and the complex structure of highly engineered technology and of biological systems is viewed as the natural by-product of tradeoffs between system-specific objectives and constraints. This paper shows how and why the latter view, when applied to the study of router-level Internet connectivity, results in conclusions that are fully consistent with the real Internet, but are the exact opposite of what the scale-free models claim. The reasons for reaching such divergent conclusions about one and the same system go well beyond the Internet and scale-free models and are endemic in the application of ideas from statistical physics to problems in engineering, where it is assumed that the details related to a complex system's design, functionality, constraints, and evolution (i.e., all ingredients that make engineering different from physics) can be safely ignored in favor of random ensembles and their emergent properties.
*FRED M. DISCENZO - Dynamic Reconfiguration of Complex Systems to Avoid Failure
Author(s):
Fred M. Discenzo, Rockwell Automation, USA
Francisco P. Maturana, Rockwell Automation, USA
Pavel Tichý, Rockwell Automation, Czech Republic
Petr Šlechta, Rockwell Automation, Czech Republic
Jan Bezdicek, Rockwell Automation, Czech Republic
Raymond J. Staron, Rockwell Automation, USA, Kenwood H. Hall, Rockwell Automation, USA, Vladimír Marík, Rockwell Automation, Czech Republic,
Abstract:
There are increasing pressures for low-cost, reliable automation systems even though system applications are becoming increasingly complex and deployed in a broader range of critical applications. Advanced automation techniques employing intelligent agent technology promises to provide important capabilities for managing complexity and to meet critical application requirements for operating performance, fault-tolerance, reliability, and survivability. Multi-agent Systems in a Distributed Artificial Intelligence (DAI) framework provides important new capabilities to enhance automation system performance across a large class of applications. A multi-agent system approach encapsulates the fundamental behavior of intelligent devices as autonomous components. These components exhibit primitive attitudes to act on behalf of equipment or complex processes to realize local agent goals as well as agent cluster goals or system-level overarching goals. Goals may emerge dynamically and are agreed upon by the agents through negotiation. For example, through agent collaboration (i.e. distributed diagnostics) a leaking pipe, degraded bearing or worn pump impellor may detected. This may establish a new goal to reconfigure the flow path and the associated controllers to avoid using these components under extreme conditions. The sequence of actions required to establish the required sub-goals and transition to the new operating regime is performed automatically through agent collaboration. Using this approach, we have implemented an agent-based chilled water system modeled after a shipboard system. This laboratory system is comprised of over 50 valve, load, and pump agents and operates in a highly distributed framework. There is no central controller and the system has been shown to dynamically establish new goals and automatically re-configure system operation to minimize damage and to meet critical cooling needs. New operating goals may emerge based on equipment prognostics or predicted component failure to avoid reaching a predicted or probable state that is undesirable (e.g. catastrophic component failure). The potential undesirable states may be efficiently avoided while continuing to satisfy critical system needs (e.g. radar cooling). This system serves to validate the agent methodology to manage the inherent complexity of highly distributed systems while responding dynamically to changes in operating requirements, degraded or failed components through prognostics, and dynamic re-configuration.
