Agent-Based Computational Economics (ACE)
Research Area: Evolution of Interaction Networks

Last Updated: 30 April 2008

Site Maintained By: Leigh Tesfatsion Department of Economics Iowa State University Ames, Iowa 50011-1070 http://www.econ.iastate.edu/tesfatsi/ tesfatsi AT iastate.edu Network Formation: General Resources Online ACE Course (Self-Study eBook) ACE Website

Table of Contents: Basic Issues Introductory Materials General Readings Software, Toolkits, and Demos Resource Sites, Groups, and Individual Researchers

Basic Issues

• What might be inferred from the observation by Craig Reynolds that "a flock is not a big bird"?
• Distinguishing between "simple" and "complex" economic systems -- what role do network effects play?
• Under what circumstances can robust point predictions of economic outcomes be obtained from a knowledge of initial economic structure, ignoring network effects? And when might network effects be important for the prediction of economic outcomes?
• Is the global economy becoming strongly interactive? If so, is this a good thing?
• How can graph theory be used to quantitatively represent and analyze economic interaction networks?
• What type of systematic phase transition do random graphs undergo as their connectivity increases?
• Do socioeconomic networks exhibit any kind of systematic phase transition as their connectivity increases?
• Why all the recent excitement about "small-world networks" (locally dense networks with global reach)?
• Is economic interaction a catalyst for economic change? In particular, do economic networks exhibit "autocatalytic" properties?
• How do agents learn in networks? Does learning through interaction necessarily promote cooperation?

Introductory Materials

• Frédéric Amblard, "Simulating Social Networks: A Review of Three Books" (html,7pp), Journal of Artificial Societies and Social Simulation (JASSS), Vol. 6, No. 2, March 2003 (electronic journal).
  Abstract: Amblard reviews Duncan Watts' Small Worlds... (1999), Albert-Lázló Barabási's Linked (2002), and Mark Buchanan's Nexus... (2002).

  David F. Batten, Chapter 3: "Sheeps, Explorers, and Phase Transitions" and Chapter 4:"The Ancient Art of Learning by Circulating", in Discovering Artificial Economics: How Agents Learn and Economies Evolve, Perseus Books, Westview Press, 2000. See also L. Tesfatsion, "Notes on Network Effects (Batten Chapter 3 Plus Glossary of Terms)" (html) and "Notes on Learning in Networks (Batten Chapter 4 Plus Glossary of Terms)" (html).

  Note: Unfortunately the Batten book is now out of print. However, the entire Batten book (figures included) in pdf can be accessed at here (pdf,17MB).

  Giorgio Fagiolo (Laboratory of Economics and Management, Sant'Anna School of Advanced studies, Pisa, Italy) has prepared a ppt presentation, titled Games on Networks: Rationality, Dynamics, and Interactions (pdf,1.5M), that reviews recent research by himself and others on the endogenous formation of networks by strategically interacting players in a variety of application contexts.

  Leigh Tesfatsion, "Presentation Slides on the Structural and Dynamical Analysis of Networks" (pdf,2.3M) and "Lecture Notes on Networks" (html).

  Abstract: These presentation slides and lecture notes provide rigorous definitions for basic structural characterizations of networks (e.g., degree, clustering, shortest path length). Also discussed are phase transitions in random graphs, the concept of a "small world network," and the possible application of small-world networks to the study of trade interactions.

  Leigh Tesfatsion, "Introductory Notes on Network Formation" (pdf,246K).

  Leigh Tesfatsion, "Endogenous Determination of Trade Networks: An Illustrative Labor Market Application" (pdf,117K).

  Allen Wilhite, "Bilateral Trade and `Small-World' Networks" (pdf,181K), Computational Economics, Vol. 18, No. 1, August 2001, pp. 49-64. The published article is available at SpringerLink. See also Leigh Tesfatsion, "Notes on Wilhite (2001)" (pdf,236K).

