Introductory Materials

Agent-Based Computational Economics (ACE)

Last Updated: 8 January 2009

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 Online ACE Course (Self-Study eBook) ACE Website

Table of Contents: Key Issues On-Line Guides for Newcomers Basic Introductory Readings Other Introductory Materials Other Miscellaneous On-Line Materials

Key Issues

• What is a complex system? A complex adaptive system?
• Does the understanding of an observed system (sand pile, washing machine, cockroach, city, economy,...) require knowing how to construct it? And what might "constructing it" mean?
• Does the understanding of an observed system require knowing how it came about historically?
• What is ACE all about? Why all the emphasis on learning, interactions, and endogenous interaction network formation?
• One goal of ACE researchers is to understand the possible genesis of persistently observed economic regularities (e.g., the use of money) by using "culture-dish" computational experiments to grow economies that reliably display these regularities when started from certain specified initial conditions. Is this a meaningful goal? A feasible goal?
• Another goal of ACE researchers is to experimentally test alternative institutional designs for economic processes (e.g., the sensitivity of market efficiency to alternative bidding protocols). Is this a meaningful goal? A feasible goal?
• Can ACE facilitate the scientific study of economic processes?

On-Line Guides for Newcomers

• Robert Axelrod and Leigh Tesfatsion, An On-Line Guide for Newcomers to Agent-Based Modeling in the Social Sciences (html,44K)
  Abstract: This site provides web support materials (readings and demonstration software) for Robert Axelrod and Leigh Tesfatsion, "A Guide for Newcomers to Agent-Based Modeling in the Social Sciences" (pdf,46K), in Leigh Tesfatsion and Kenneth L. Judd (Eds.), Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics (Table of Contents,html), Handbooks in Economics Series, Elsevier/North-Holland, Amsterdam, 2006.

  Leigh Tesfatsion, Agent-Based Modeling: A Bridge Between Games & Social Sciences (pdf,997K).

  Abstract: This short presentation, designed for younger newcomers to Agent-Based Modeling (ABM), starts by giving a brief introduction to ABM using examples from the movies. It next invites readers to participate in a "hands on" demonstration of ABM by playing a version of the Schelling Tipping Game using a checkerboard and tokens. (The Schelling Tipping Game was first introduced in 1978 by Tom Schelling in Micromotives and MacroBehavior.) It then introduces readers to the Schelling Tipping Game Demonstration Software developed by Chris Cook (see below) as well as the SimSeg test bed, an elaboration of the Schelling Tipping Game suitable for serious social science research. It concludes by pointing readers to a website where numerous commercial applications of ABM are discussed.

  Schelling Tipping Game Demonstration Software (html)

Basic Introductory Readings

• Peter Albin, Preface (pp. xiii-xxxi) (pdf,146K) and Duncan K. Foley, Chapter 1: "Introduction (pdf,369K), in Peter S. Albin and Duncan K. Foley (Eds.), Barriers and Bounds to Rationality: Essays on Economic Complexity and Dynamics in Interactive Systems, Princeton Studies in Complexity, Princeton University Press, NJ, 1998, posted with permission of Princeton University Press.
  Abstract: This book preface and wide-ranging introductory chapter by two seminal contributors to economic complexity theory cover the following topics: Possible automata-theoretic resolutions to economic complexity puzzles; dynamical systems (in social and physical science); economic models of fully rational behavior; definitions and measures of complexity; complexity in cellular automata; modeling of complex social and economic interactions; complexity, rationality, and social interaction; and towards a robust theory of action and society.

  Sunny Y. Auyang, Synthetic Analysis of Complex Systems I - Theories (html,11pp), circa 2000.

  Abstract:

  Abstract: This thoughtful essay by independent researcher Sunny Auyang addresses the question "what is complexity." The author attempts to extract the commonly shared ideas implicit in many formalized conceptions of complexity and also in the general way that people tend to think about complicated situations. One important distinction not stressed by the author, however, is that living agents in social and biological systems (as opposed to inanimate elements in purely physical systems) can exert some degree of deliberate forward-looking control over the formation and evolution of their interaction networks.

  Robert Axelrod, Advancing the Art of Simulation in the Social Sciences (pdf,119K), Japanese Journal for Management Information System, Special Issue on Agent-Based Modeling, Vol. 12, No. 3, Dec. 2003.