DARIO MANCINI - Design, development, management and social organization of new very large scientific plants. The case study of the GMT (Giant Modular Telescope)
Author(s):
Dario Mancini, CSAMI - Complex System and Advanced Management Institute + ATEC Robotics - Advanced Technologies for Research and Management + TWG - INAF - Astronomical Observatory of Capodimonte - Italy, Italy
Abstract:
Modern scientific and technological projects are increasingly in competition over scientific aims, technological innovation, increased performance, reduced time and costs. They require a dedicated and innovative organization able to satisfy contemporarily all technical, managerial and logistic constraints, thus ensuring the final user's expectations. In order to satisfy all the above, the management has to be strategically innovative and intuitive by removing first and foremost the bottlenecks that turn out to be - more often than not only at the end of the project - the causes of general dissatisfaction. More than 25 years spent working on complex multidisciplinary systems in scientific, technological and industrial projects have given the author the opportunity to study, test, optimize and validate strategies for parallel system engineering, integrated design, feedback modeling and project management. They are seen as merged in a sort of unique optimized task, using the newly-coined word "Technomethodology". Starting from one case study in the field of astronomy projects, and in particular from the 100-m extra large Giant Modular Telescope proposed by CSAMI Institute, the author shows and describes how the schematization of such complex dynamic system does not actually involve only technical and managerial aspects. Consequently the project strategy should also take into account the impact and the relationships of such installation with several aspects of social life, environment, politics, science, alternative energies utilization, migration of know-how towards companies, investment plans, new perspectives on technology and other matters. These aspects are generally neglected by such plants, whereas GMT always takes all of them into due accoun
*CARLOS GERSHENSON - Protocol Requirements for Self-organizing Artifacts: Towards an Ambient Intelligence
Author(s):
Carlos Gershenson, Vrije Universiteit Brussel, Belgium
Francis Heylighen, Vrije Universiteit Brussel, Belgium
Abstract:
We discuss which properties common-use artifacts should have to collaborate without human intervention. We conceive how devices, such as mobile phones, PDA’s, and home appliances, could be seamlessly integrated to provide an “ambient intelligence” that responds to the user’s desires without requiring explicit programming or commands. While the hardware and software technology to build such systems already exists, yet there is no protocol to direct and give meaning to their interactions. We propose the first steps in the development of such a protocol, which would need to be adaptive, extensible, and open to the community, while promoting self-organization. We argue that devices, interacting through “game-like” moves, can learn to agree about how to communicate, with whom to cooperate, and how to delegate and coordinate specialized tasks. Like this, they may evolve distributed cognition or collective intelligence able to tackle any complex of tasks.
*TAESIK LEE - Fundamental Long-Term Stability Conditions for Design of Complex Systems: Equilibrium and Functional Periodicity
Author(s):
Nam P. Suh, Massachusetts Institute of Technology, USA
Abstract:
All matter – subatomic particles to human beings -- that exist in nature owe their existence to their long-term stability. The complexity theory presented by Suh in recent years shows that for long-term stability, both engineered systems and natural systems must be either at a stable equilibrium state or must have a functional periodicity. In engineering and physics, the former, i.e., equilibrium, is well known, but the concept of functional periodicity has been introduced only recently. There are many different kinds of Functional Periodicity that govern natural and engineered systems. The performance of engineered systems has been improved by introducing a functional periodicity by design. Nature has evolved based on the stability provided by equilibrium states or by having functional periodicity. Classical physics such as Newtonian mechanics and thermodynamics are based on the assumption that equilibrium states exist. The basic postulates of modern physics such as quantum mechanics and superstring theory are consistent with the proposed existence of functional periodicity in natural systems. The particle/wave duality of matters that forms the basis of quantum mechanics can be explained in terms of the stability criterion presented in this paper rather than in terms of the probability argument presented in the past.
*JORGE FINKE - The Ecological Ideal Free Distribution and Resource Allocation in Distributed Computing and Control: Theory and Cross-Fertilization for Applications
Author(s):
Jorge Finke, The Ohio State University, USA
Kevin M. Passino, The Ohio State University, USA
Abstract:
The ideal free distribution (IFD) concept from ecology characterizes how animals optimally distribute themselves across habitats. The word "ideal" refers to the assumption that animals have perfect sensing capabilities for determining habitat quality. "Free" indicates that they can move from any habitat directly to any other habitat at any time. If an animal perceives one habitat as "better," via some correlate of fitness such as rate of arrival of nutrients, it will move to it. This movement will, however, reduce its new habitat's desirability, both to itself and other animals at that habitat. The IFD is the equilibrium distribution where all animals achieve equal fitness. Suppose that in a distributed computing system there are spatially-distributed computing "nodes" that are interconnected by a computer network, and a continuous input of tasks to the nodes that need to be processed. Suppose that there are task-processing "software agents" that can move about the network and seek to allocate their processing services to nodes in order to optimize the overall task processing rate for the distributed computing system. This can occur via distributed "resource allocation" algorithms for moving software agents around the network to respond to task-processing demands. In this paper we first establish an analogy where we view both animals and software agents as generic agents, nutrients as tasks, and habitats as nodes. The computer network topology can represent which other nodes (habitats) an agent can sense at each node (i.e., "perceptual constraints"), and travel constraints that dictate which nodes (habitats) can be traveled to from a given node (habitat). Network delays can represent agent movement constraints (e.g., agent velocity limits coupled with inter-habitat distances). We specify a mathematical "discrete event system model" of the generic problem by generalizing models of the "load balancing problem" in distributed computing. We show how an "invariant set" can represent the IFD, and generalizations of it that result when the "ideal" and "free" assumptions are lifted. We then use Lyapunov stability analysis of this invariant set to illustrate that there is a wide class of agent strategies (i.e., "proximate" decision-making mechanisms), and resulting agent movement trajectories across nodes (habitats), that still achieve the desired distribution. The results extend the existing theory of the IFD by showing the impact of a class of perceptual constraints, travel constraints, movement trajectories, and animal strategies on achievement of the distribution. Next, we will show via simulations how the ideas apply to cooperative control of a network of autonomous vehicles when vehicles must be allocated to spatially-distributed tasks.