  Abstract: Wilhite develops an agent-based computational model of a bilateral exchange economy. He uses this model to explore the consequences of restricting trade to different types of networks, including a "small-world network" with both local connectivity and global reach. His key finding is that small-world networks provide most of the market-efficiency advantages of completely connected networks while retaining almost all of the transaction cost economies of locally connected networks.

  Allen W. Wilhite, "Economic Activity on Fixed Networks", in Leigh Tesfatsion and Kenneth L. Judd (editors), Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics, Handbooks in Economics Series, North-Holland/Elsevier, Amsterdam, Spring 2006.

  Abstract: "A large portion of our economic interactions involves a very small portion of the population. We seem to prefer familiar venues. But the tendency to focus our attention on a few individuals or activities is an attribute that is typically omitted in our characterization of markets. In markets, agents seem to interact impersonally and efficiently with countless other faceless agents. This chapter looks into the consequences of including a connection between agents, a tendency to interact with a specific few, in economic decision-making. Agents are assumed to occupy the nodes of a network and to interact exclusively with agents to whom they are directly linked. We then study evolution of game strategies and the effectiveness of exchange as the topology of the underlying network is altered. We find that networks matter, that changes in a network's structure can alter the steady-state attributes of an artificial society as well as the dynamics of that system."

  Nicolaas J. Vriend, "ACE Models of Endogenous Interactions", in Leigh Tesfatsion and Kenneth L. Judd (editors), Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics, Handbooks in Economics Series, North-Holland/Elsevier, Amsterdam, Spring 2006.

  Abstract: Various approaches used in Agent-based Computational Economics (ACE) to model endogenously determined interactions between agents are discussed. This concerns models in which agents not only learn how to play games, but also how agents learn to decide with whom to play or not.

General Readings

• Janette Aschenwald, Stefan Fink, and Gottfied Tappeiner, "Brave New Modeling: Cellular Automata and Artificial Neural Networks for Mastering Complexity in Economics," Complexity 7(1), 2002, 39-47.

• Albert-László Barabási, Linked: The New Science of Networks (html), Perseus Publisher, May 2002. ISBN: 0-738-20667-9.
  Abstract: "In Linked, Barabasi, a physicist whose work has revolutionized the study of networks, traces the development of this rapidly unfolding science and introduces us to the scientists carrying out this pioneering work. These `new cartographers' are mapping networks in a wide range of scientific disciplines, proving that social networks, corporations, and cells are more similar than they are different, and providing important new insights into the interconnected world around us. This knowledge, says Barabasi, can shed light on the robustness of the Internet, the spread of fads and viruses, even the future of democracy. Engaging and authoritative, Linked provides an exciting preview of the next century in science, guaranteed to be transformed by these amazing discoveries."
  Albert-László Barabási is the Emil T. Hofman Professor of Physics at the University of Notre Dame, South Bend, Indiana.

  Sushil Bikhchandani, David Hirshleifer, and Ivo Welch, "Learning from the Behavior of Others: Conformity, Fads, and Information Cascades," Journal of Economic Perspectives 12(3), 1998, 151-170.

  Phillip Bonacich, The Evolution of Exchange Networks (html, 21pp.), Journal of Social Structure, Volume 2, Number 5, 2001.

  Abstract: Computer simulations are used to explore how 2-dimensional spatial networks of exchange opportunities evolve. Agents are placed on a checkerboard with patched edges (a torus) in which neighboring positions can exchange with one another. Each transaction produces a profit that can be divided between the two participants if they can agree on a division. Agents change their positions (locations) in successive rounds based on a very simple win-stay/lose-change strategy: Agents do not change their position is they earn more than a certain minimum; agents whose earnings fall below the minimum randomly pick a neighboring square and move to this square if unoccupied. The computer simulations suggest that only networks with high degrees of power imbalance are unstable, and that there are three basic forms of stable networks.