  Abstract: The author offers advice for doing social science simulation research, focusing on the programming of a simulation model, analyzing the results, and sharing the results with others. The essay is scheduled to appear in a special issue on agent-based modeling in the Japanese Journal for Management Information Systems.

  Rob Axtell, "Agent-Based Computing in Economics" (pdf,256K), presented at the VII Trento Summer School, July 2006.

  Rob Axtell, Why Agents? On the Varied Motivations for Agent Computing in the Social Sciences (pdf,115K), Center on Social and Economic Dynamics, Working Paper No. 17, November 2000.

  Rob Axtell, The New Coevolution of Information Science and Social Science (pdf,198K), Working Paper, Brookings Institution, 2003.

  Abstract: This paper briefly reviews the ways in which exponentially increasing information technology (IT) capabilities are reshaping the social sciences, and how results from the social sciences are in turn making their way into computer and information sciences. The author argues that one specific IT-facilitated development in particular -- multi-agent systems -- "holds out the promise of fundamentally altering the ways in which social science models are conceived, built, explored, and evaluated."

  Shu-Heng Chen, "Computational Intelligence in Agent-Based Computational Economics", Chapter (pp. 517-594) in Computational Intelligence: A Compendium, SpringerLink Book Series, Studies in Computational Intelligence, Volume 115/2008, Springer-Berlin/Heidelberg.

  Abstract: This chapter reviews the algorithmic foundations of agent-based computational economics. It also introduces the field known as "computational intelligence" and discusses its use in agent-based computational economics and its relevance for economics and finance in general.

  Colloquium on Capturing Complexity Through Agent-Based Modelling, published in the Proceedings of the National Academy of Sciences, Vol. 99 (suppl. 3), 2002. This entire proceedings issue is available on-line at the PNAS website (www.pnas.org).

  Joshua M. Epstein and Robert Axtell, Growing Artificial Societies: Social Science from the Bottom Up, MIT Press/Brookings, MA, 1996 (Review, JEL, March 1998, pp. 233-234, ps preprint, 28K).

  Abstract: Using a relatively simple agent-based computational framework called Sugarscape in which traders exchange sugar (or sugar and spice), the authors show how the complex adaptive systems paradigm can be applied to the study of social phenomena. Illustrative applications include trade, migration, group formation, combat, interaction with an environment, transmission of culture, propagation of disease, and population dynamics.

  Joshua M. Epstein, Generative Social Science: Studies in Agent-Based Computational Modeling, Princeton University Press, Princeton, 356pp., 2006.

  Abstract: "Agent-based computational modeling is changing the face of social science. In (this book), Joshua Epstein argues that this powerful, novel technique permits the social sciences to meet a fundamentally new standard of explanation, in which one `grows' the phenomenon of interest in an artificial society of interacting agents: heterogeneous, boundedly rational actors, represented as mathematical or software objects."

  Joshua M. Espstein is a Senior Fellow in Economic Studies at The Brookings Institution, Washington D.C., and a member of the External Faculty of the Santa Fe Institute.

  Magda Fontana, "Computer Simulations, Mathematics, and Economics" (pdf,269K), Working Paper No. 06/2005, University of Torino, 2005. (Note: This paper was published with minor revisions as “Computer Simulations, Mathematics, and Economics”, Rivista Internazionale di Scienze Economiche e Commerciali, vo. 53(1), 2006, pp. 96-124.)

  Abstract: "This paper suggests a systematisation of the relationghip between simulations, mathematics, and economics. In particular, it traces the evolution of simulation techniques, comments on some of the contributions that deal with their nature, and, finally, illustrates with some examples their influence on economic theory."

  Nigel Gilbert, Agent-Based Social Simulation: Dealing with Complexity (pdf,281K), centre for Research on Social Simulation, University of Surrey, Guildford, UK, 18 December 2004.

  Abstract: This non-technical but carefully written paper examines recent advances in the application of computer simulation to the social sciences. Illustrative examples are used to show how this new methodology is appropriate for analyzing social phenomena that are inherently complex, and how it encourages experimentation and the study of emergence.