Networks / Systems Biology

*JANET WILES - Mapping biology onto computation: modelling and analysis of artificial genetic regulatory networks
Author(s):
Janet Wiles, The University of Queensland, Australia
Abstract:
Network computation and evolutionary computation provide analytic tools to explore the fundamental computational properties of biological systems, abstracting away from the myriad details of real biology. The value of such computational modelling depends on matching the level of abstraction to the research aims of the project. Network analysis tools have been widely used to model genetic regulatory networks at a variety of levels from biologically detailed to abstract Boolean models. In this project we are investigating how models of genetic regulatory networks can be derived from artificial genomes represented as nucleotide sequences. We report simulations mapping an artificial genome to a network, and the application of such genetic networks to the ontogeny of organisms.
*L. M. ROCHA - Extraction and Semi-metric Analysis of Social and Biological Networks
Author(s):
L. M. Rocha, USA
Abstract:
We discuss the extraction of social networks from co-occurrence data in several electronic resources such as the World Wide Web, as well as the extraction of networks of genes and other biological entities from both gene expression experiments and collections of electronic documents. These associative networks are represented as weighted graphs whose edges denote degrees of proximity which we compute as a co-occurrence probability. Using an inverse function, from the proximity graphs we produce distance graphs. We show that most distance graphs obtained this way violate the triangle inequality expected of Euclidean distances. Non-Euclidean distance functions are known as a semi-metric. We show that the semi-metric behavior of these distance graphs, can be used for identifying specific implicit associations in the graph, and thus useful to identify trends in communities associated with the sets of documents from where associations were extracted. In this paper we describe our work on inferring relevant associations from, as well as characterizing, semi-metric distance graphs. We present the idea of semi-metric distance graphs, and introduce ratios to measure semi-metric behavior. The discussion is based on empirical evidence from different sources such as a large database of scientific publications associated with the Active Recommendation Project at the Los Alamos National Laboratory (http://arp.lanl.gov), collections of reports about terrorism (used to build networks of terrorists), a web-site devoted to interdisciplinary science (the Principia Cybernetica Project web site http://pespmc1.vub.ac.be/), biomedical collections of publications, data from word free association experiments, and random distance graphs. Finally, we discuss how loosening the metric requirement of distance graphs extracted from document collections, results in a methodology capable of both discovering important associations for recommendation algorithms and quantifying the completeness and amount of latent knowledge stored in a document collection. Most important, this methodology is one we loose when metric distance graphs are required.
*ETAY ZIV - Systematic identification of statistically significant network measures
Author(s):
Etay Ziv, Columbia University, USA
Robin Koytcheff, Columbia University, USA
Manuel Middendorf, Columbia University, USA
Chris Wiggins, Columbia University, USA
Abstract:
The physicist's desire to break systems into fundamental parts is uniquely thwarted by idealized networks: they are composed of identical nodes, differentiated only by the combinatorial explosion of possible connections. Ideal reduced degrees of freedom -- the correct ``local substructures" -- are not at all obvious; we strive here to develop systematic and principled algorithms for their discovery. Functional genomics and the development of modular biology motivate a statistical identification of the most important scalars, functionals of the adjacency matrix representing the network. Scalars are global, involving all nodes; although they can be related to subgraph enumeration, the analysis does not require hypothetical ``most important" subgraphs. The resulting algorithm also suggests novel machine-learning techniques for network classification.