  Ronald Brieger, Kathleen M. Carley, and Philippa Pattison (eds.), Dynamic Social Network Modeling and Analysis, Workshop Summary and Papers, Committee on Human Factors, Board on Behavioral, Cognitive, and Sensory Sciences, National Academy Press, Washington, D.C., 2003.

  Abstract: This volume collects together papers focusing on current developments in the science of social network modeling, including applications in the area of national security. Part I of this document is a summary of major themes, together with research issues and prospects. Part II contains the papers themselves.

  Mark Buchanan, Nexus: Small Worlds and the Groundbreaking Science of Networks, W. W. Norton, New York, N.Y., 2002. ISBN: 0-393-04153-0.

  Mark Buchanan holds a Ph.D. in physics and has been an editor at Nature and New Scientist.

  Giorgio Fagiolo, "Endogenous Neighborhood Formation in a Local Coordination Model with Negative Network Externalities" (pdf,267K), Journal of Economic Dynamics and Control, Vol. 29, 2005, pp. 297-319.

  Abstract" The paper studies the evolution of coordination in a local interaction model where agents can simultaneously choose the strategy to play in the game and the size of their neighborhood. We focus on pure-coordination games played by agents located on one-dimensional lattices and we assume that network externalities become eventually negative as neighborhood sizes increase. We show that the society almost always converges to a steady-state characterized by high levels of coordination and small neighborhood sizes. We find that neighborhood adjustment allows for higher coordination than if interaction structures were static and that large populations attain higher coordination provided that average initial neighborhood sizes are not too small.

  John Foster, "From Simplistic to Complex Systems in Economics" (pdf,148K), Discussion Paper 335, School of Economics, University of Queensland, October 2004, to appear in the Cambridge Journal of Economics.

  Abstract: The applicability of complex systems theory in economics is evaluated and compared with standard approaches to economic theorizing based upon constrained optimization... It is explained why it is necessary to approach economic analysis from a network, rather than a production and utility function perspective, when we are dealing with complex systems.

  Alan P. Kirman and Nicolaas J. Vriend, "Evolving Market Structure: An ACE Model of Price Dispersion and Loyalty" (pdf preprint), Journal of Economic Dynamics and Control, Vol. 25, Nos. 3-4, 2001, pp. 459-502

  Abstract: Kirman and Vriend develop an agent-based computational model capturing salient structural aspects of the actual wholesale fish market in Marseilles, France. Two features characterizing this actual market are: (a) loyalty relationships (persistent trade partnerships) between particular buyers and sellers; and (b) persistent price dispersion unexplainable by observable characteristics of the fish. The simulation results show that loyalty relationships can indeed emerge naturally between particular buyer-seller pairs as the buyers and sellers co-evolve their trading rules over time. Buyers learn to become loyal to particular sellers while, at the same time, sellers learn to offer higher payoffs (lower prices and more reliable supplies) to their more loyal buyers. Moreover, this evolving trade network supports persistent price dispersion over time.

  Alina Lazar, David Chavalarias, and T. K. Ahn, "Endogenous Network Formation and the Evolution of Preferences" (pdf,370K), Presented at Agent2002, University of Chicago, October 11-12, 2002.

  Abstract: The authors develop analytical and computational models to study the conditions for the stability of a population consisting of agents with heterogeneous preferences. The analytical models that utilize an indirect evolutionary approach show that the ability to detect others' types is critical for the evolution of reciprocal preferences. The computational models of this paper incorporate agents' memories and endogenously built social networks into the evolutionary dynamics. The simulations based on the computational models show that the strength of the social network is a critical factor for the success of nonselfish preferences. A fully heterogeneous population consisting of egoists, reciprocators, and altruists can be stable for a range of parameter conditions.