  Mark Granovetter, "A Theoretical Agenda for Economic Sociology" (pdf,253K), in M. F. Guillen, R. Collins, P. England, and M. Meyer (eds.), Economic Sociology at the Millenium, Russell Sage Foundation, New York, 2001.

  Abstract: Economic sociology is concerned with the study of economic processes imbedded in social contexts and relational (interaction) networks. This paper reassesses what theoretical agenda a structural economic sociology might pursue, and where this agenda fits with the main concerns of sociology and economics.

  Nicholas R. Jennings, On Agent-Based Software Engineering (pdf,257K), Artificial Intelligence 117 (2000), 277-296, copyright © 2002 Elsevier Science B.V. All rights reserved.

  Abstract: Agent-based computing represents an exciting new synthesis both for Artificial Intelligence (AI) and, more generally, Computer Science. It has the potential to significantly improve the theory and the practice of modeling, designing, and implementing computer systems... The standpoint of this analysis is the role of agent-based software in solving complex, real-world problems. In particular, it will be argued that the development of robust and scalable software systems requires autonomous agents that can complete their objectives while situated in a dynamic and uncertain environment, that can engage in rich, high-level social interactions, and that can operate within flexible organizational structures.

  Kenneth L. Judd, "Computationally Intensive Analyses in Economics" (pdf,115K), in Leigh Tesfatsion and Kenneth L. Judd (editors), Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics (Preface, Table of Contents, & Abstracts,html), Handbooks in Economics Series, Elsevier/North-Holland, Amsterdam, the Netherlands, 2006.

  Abstract: Computer technology presents economists with new tools, but also raises novel methodological issues. This essay discusses the challenges faced by computational researchers, and proposes some solutions.

  Aki Lehtinen and Jaakko Kuorikoski, "Computing the Perfect Model: Why Do Economists Shun Simulation?" (pdf,115K), Philosophy of Science 74 (July 2007) pp. 304–329.

  Abstract: "Like other mathematically intensive sciences, economics is becoming increasingly computerized. Despite the extent of the computation, however, there is very little true simulation. Simple computation is a form of theory articulation, whereas true simulation is analogous to an experimental procedure. Successful computation is faithful to an underlying mathematical model, whereas successful simulation directly mimics a process or a system. The computer is seen as a legitimate tool in economics only when traditional analytical solutions cannot be derived, i.e., only as a purely computational aid. We argue that true simulation is seldom practiced because it does not fit the conception of understanding inherent in mainstream economics. According to this conception, understanding is constituted by analytical derivation from a set of fundamental economic axioms. We articulate this conception using the concept of economists' perfect model. Since the deductive links between the assumptions and the consequences are not transparent in ‘bottom-up' generative microsimulations, microsimulations cannot correspond to the perfect model and economists do not therefore consider them viable candidates for generating theories that enhance economic understanding."

  Michael W. Macy and Robert Willer, "From Factors to Actors: Computational Sociology and Agent-Based Modeling" (pdf,134K), Annual Review of Sociology, Vol. 28, 2002, pp. 143-166. Published article available (in 2007) at JSTOR.

  Abstract: While written for sociologists, this review article should be of value to all agent-based modelers. It places agent-based modeling in its historical context, explains its meaning and goals, provides many good examples, and offers useful advice to those who want to try it for themselves.

  John Miller and Scott E. Page, "The Standing Ovation Problem" (pdf,141K), Complexity, Vol. 9, No. 5, May/June 2004, pp. 8-16. Published article available at journal back-issues site at Wiley InterScience.

  Abstract: Miller and Page use audience ovation to introduce many key ABM themes, in particular the emergence of collective behavior, and to provide specific modeling suggestions suitable for implementation by newcomers to the field. As a public performance draws to a close, and audience members begin to applaud and some even tentatively to stand, will a standing ovation ensue or not? This is the famous Standing Ovation Problem (SOP) inspired by the seminal work of Thomas Schelling on the relationship between micro decisions and macro behaviors (see the Section on Complexity and ABM, above). Miller and Page use the SOP to illustrate how complex social dynamics can arise from the interactions among simple personal choices, in this case to stand or not. They argue (p. 9) that the success of the SOP as an expository device is that it forces modelers "to confront the core methodological issue in complex adaptive social systems, namely, how does one model a system of thoughtful, interacting agents in time and space?"

