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
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
Can ACE facilitate the scientific study of economic processes?
Robert Axelrod and Leigh Tesfatsion, An On-Line Guide for Newcomers to
Agent-Based Modeling in the Social Sciences(html,44KB)
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,46KB),
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.
Jason M. Barr et al., The Future of Agent-Based Research in Economics: A Panel Discussion(pdf,138KB),
Eastern Economic Journal 34 (2008), 550-565.
Abstract: Exceptionally thoughtful commentaries on the theory and practice of ABM by a group of co-moderators (Jason Barr, Troy Tassier, Leanne Ussher) and panelists (Blake LeBaron, Shu-Heng Chen, Shyam Sunder) at the Eastern Economic Association Meeting held in Boston, March 7, 2008. Well worth reading.
Paul L. Borrill and Leigh Tesfatsion, "Agent-Based Modeling: The Right Mathematics for the Social Sciences?"(pdf,3.5MB),
pp. 228-258 in J.B. Davis and D.W. Hands (eds.), Elgar Companion to Recent Economic Methodology, Edward Elgar Publishers, February 2011, 560pp. ISBN-13: 9781848447547
This study provides a basic introduction to agent-based modeling (ABMB) as a powerful blend of classical and constructive mathematics, with a primary focus on its applicability for social science research.
The typical goals of ABM social science researchers are discussed along with the culture-dish nature of their computer experiments. The applicability of ABM for science more generally is also considered, with special attention to physics. Finally, two distinct types of ABM applications are summarized in order to illustrate concretely the duality of ABM: Real-world systems can not only be simulated with verisimilitude using ABM; they can also be efficiently and robustly designed and constructed on the basis of ABM principles.
Andrei Borshchev and Alexei Filippov, "From System Dynamics and Discrete Event to Practical Agent-Based Modeling: Reasons, Techniques, and Tools"(pdf,308KB),
Proceedings, 22nd International Conference of the System Dynamics Society, Oxford, England, July 25-29, 2004.
The authors compare three major paradigms in simulation modeling: system dynamics, discrete event modeling, and agent-based modeling.
Shu-Heng Chen, "Varieties of Agents in Agent-Based Computational Economics: A Historical and an Interdisciplinary Perspective"(Preprint pdf,1.2MB),
Journal of Economic Dynamics and Control 36(1), January 2012, 1-25
Abstract: This thoughtful and comprehensive study traces the origins of agent-based computational economics (ACE) through four different gateways: namely, study of market processes; study of cellular automata with fixed rules of behavior; evolution-of-cooperation tournaments with programmed strategies; and experiments with autonomous human-like agents (artificial life).
Alan Kirman, "The Economic Entomologist: An Interview with Alan Kirman"(Preprint pdf,236KB),
Erasmus Journal for Philosophy and Economics 4(2), Autumn, 2011.
Abstract: This article presents an engaging wide-ranging interview with Alan Kirman, one of the seminal explorers and advocates of an interactionist approach to the study of economic systems to bridge the divide between individual behavior and aggregate patterns.
Charles M. Macal and Michael J. North, "Tutorial on Agent-Based Modelling and Simulation"(pdf,359KB),
Journal of Simulation, Vol. 4, 2010, 151–162
This article provides a brief introduction to agent-based modeling and simulation. The authors illustrate the main concepts and foundations, discuss some recent applications across a variety of disciplines, and identify methods and toolkits for developing agent models.
Flaminio Squazzoni, "The Impact of Agent-Based Models in the Social Sciences After 15 Years of Incursions(pdf,248KB),
History of Economic Ideas, XVIII/2010/2.
Abstract: The author provides a retrospective overview of the impact of agent-based modeling (ABMB) on social science over the past fifteen years. He discusses contributions that ABM has made to economics, sociology, anthropology, and the behavioral sciences, new fields that ABM has opened up, and issues that ABM social science researchers need to address in the future.
Leigh Tesfatsion, Tutorial on Agent-Based Computational Economics(ppt,2.4MB),
Latest Revision: 24 March 2013.
Leigh Tesfatsion, Agent-Based Modeling: A Bridge Between Games & Social Sciences(pdf,997KB).