  M. E. J. Newman, "Models of the Small World: A Review" (pdf), Journal of Statistical Physics 101 (2000), 819-841.

  Mark Pingle and Leigh Tesfatsion, "Evolution of Worker-Employer Networks and Behaviors Under Alternative Non-Employment Benefits: An Agent-Based Computational Study", pp. 256-285 in Anna Nagurney (ed.), Innovations in Financial and Economic Networks, New Dimensions in Networks Book Series, Edward Elgar Publishers, 2003 (pdf preprint,269K), (pdf presentation,88K).

  Abstract: This study replaces the standard exogenously-given worker-employer matching function with endogenous preferential worker-employer matching based on past work-site experiences. Workers and employers participate in a sequential employment game with adaptive job search and incomplete contracts. Matched workers and employers participate in a work-site game. The study examines the effects of a non-employment payoff (NEP) on network formation and work-site behaviors. Taking both utility benefits and program costs into account, the highest efficiency is achieved with a moderate NEP level. A zero NEP encourages too much shirking on the work-site, while a high NEP results in too high a risk of lost earnings due to coordination failure.

  Domenico Prisi and Massimiliano Ugolini, "Living in Enclaves", Complexity, Volume 7, No. 1, September/October 2001.

  Abstract: The authors ask: "What are the consequences of living in small isolated communities vs living in larger environments as members of bigger communities?" The authors use an agent-based spatial computational framework similar to Epstein and Axtell's Sugarscape and Holland's Echo to study populations of organisms living and reproducing either in isolated enclaves or in a common environment. They study the role of artifacts that increase energy extracted from natural resources in helping these populations avoid extinction.

  James E. Rauch and Alessandra Casella (eds.), Networks and Markets, Russell Sage Foundation, New York, 2001.

  Abstract:: "(This book) argues that economists' knowledge of markets and sociologists' rich understanding of networks can and should be combined... The book includes contributions by both sociologists and economists, applying the concepts of markets and networks to concrete empirical phenomena."

  James E. Rauch, "Business and Social Networks in International Trade," Journal of Economic Literature, Volume XXXIX (December 2001), pp. 117-1203.

  Abstract: "Nations appear to trade too much with themselves and too little with each other. ... Attempts to explain this `mystery of the missing trade' have increasingly focused on informal trade barriers, especially weak enforcement of international contracts... and inadequate information about international trading opportunities. (This study surveys research) dealing with the role of networks in overcoming or creating trade barriers..., the role of intermediaries who can connect foreign agents to domestic networks, and the ability of transnational production networks to facilitate technology transfer."

  David Schmidt, Robert Shupp, James Walker, T. K. Ahn, and Elinor Ostrom, "Dilemma Games: Game Parameters and Matching Protocols" (pdf,21pp.), Journal of Economic Behavior and Organization 46 (2001), 357-377.

  Abstract: This study examines the impact of changes in pecuniary payoffs and the linkages between players in the game environment on strategy choice in repeated Prisoner's Dilemma (PD) games.

  John Skvoretz, "Complexity Theory and Models for Social Networks," Complexity, Volume 8, No. 1, September-October 2002, 47-55.

  Abstract: "Much work in complexity theory employs agent-based models in simulations of systems of multiple agents. Agent interaction follows some standard types of network topologies. My aim is to assess how recent advances in the statistical modeling of social networks may contribute to agent-based modeling traditions, specifically, by providing structural characterizations of a variety of network topologies. I illustrate the points by reference to a computational model for the evolution of cooperation among agents embedded in neighborhoods and by reference to complex, real social networks defined by ties of political support between US Senators as revealed through ties of cosponsorship of legislation."

  John Skvoretz is with the Department of Sociology, Sloan College, University of South Carolina, Columbia.