  John H. Miller and Scott E. Page, Complex Adaptive Systems: Introduction to Computational Models of Social Life, Princeton Studies in Complexity, Princeton University Press, 284pp., March 2007.

  Abstract: "This book provides the first clear, comprehensive, and accessible account of complex adaptive social systems, by two of the field's leading authorities. Such systems -- wehter political parties, stock markets, or ant colonies -- present some of themost intriguing theoretical and practical challenges confronting the social sciences. Engagingly written, and balancing technical detail with intuitive explanations, (this book) focuseds on thekey tools and ideas that have emerged in the field since the mid-1990s, as well as the techniques needed to investigate such systems."

  John H. Miller is Professor of Economics and SOcial Sciences at Carnegie Mellon University. Scott E. Page is Professor of Complex Systems, Political Science, and Economics at the University of Michigan.

  Denis Phan and Frédéric Amblard, Agent-Based Modelling and Simulation in the Social and Human Sciences, The Bardwell Press, Oxford, UK, 2007.

  Abstract: "This volume brings together contributions from leading researchers in the field of agent-based modeling and simulation. ... The research case studies and theoretical approaches discussed in this book are designed to introduce beginners and experts alike to the current state of play in this new and exciting field of social science."

  Jonathan Rauch, Seeing Around Corners (pdf,4.2M), The Atlantic, April 2002.

  Abstract: This feature article traces the history of artificial societies from its roots in the work of Thomas Schelling to Axtell and Epstein's Sugarscape Model. Rauch discusses (Brookings Institution) research on Zipf distributions, A-societies and the Anasazi tribe, to posit that the construction of artificial communities provides social scientists with a method to predict the outcomes of particular policy interventions.

  Herbert Simon, "The Architecture of Complexity", pp. 193-230 in The Sciences of the Artificial, Second Edition, The MIT Press, Cambridge, MA, 1982.

  Abstract: Simon informally defines a "complex system" to be a system made up of a large number of parts that interact in a non-simple way. He considers a number of complex systems encountered in the behavioral sciences, from families to formal organizations, and describes features that are common in a wide variety of such systems. His central theme (p. 196) is that "complexity frequently takes the form of hierarchy and that hierarchic systems have some common properties independent of their specific content." He discusses the design advantages of nearly decomposable subsystems with a hierarchical organization of their parts. He also conjectures that complex systems evolve from simple systems much more rapidly if there are stable intermediate forms along the way, hence evolution favors hierarchic over non-hierarchic systems.

  Leigh Tesfatsion, Agent-Based Computational Economics: A Constructive Approach to Economic Theory (pdf,253K), in Leigh Tesfatsion and Kenneth L. Judd (eds.), Handbook of Computational Economics, Volume 2: Agent-Based Computational Economics, Handbooks in Economics Series, Elsevier/North-Holland, the Netherlands, 2006.

  Abstract: This chapter explores the potential advantages and disadvantages of ACE for the study of economic systems. General points are concretely illustrated using an ACE model of a two-sector decentralized market economy. Six issues are highlighted: Constructive understanding of production, pricing, and trade processes; the essential primacy of survival; strategic rivalry and market power; behavioral uncertainty and learning; the role of conventions and organizations; and the complex interactions among structural attributes, behavior, and institutional arrangements.

  Leigh Tesfatsion, Agent-Based Computational Economics (ppt,101K), Computations in Science Distinguished Lecture Series presentation, Computation Institute, University of Chicago, October 27, 2003 (revised Spring 2004).

  Leigh Tesfatsion, Economic Agents and Markets as Emergent Phenomena (pdf,167K), Proceedings of the National Academy of Sciences U.S.A., Vol. 99, Suppl. 3, 2002, 7191-7192.

  Abstract: A brief overview of recent work in agent-based computational economics is provided, with a stress on the application areas highlighted in the NAS Sackler Colloquium session "Economic Agents and Markets as Emergent Phenomena" held in October 2001.