*ALI ZARRINPAR - Optimization of Specificity in a Cellular Protein Interaction Network by Negative Selection
Author(s):
Ali Zarrinpar, University of California, San Francisco, USA
Sang-Hyun Park, University of California, San Francisco, USA
Wendell A. Lim, University of California, San Francisco, USA
Abstract:
Most proteins that participate in cellular signalling networks contain modular protein-interaction domains. Multiple versions of such domains are present within a given organism: the yeast proteome, for example, contains 27 different Src homology 3 (SH3) domains. This raises the potential problem of cross-reaction. It is generally thought that isolated domain-ligand pairs lack sufficient information to encode biologically unique interactions, and that specificity is instead encoded by the context in which the interaction pairs are presented. Here we show that an isolated peptide ligand from the yeast protein Pbs2 recognizes its biological partner, the SH3 domain from Sho1, with near-absolute specificity--no other SH3 domain present in the yeast genome cross-reacts with the Pbs2 peptide, in vivo or in vitro. Such high specificity, however, is not observed in a set of non-yeast SH3 domains, and Pbs2 motif variants that cross-react with other SH3 domains confer a fitness defect, indicating that the Pbs2 motif might have been optimized to minimize interaction with competing domains specifically found in yeast. System-wide negative selection is a subtle but powerful evolutionary mechanism to optimize specificity within an interaction network composed of overlapping recognition elements.
*LAZAROS GALLOS - Simulation of random walks and reaction-diffusion processes on scale-free networks
Author(s):
Lazaros Gallos, Aristotle University of Thessaloniki, Greece
Panos Argyrakis, Aristotle University of Thessaloniki, Greece
Abstract:
In this work we study diffusion properties on scale-free networks via Monte-Carlo simulations. We study some basic random walk properties taking place on a network substrate, including the mean squared displacement, the number of nodes visited and the survival probability of a random walker on a network with a concentration c of static traps. We find important deviations from the well-established classical diffusion solutions on lattice, where although the random walkers remain close to their origin, they can sample a large part of the network. For the trapping problem, we observe both mean-field and complicated behavior, depending on the connectivity of the network. We also report results on chemical reactions taking place on scale-free networks, based on the A+A and A+B models, where we show that a drastically different behavior arises as compared to the same reactions in normal spaces. The reactions proceed in an extremely rapid rate, where the concentration of the reactants reduces in a power-law form with an exponent higher than 1. The important effects of depletion zone generation (A+A) and segregation of the reactants (A+B) do not occur at all as in normal spaces. Instead, we have observed clustering of A (A+A) and of mixed A and B (A+B) in the neighborhood of the hubs of the network. At the limit of very sparse networks the usual behavior is recovered.
*MARKUS BREDE - Constructing Scale-Free Networks by a Matrix Stability Criterium
Author(s):
Markus Brede, CSIRO Centre for Complex Systems Science, Australia
John Finnigan, CSIRO Centre for Complex Systems Science, Australia
Abstract:
Scale-free (SF) networks have been found to occur in very diverse contexts. Examples are found in artificially created systems such as the WWW, transport flow systems or social networks, and also in many biological regulatory networks -- e.g., metabolic, protein folding and genetic networks. It is this striking universality which makes one look for widely applicable general principles leading to the formation of SF networks. In principle, two processes for the formation of SF networks are known: preferential attachment (and modifications thereof) and optimization with respect to diameter and link number. In this paper we propose a potentially third mechanism: Evolving SF networks as interaction networks of systems which are distinguished by their stability to perturbation away from equilibrium. The model we propose thus for the first time relates SF networks to stability properties of the underlying dynamical system. Consider a model network where links can either be positive or negative. A positive link between node $i$ and node $j$ means that the population associated with $i$ promotes the growth of $j$'s population. Conversely, a negative link means suppression of the growth of $j$ by $i$. The model evolves by two major steps: (i) attachment of new nodes, which each form a positive and a negative in- and out-link to randomly selected nodes from the old network and (ii) acceptance or rejection of the resulting new network configurations according to their stability. Stability is measured by the size of the largest eigenvalue of the adjacency matrix. Networks generated in this way exhibit a SF topology with exponents $\gamma$ in the range between $-2$ and $-3$. We extend the model to weighted directed networks, now randomly drawing link strength in step (i) uniformly from the intervals $[-1,0)$ (for positive links) and $(0,1]$ (for negative links). We report power law behaviour of the link strength distribution of the weighted graphs in the SF regime, favouring weak links.