  Steven H. Strogatz, Sync: The Emerging Science of Spontaneous Order, Hyperion, 338 pp., March 2003. ISBN: 0-786-86844-9

  Abstract (from Publishers Weekly, Copyright 2003 Reed Business Information, Inc.): "Strogatz is a Cornell mathematician and pioneer of the science of synchrony, which brings mathematics, physics, and biology to bear on the mystery of how spontaneous order occurs at every level of the cosmos, from the nucleus on up. In this eminently accessible and entertaining book..., Strogatz explores synchrony in chaos systems, at the quantum level, in small-world networks as exemplified by the parlor game `six degrees of Kevin Bacon' and in human behavior involving fads, mobs and the herd mentality of stock traders. The author traces how the isolated and often accidental discoveries of researchers are beginning to get into the science of synchrony, and he amply illustrates how the laws of mathematics underlie the universe's uncanny capacity for spontaneous order."

  Steven Strogatz is a Professor of Theoretical and Applied Mechanics and a member of the Center for Applied Mathematics at Cornell University, Ithaca, New York.

  Steven H. Strogatz, "Exploring Complex Networks" (pdf,589K), Nature, Volume 410, No. 6825, March 8, 2001.

  Abstract: This article includes a summary of Strogatz's work with Duncan Watts on "small-world networks" that has started a major new field of research within network theory. The author also discusses more generally how the study of networks pervades all of science and everyday life. As examples, he points to food webs, power transmission lines, the World-Wide Web, and the neural network of nematode worms. He provides an introduction to terminology and concepts from nonlinear dynamics used in the study of complex networks.

  Troy Tassier and Filippo Menczer, "Emerging Small-World Referral Networks in Evolutionary Labor Markets", IEEE Transactions on Evolutionary Computation, Volume 5, Number 5, October 2001.

  Abstract: The authors construct an ACE labor market model in which potential workers engage in both social network formation and direct job search. They show that the evolved social networks have "small world network" properties in the sense that they are both very clustered (locally structured) and have global reach, and that these two properites enhance the ability of the social networks to perform as job referral networks. Nevertheless, as evolution progresses, agents end up expending more energy on social network formation and/or direct job search than is socially efficient.

  Leigh Tesfatsion, "Structure, Behavior, and Market Power in an Evolutionary Labor Market with Adaptive Search" (pdf,295K), Journal of Economic Dynamics and Control 25 (2001), pp. 419-457. The published article is also available from Science Direct.

  Abstract: This study undertakes a systematic experimental investigation of the relationship between market power and labor market structure (concentration and capacity conditions) when workers and employers preferentially match based on past worksite experiences. For each tested market structure, workers and employers repeatedly seek preferred worksite partners based on continually updated expected utility, engage in efficiency-wage worksite interactions modeled as prisoner's dilemma games, and evolve their worksite behaviors over time. A key finding is the presence of strong learning and network effects. Each tested market structure maps into a "spectral" distribution of observed interaction networks exhibiting one dominant attractor (frequent network pattern) with one or two weaker attractors (less frequent network patterns). Market structure is strongly predictive for the relative market power of workers and employers across all network attractors, but the magnitudes of the market power levels attained by workers and employers vary widely across the network attractors.

  Leigh Tesfatsion, "Hysteresis in an Evolutionary Labor Market with Adaptive Search," (ps,216K), (pdf,534K), pp. 189-210 in S.-H. Chen (ed.), Evolutionary Computation in Economics and Finance, Physica-Verlag Heidelberg, New York, 2002

  Abstract: This study undertakes a systematic experimental investigation of hysteresis (path dependency) when workers and employers preferentially match based on past worksite experiences. It is shown that two distinct hysteresis effects can arise, network and behavioral, when workers and employers interact strategically and evolve their worksite behaviors over time. These hysteresis effects result in persistent heterogeneity in earnings and employment histories across agents who have no observable structural differences. At a more global level, these hysteresis effects are shown to result in a one-to-many mapping between treatment factors and experimental outcomes. These hysteresis effects may help to explain why excess earnings heterogeneity is commonly observed in real-world labor markets.