  Leigh Tesfatsion, Agent-Based Computational Economics: Growing Economies from the Bottom Up [ (pdf,216K), (ps,353K)], Artificial Life, Volume 8, Number 1, 2002, 55-82, published by the MIT Press. Also available is a modified version (pdf,269K) ("Agent-Based Computational Economics," Economics Working Paper No. 1, Iowa State University, Ames, IA, June 2002, Revised August 2003) directed more specifically to economists.

  Abstract: This study is an extended version of the previous PNAS overview. The main objectives and defining characteristics of agent-based computational economics (ACE) are outlined, and similarities and distinctions between ACE and artificial life research are clarified. Eight ACE research areas are identified, and a number of publications in each area are highlighted for concrete illustration. Open questions and directions for future ACE research are also considered. The study concludes with a discussion of the potential benefits of the ACE approach, as well as some potential difficulties.

  Leigh Tesfatsion, Introduction to the special ACE double-issue for the Journal of Economic Dynamics and Control (Volume 25, Numbers 3-4, March 2001, pp. 281-293), Introduction to the special ACE issue for Computational Economics (Volume 18, Number 1, October 2001), and Guest Editorial for the special issue of the IEEE Transactions on Evolutionary Computation on Agent-Based Modelling of Evolutionary Economic Systems (Volume 5, Number 5, October 2001, pp. 437-441).

  Leigh Tesfatsion and Kenneth L. Judd (eds.), Handbook of Computational Economics, Volume 2: Agent-Based Computational Economics, Handbooks in Economics Series, Elsevier/North-Holland, the Netherlands, 2006, 904pp. Preface, Table of Contents, and Abstracts, Publisher Book and Order Information

  Abstract: This handbook volume surveys recent research on Agent-based Computational Economics (ACE), the computational study of economic processes modeled as dynamic systems of interacting agents. Empirical referents for "agents" in ACE models can range from individuals or social groups with learning capabilities to physical world features with no cognitive function. The handbook includes 16 chapters surveying ACE research, 6 general perpectives on ACE (by Arthur, Axelrod, Epstein, Howitt, Leijonhufvud, and Schelling), and an appendix providing a guide for newcomers to agent-based modeling in the social sciences (by Axelrod and Tesfatsion). Topics covered include: learning; empirical validation; network economics; social dynamics; financial markets; innovation and technological change; organizations; market design; automated markets and trading agents; political economy; social-ecological systems; computational laboratory development; and methodological issues.

  Tamás Vicsek, "Complexity: The Bigger Picture" (pdf,71K), Nature, Vol. 418, Issue 6894, 11 July 2002, p. 131.

  Abstract: In this short essay, Vicsek describes how computer simulation fits into the scientific enterprise. The goal is to "capture the principal laws behind the exciting variety of new phenomena that become apparent when the many units of a complex system interact."

Other Introductory Materials

• ACE/CAS Surveys

• ACE/CAS Introductory Books

• Artificial Intelligence: Dynamic Library of Introductory Information, an extensive categorized collection of annotated AI-related links maintained by the American Association for Artificial Intelligence (AAAI).

• Robert Axelrod, Advancing the Art of Simulation in the Social Sciences, Complexity, Vol. 3/No. 2, November/December 1997, 16-22.

• W. Brian Arthur, Steven N. Durlauf, and David A. Lane, The Economy as an Evolving Complex System II, Proceedings Volume XXVII, Santa Fe Institute Studies in the Sciences of Complexity, Addison-Wesley, Reading, MA, 1997.

• David F. Batten, "Preface" plus Chapter 1:"Chance and Necessity" (pdf preprint-no figures,247K) plus Chapter 8: "Artificial Economics" (pdf preprint-no figures,173K) in Discovering Artificial Economics: How Agents Learn and Economies Evolve, Perseus Books, Westview Press, 2000.
  IMPORTANT NOTE: The Batten book is unfortunately out of print. However, a pdf file for the entire Batten book (including figures) can be accessed here (pdf,17MB).

  Shu-Heng Chen, Lakhmi Jain, and Chung-Ching Tai, Computational Economics: A Perspective from Computational Intelligence, Idea Group Inc., 318pp., 2006.

  Abstract: "(This book) provides models of various economic and financial issues while using computational intelligence as a foundation. The scope of this volume comprises finance, economics, management, organizational theory and public policies. It explains the ongoing and novel research in this field, and displays the power of these computational methods in coping with difficult problems with methods from traditional perspectives."