*ALLAN R. ROBINSON - Oceanography

*GLENN FLIERL - Multiscale Physical Ocean Dynamical Processes
*BRIAN ROTHSCHILD - Biological Patchiness in Interdisciplinary Ocean Dynamics
*JAMES MCCARTHY - Climate Processes and Ocean Event Dynamics
*JAMES BELLINGHAM - Systems Oceanography: Ocean Observing and Prediction Systems
*PIERRE LERMUSIAUX - ITR-Based Data Driven Systems for Ocean Science
*IRA DYER - The End-to-End Sonar System (Physical-Meteorological-Ocean Acoustics-Geoacoustics) for Naval Operations

2:00PM-5:00PM AFTERNOON EXTENDED TALK SESSIONS

*JUERGEN KLUEVER - Social Systems

*PHILIP V. FELLMAN - Adaptation and Coevolution on an Emergent Global Competitive Landscape
Author(s):
Philip V. Fellman, Southern New Hampshire University, Woodbury University, USA
Jonathan Vos Post, Woodbury University, USA
Roxana Wright, Southern New Hampshire University, USA
Usha Dasarari, Southern New Hampshire University, USA
Abstract:
Notions of Darwinian selection have been implicit in economic theory for at least sixty years. Richard Nelson and Sidney Winter have argued that while evolutionary thinking was prevalent in prewar economics, the postwar Neoclassical school became almost entirely preoccupied with equilibrium conditions and their mathematical conditions. One of the problems with the economic interpretation of firm selection through competition has been a weak grasp on an incomplete scientific paradigm. As I.F. Price notes: “The biological metaphor has long lurked in the background of management theory largely because the message of “survival of the fittest” (usually wrongly attributed to Charles Darwin rather than Herbert Spencer) provides a seemingly natural model for market competition (e.g. Alchian 1950, Merrell 1984, Henderson 1989, Moore 1993), without seriously challenging the underlying paradigms of what an organisation is.” In this paper we examine the application of dynamic fitness landscape models to economic theory, particularly the theory of technology substitution, drawing on recent work by Kauffman, Arthur, McKelvey, Nelson and Winter, and Windrum and Birchenhall. In particular we use Professor Post’s early work with John Holland on the genetic algorithm to explain some of the key differences between static and dynamic approaches to economic modeling.
*MATT GROSSMANN - Group Allegiance & Issue Salience in Factional Competition
Author(s):
Matt Grossmann, University of California, Berkeley, USA
David Scherzer, Rensselaer Polytechnic Institute, USA
Abstract:
We model a decision-making process with factional competition over two policy outcomes. The goal of our model is to incorporate two elements normally absent from models of group competition: the role of individual allegiance and assimilation to group norms and the effect of changes in issue salience as policy results come closer to matching an individual’s ideal position. We track the relationships among individual, group, and global policy preferences and we discover how behavior differs as the number of groups in competition increases. The model produces several important results. First, we show that most competitive environments can produce an equilibrium on moderate policy outcomes even if some groups retain extreme positions. Second, we show that the extent of individual assimilation to group norms on policy preferences will alter both the temporal and distributional character of factional competition; the nature of assimilation can alter the size and issue preferences of each group. Finally, the model shows that factional competition with a larger number of groups produces more predictable outcomes and is more likely to feature emergent moderating strategies on the part of the groups.
*DMITRI PUSHKIN - Bank-mergers as scale-free coagulation
Author(s):
Dmitri Pushkin, University of Illinois at Urbana-Champaign, USA
Hassan Aref, Virginia Tech, USA
Abstract:
The asset size distribution of US banks is viewed as the result of a scale-free coagulation process. When two banks merge, the assets of the combined institution equals the sum of the assets of the constituent banks. Analysis of the Smoluchowski coagulation equation suggests the emergence of a steady-state, power law distribution with an exponent that only depends on the degree of homogeneity of the coagulation rat