  Leigh Tesfatsion, "Preferential Partner Selection in Evolutionary Labor Markets: A Study in Agent-Based Computational Economics" (ps,130K), pp. 15-24 in V. W. Porto, N. Saravanan, D. Waagen, and A. E. Eiben (eds.), Evolutionary Programming VII, Proceedings of the Seventh Annual Conference on Evolutionary Programming, Springer-Verlag, Berlin, 1998.

  Abstract: This ACE study develops a computational labor market framework for studying the formation and evolution of contractual networks between workers and employers. Resource-constrained workers and employers choose and refuse contractual partners on the basis of continually updated expected utility, engage in risky worksite interactions modelled as two-person "efficiency-wage" prisoner's dilemma games, and evolve their work-site strategies over time on the basis of past worksite earnings. Illustrative computational experiments are reported and interpreted.

  Leigh Tesfatsion, "A Trade Network Game with Endogenous Partner Selection" (pdf,401K), (ps,151K), pp. 249-269 in H. M. Amman, B. Rustem, and A. B. Whinston (eds.), Computational Approaches to Economic Problems, Kluwer Academic Publishers, 1997

  Abstract: This ACE study develops a computational trade network game (TNG) framework for exploring the interplay between evolutionary game dynamics and preferential partner selection in buyer-seller markets. The TNG framework consists of successive generations of resource-constrained traders who choose and refuse trade partners on the basis of continually updated expected payoffs, engage in risky trades modelled as two-person games, and evolve their trade strategies over time. Preliminary computer experiments are reported which suggest that the standard optimality properties used to judge the desirability of matching mechanisms in static market contexts may be inadequate measures of optimality from an evolutionary perspective.

  For additional information regarding TNG research articles and software, see the TNG Home Page.

  Leigh Tesfatsion, "How Economists Can Get Alife: Abbreviated Version" (html), a summary version of "How Economists Can Get Alife," pages 533-561 in W. Brian Arthur, Steven Durlauf, and David Lane, The Economy as an Evolving Complex System II, Proceedings Volume XXVII, Santa Fe Institute Studies in the Sciences of Complexity, Addison-Wesley, 1997.

  Abstract: This study argues the importance of considering who is trading with whom, with what regularity, and why, in economic markets, especially those with small and intermediate numbers of participants in which price is only one of several strategic considerations. It discusses how agent-based computational modeling tools adapted from the newly developing field of artificial life (alife) provide a potentially promising way to study the formation and evolution of such economic networks.

  Stanley Wasserman and Katherine Faust, Social Network Analysis: Methods and Applications, Cambridge University Press, Cambridge, 1994.

  Abstract: "(This book) reviews and discusses methods for the analysis of social networks with a focus on applications of these methods to many substantive examples."

  Duncan J. Watts and Steven H. Strogatz, "Collective Dynamics of `Small-World' Networks," Nature, Volume 393, No. 6684, 18 June 1998, 440-442.

  Duncan J. Watts, Six Degrees: The Science of a Connected Age, W. W. Norton and Co., 368 pp., February 2003. ISBN: 0-393-04142-5

  Abstract: "(Watts is) the pioneering young scientist whose work on the structure of small worlds has triggered an avalanche of interest in networks. In this remarkable book, (he) sets out to explain the innovative research that he and other scientists are spearheading to create a blueprint of our connected planet. Whether they bind computers, economies, or terrorist organizations, networks are everywhere in the real world, yet only recently have scientists attempted to explain their mysterious workings."

  Duncan Watts is an Assistant Professor of Sociology at Columbia University.

  Duncan J. Watts, Small Worlds: The Dynamics of Networks Between Order and Randomness, Princeton University Press, October 1999, 262 pp., ISBN 0-69-100541-9.

  Abstract: "How do networks matter? Simply put, local actions can have global consequences, and the relationship between local and global dynamics depends critically on the network's structure. Watts illustrates the subtleties of this relationship using a variety of simple models --- the spread of infectious disease through a structured population; the evolution of cooperation in game theory; the computational capacity of cellular automata; and the synchronisation of coupled phase-oscillators. ...This fascinating exploration will be fruitful in a remarkable variety of fields, including physics and mathematics, as well as sociology, economics, and biology."