  David Colander (ed.), The Complexity Vision and the Teaching of Economics, Edward Elgar Pub., June 2000, 328 pp., ISBN: 1-840-64252-1.

  Abstract: From the publisher: "This ground-breaking book focuses on the implications of the complexity vision, such as that held by economists at the Santa Fe Institute, for the teaching of economics. ... It asks the question: how would the teaching of economics change if complexity is taken seriously? An outstanding group of contributors, including Brian Arthur, Buz Brock, and Duncan Foley, provide interesting and provocative answers to that question in a non-technical and highly accessible style."

  Joshua Epstein, Agent-Based Computational Models and Generative Social Science, Complexity, Vol. 4/No. 5, May/June 1999, 41-60.

  Dominique Gross and Roger Strand, Can Agent-Based Models Assist Decisions on Large-Scale Practical Problems? A Philosophical Analysis, Complexity, Vol. 5/No. 6, July/August 2000, 26-33.

  John H. Holland, Hidden Order: How Adaptation Builds Complexity, Addison-Wesley, 1995, ISBN 0-201-40793-0.

  Abstract: Clear and thoughtful discussion of the characteristics of complex adaptive systems, both man-made and natural. Proposes a possible tractable approach to the modelling of complex adaptive systems. Illustrates in concrete terms how this approach might be applied to the modelling of ecological systems (the Echo framework).

  Steven Johnson, Emergence: The Connected Lives of Ants, Brains, Cities, and Software, Scribner, 288 pp., Sept. 2001. ISBN: 0-684-86875-X.

  Abstract: From the publisher: "Drawing upon evolutionary theory, urban studies, neuroscience, and computer games, Emergence is a guidebook to one of the key components of twenty-first-century culture. Until recently, Johnson explains, the disparate philosophers of emergence have worked to interpret the world. But today they are starting to change it. This book is the riveting story of that change and what it means for the future. If you've searched for information on the Web, played a recent video game, or accepted a collect call using voice recognition software, you've already encountered the new world of artificial emergence. Provocative, engaging, and sophisticated, Emergence puts you on the front lines of a sweeping revolution in science and thought."

  David A. Kendrick, P. Ruben Mercado, and Hans M. Amman, Computational Economics, Princeton University Press, Princeton, NJ, 2006.

  Abstract: Chapters 11 through 14 of this book provide illustrive GAMS/Matlab implementations of genetic algorithms as used in evolutionary games and portfolio models and agent-based computational macroeconomic and environmental models.

  Steven Levy, Artificial Life: A Report from the Frontier Where Computers Meet Biology, Random House, NY, 1992.

  Abstract: A bit dated, now, but still a highly entertaining introduction to the field of artificial life.

  Francesco Luna and Alessandro Perrone (eds.), Agent-Based Methods in Economics and Finance: Simulations in Swarm, Kluwer Academic Publishers, Dordrecht and London, 2001.

  Philip Mirowski, Machine Dreams: Economics Becomes a Cyborg Science, Cambridge University Press, Cambridge, UK, 655 pp., 2002. ISBN: 0-521-77526-4.

  Abstract: From a review by Duncan Foley (New School University): "(This book) is an astonishing performance of synthetic scholarship. Mirowski traces the present-day predicaments of economic theory to its intellectual reformulation and institutional restructuring by military funding and in the crucibles of World War II and the cold war. His demonstration that the mathematical economics of the postwar era is a complex response to the challenges of `cyborg science,' the attempt to unify the study of human beings and intelligent machines through John von Neumann's general theory of automata, is bound to be controversial. His critics, however, will have to contend with a breathtakingly wide range of published and unpublished evidence in fields ranging from psychology to operations research he presents. This noir history of economic thought will change its readers' understanding of twentieth-century economics profoundly."

  Philip Mirowski is Carl Koch Professor of Economics and the History and Philosophy of Science at the University of Notre Dame, Indiana.

  Mitchel Resnick, Unblocking the Traffic Jams in Corporate Thinking: Models of Decentralized Systems, Complexity, Vol. 3/No. 4, March/April 1998, pp. 27-30.