  Duncan J. Watts is an Assistant Professor of Sociology at Columbia University.

  G. Weisbuch, Alan P. Kirman, and Dorothy Herreiner, "Market Organization," Economic Journal 110(465), 2000, 411-436.

  Douglas R. White (Guest Editor), Special Issue on Networks and Complexity, Complexity, Volume 8, Number 1, September-October 2002.

  Abstract: This special issue, guest-edited by Douglas R. White (Anthropology, UC Irvine), grew out of the August 2002 Founding Workshop for the Santa Fe Institute's Network Dynamics Program, directed by James Crutchfield (Program Director, SFI) and Duncan Watts (Sociology, Columbia University).

  H. Peyton Young, "Social Dynamics: Theory and Applications", in Leigh Tesfatsion and Kenneth L. Judd (editors), Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics, Handbooks in Economics Series, North-Holland/Elsevier, Amsterdam, Spring 2006.

  Abstract: Agent-based models typically involve large numbers of interacting individuals with widely differing characteristics, rules of behavior, and sources of information. The dynamics of such systems can be extremely complex due to their high dimensionality. This chapter discusses a general method for rigorously analyzing the long-run behavior of such systems using the theory of large deviations in Markov chains. The theory highlights certain qualitative features that distinguish agent-based models from more conventional types of equilibrium analysis. Among these distinguishing features are: local conformity versus global diversity, punctuated equilibrium, and the persistence of particular states in the presence of random shocks. These ideas are illustrated through a variety of examples, including competition between technologies, models of sorting and segregation, and the evolution of contractual customs.

Software, Toolkits, and Demos

• The Trade Network Game: Demonstration Software (Network Formation) (html)

• David McFadzean, Deron Stewart, and Leigh Tesfatsion, "A Computational Laboratory for Evolutionary Trade Networks" (pdf,508K), IEEE Transactions on Evolutionary Computation, Vol. 5, No. 5, October 2001, pp. 546-560. The published article is also available at IEEE Xplore.
  Abstract: This report presents, motivates, and illustrates the use of a computational laboratory for the investigation of evolutionary trade network formation among strategically interacting buyers, sellers, and dealers. This Trade Network Game Laboratory (TNG Lab) is targeted for the Microsoft Windows desktop. The TNG Lab is both modular and extensible and has a clear, easily operated graphical user interface. It permits visualization of the formation and evolution of trade networks by means of run-time animations. Data tables and charts reporting descriptive performance statistics are also provided in real time. The capabilities of the TNG Lab are demonstrated by means of labor market experiments.
  For an automatic installation program for the TNG Lab, along with TNG Lab tutorials and other supporting materials, visit the TNG Home Page.

  Annotated list of pointers to ACE/CAS Computational Laboratories and Demonstration Software. Included are many demos of possible interest for the agent-based computational modeling of networks (e.g., demos for several distinct versions of the Schelling Segregation Model, a Trade Network Game demo, demos of cellular automata, and demos of flocking behavior).

  Annotated list of pointers to ACE/CAS General Software and Toolkits. Included at this site are social network analysis packages such as ORA and toolkits such as Repast that support agent-based network modeling.

Resource Sites, Groups, and Individual Researchers

• Formation of Economic and Social Networks (Tesfatsion, Iowa State University). Resources at this site include an annotated list of pointers to research groups and individual researchers studying economic and social network formation.

• Internet Site for the Economics of Networks (Economides, New York University). This site provides a collection of information on economic issues related to networks, such as the telephone and fax communications networks, the Internet, financial exchange and credit card networks, and "virtual networks" such as the virtual network of all Windows or all Mac computers.