  Mitchel Resnick, Turtles, Termites, and Traffic Jams: Explorations in Massively Parallel Microworlds, MIT Press, Cambridge, MA, 1994, ISBN: 0-262-18162-2.

  Abstract: Focuses on decentralized systems and self-organizing phenomena. Examines how and why people resist decentralized ideas, and describes a new computer language, StarLogo, designed to help people explore decentralized systems and move beyond the centralized mindset.

  Thomas Schelling, Micromotives and Macro Behavior, Norton, N.Y., 1978.

  Abstract: From the publisher: "Through familiar and readily grasped examples, Professor Schelling demonstrates what happens when behavior in the aggregate is more than a simple summation of individual behaviors, how members of a society tend to be blind to the collective consequences of their separate decisions, and why attempts to infer individual intentions from group phenomena are tricky at best and often downright impossible."

  Karl Sigmund, Games of Life: Explorations in Ecology, Evolution, and Behavior, Oxford University Press, 1993.

  Abstract: Great introductory presentation of various models of evolution. Topics include: population ecology and chaos; random drift and chain reactions; population genetics; evolution and sex; evolutionary game theory; and reciprocity and the evolution of cooperation.

Other Miscellaneous On-Line Materials

• Leigh Tesfatsion (Economics, ISU) offers an introductory course each Spring on Agent-Based Computational Economics (Econ 308). The syllabus for this course, available here, is organized as a self-study guide for students and long-distance learners alike. Resources available at this site (mostly on-line) include lecture notes, readings, homework exercises, student projects, and related links.

• Geoffrey M. Hodgson, Darwinism in Economics: From Analogy to Ontology (pdf,120K), Journal of Evolutionary Economics 12 (2000), 259-281.
  Abstract: The author argues that objections to `biological analogies' and `Darwinism' put forward by several social scientists, including evolutionary economists, are ungrounded. He argues that Darwinism includes a broad theoretical framework for the analysis of the evolution of all open, complex systems, including socio-economic systems. He concludes by noting that the recognition of the relevance of Darwinism does not undermine the need for auxiliary theories and explanations in the economic domain.

  Paul Krugman, What Economists Can Learn from Evolutionary Theorists (html), Presentation, European Association for Evolutionary Political Economy, November, 1996.

  Abstract: A cautious assessment of the potential of evolutionary thinking for economic analysis. In particular, Krugman argues that evolutionary economists should not be too quick to abandon the use of equilibrium modeling since many scientists in other disciplines make use of equilibrium assumptions.

  [Note by LT: A further question must be raised here, I believe. How many scientists in other disciplines follow the common practice of economic theorists of incorporating equilibrium conditions into their system descriptions as untested maintained hypotheses, without consideration of their basins of attraction and hence their likelihood of occurrence and persistence? There is nothing wrong with incorporating equilibrium assumptions once their empirical relevance has been established, but who wants to spend time studying eggs on their tip points?]

  Thomas S. Ray (Department of Zoology, University of Oklahoma) maintains an interesting on-line introduction to artificial life originally developed in 1996.

  Abstract: "Artificial Life (AL) extends the field of biology by allowing us to study living forms other than those occurring naturally on Earth. In this way, AL bears the same relationship to biology that synthetic chemistry does to chemistry. Some of the most significant advances in AL have been in the area of synthetic evolutions within computers. One of the major currents in this work has been to move towards systems which evolve freely within the digital medium, like the evolution by natural selection in the carbon medium that generated life on Earth. The primary objective of this work is to provoke digital evolution to generate complexity within the digital medium, comparable in magnitude to the complexity of organic life."

  Leigh Tesfatsion (Economics, ISU) maintains an annotated list of pointers to ACE/CAS Computational Laboratories and Demonstration Software. The goal of this site is to facilitate the understanding of the ACE methodology by providing people with an opportunity to gain hands-on experience running simple ACE/CAS experiments under different parameter settings with no original programming required and with rapid visual feedback of findings.

  Zooland, maintained by Jörg Heitkötter, provides pointers to an extensive catalogued collection of resources on artificial life. Topics covered include computer viruses, artificial life simulators, infospiders, biomorphs, L-systems, cellular automata, and much more.

Copyright © 2009 Leigh Tesfatsion. All Rights Reserved.