• Networking Games (Columbia University) provides pointers to resources focusing on the application of game theoretic concepts to decentralized network resource control and management.

• Growing Self-Organizing Networks (Dresden University of Technology). This site features work by Dr.-Ing. habil. Bernd Fritzke and his students on growing self-organizing networks. Background material is provided, along with pointers to examples, software, Java simulations, publications, and related work by other authors.

• Kathleen M. Carley (School of Computer Science, Carnegie Mellon University, Pittsburgh, PA): Computational and social and organization theory; dynamic social networks; multi-agent network models; group, organizational, and social adaptation and evolution; statistical models for dynamic network analysis and evolution; computational text analysis; and the impact of telecommunication technologies on communication and information diffusion within and among groups.

• Robin Cowan (Maastricht Economic Research Institute on Innovation and Technology (MERIT), University of Maastricht, the Netherlands, and Economics Department, University of Waterloo, Canada): Dynamics of networks and network structures; Economics of technology adoption; Economics of knowledge generation; Technological competition and standardization; Consumption dynamics; Methodology.

• Catherine Dibble (Department of Geography, University of Maryland, College Park) specializes in agent-based simulation (using Swarm), computational laboratories in economic geography, the formation and effects of socio-economic networks in spatial landscapes, and small-world networks. Her home page provides pointers to a variety of resources related to these topics.

• John Duffy (Department of Economics, University of Pittsburgh, Pennsylvania): Incorporation of learning in computational economic models; Using genetic algorithms to model how agents learn and adaptively update their forecasts; Endogenous network formation.

• Giorgio Fagiolo (Sant'Anna School of Advanced Studies, Pisa, Italy): Endogenous networks; Local interaction models; Endogenous neighborhood formation; Segregation in networks; Minority games.

• Matthew O. Jackson (Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, California): Strategic models of social and economic networks; Evolution of social and economic networks; Coalition and Party Formation in legislative voting games; Reputation versus social learning.

• Michael W. Macy (Sociology, Cornell University, Ithaca, N.Y.): Informal social control in on-line trading communities; Coalition formation in exchange networks; Trust and cooperation in the U.S. and Japan; Management fads; Collective action; Evolutionary game theory; Deviance and social control; Social psychology; Social Exchange theory; Rational choice.

• Filippo Menczer (School of Informatics and Computer Science Department, Indiana University, Bloomington): Labor market referral networks as "small-world" networks; Adaptive and evolutionary computation applied to intelligent and distributed information agents; Interactions between learning and evolution; Distributed decision making; Artificial life.

• Anna Nagurney (Finance and Operations Management Department, University of Massachusetts, Amherst, Massachusetts): Network models of large-scale financial, transportation, and regional economic systems; Algorithms on serial and parallel computer architectures to predict flows of funds, people, goods, and services.

• Michael Neugart (Chemnitz University of Technology, Chemnitz, Germany): Labor economics; Endogenous matching functions; Nonlinear economic dynamics; Political economics; Agent-based computational modeling; Applied econometrics.

• Denis Phan (ENST de Bretagne and ICI Université de Bretagne Occidentale, France): Global and local effects of interaction structures; Network externalities; Small-world networks, phase transitions, and avalanches in ACE frameworks; Moduleco (an agent-based computational laboratory); Cognitive economics; Generic properties of complex adaptive systems.

• Leigh Tesfatsion (Economics, Iowa State University, Ames, Iowa): Trade network games; Formation and evolution of networks in labor markets; Transmission-grid constrained exchange in electricity markets.

• Nick Vriend (Economics, Queen Mary and Westfield College, University of London): Dynamics of interactive market processes; Emergent properties of evolving market structures and outcomes; Learning algorithms; History of economic thought.

• Allen Wilhite (Economics, University of Alabama in Huntsville):Small-world trading networks; Decision making when agents are influenced by the decisions of others.

Copyright © 2008 Leigh Tesfatsion. All Rights Reserved.