Abstract: This short presentation, designed for younger newcomers to Agent-Based Modeling (ABMB), 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)
Paola Tubaro, Agent-Based Computational Economics: A Methodological Appraisal(pdf,176KB),
EconomiX Working Paper 2009-42, University of Paris, July 2009.
"This paper is an overview of `Agent-based Computational Economics' (ACE), an emerging approach to the study of decentralized market economies, in methodological perspective. It summarizes similarities and differences with respect to conventional economic models, outlines the unique methodological characteristics of this approach, and discusses its implications for economic methodology as a whole."
Peter Albin, Preface (pp. xiii-xxxi)(pdf,146KB)
and Duncan K. Foley, Chapter 1: "Introduction(pdf,369KB),
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.
W. Brian Arthur, "Complexity Economics: A Different Framework for Economic Thought"(pdf,781KB),
Santa Fe Institute Working Paper: 2013-4-2012.
"This paper provides a logical framework for complexity economics. Complexity economics builds from the proposition that the economy is not necessarily in equilibrium: economic agents (firms, consumers, investors) constantly change their actions and strategies in response to the outcome they mutually create. This further changes the outcome, which requires them to adjust afresh. Agents thus live in a world where their beliefs and strategies are constantly being "tested" for survival within an
outcome or "ecology" these beliefs and strategies together create. Economics has largely avoided this nonequilibrium view in the past, but if we allow it, we see patterns or phenomena not visible to equilibrium analysis. These emerge probabilistically, last for some time and dissipate, and they correspond to complex structures in other fields. We also see the economy not as something given and existing but forming from a constantly developing set of technological innovations, institutions, and arrangements that draw forth further innovations, institutions and arrangements."
W. Brian Arthur, Complexity Economics, Oxford University Press, 2013.
This book surveys the birth and growth over the past twenty-five years of a different approach to economics, termed complexity economics. This approach holds that an economy is not necessarily in equilibrium, that computation as well as mathematics is useful in economics, that increasing as well as diminishing returns may be present in an economic situation, and that the economy is not something given and existing but forms from a constantly developing set of institutions, arrangements, and technological innovations. Questions addressed include: What does this different way of thinking about the economy offer? How exactly does it work and where does it fit in? Will it replace neoclassical economics, or be subsumed into neoclassical economics? And under what logic, if any, does it operate?
Sunny Y. Auyang, Synthetic Analysis of Complex Systems I - Theories(html,11pp),
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.
Sunny Y. Auyang, "Foundations of Complex-System Theories in Economics, Evolutionary Biology, and Statistical Physics", Cambridge University Press, Cambridge, UK, 1998.
Abstract: "This book continues my effort to uncover the categorical framework of objective thought as it is embedded in scientific theories and common sense....Composition is not merely congregation; the constitutents of a compound interact and the interaction generates complicated structures. ...How does science represent and explain the complexity of composition?"
Robert Axelrod, Advancing the Art of Simulation in the Social
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,256KB),
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,115KB),
Center on Social and Economic Dynamics, Working Paper No. 17, November 2000.
Rob Axtell, The New Coevolution of Information Science and
Working Paper, Brookings Institution, 2003.
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."
Forecast: What Physics, Meteorology, and the Natural Sciences can Teach Us About Economics(booksite), Bloomsbury, New York, 2013.
"In all these books (on the global economic crisis), I felt one thing was missing - an examination of the peculiar concepts of economic thinking, of the atmosphere of ideas of modern economic theory, which swayed many people to believe that the tumultuous history of economics and finance, a history of almost continual crises and disruption going back four hundred years, had somehow come to a miraculous end in our era because of the markets' self-regulating nature and tendency towards `equilibrium.' I've written this book to help fill this gap, and also to explore more constructive ideas for building a more realistic and more natural understanding of economic systems."
Agent-Based Computational Economics,
Book Series: Advances in Experimental and Computable Economics, Routledge Publishers, October 2013, 256pp.
"This book provides a review of the development of agent-based computational economics (ACE) from a perspective on how artificial economic agents are designed under the influences of complex sciences, experimental economics, artificial intelligence, evolutionary biology, psychology, anthropology and neuroscience. The review also includes how these agents are networked by using ideas from physics, mathematics and sociology. Despite this rich interdisciplinary colour, the book has a simple fundamental pursuit, that is, to use ACE to clothe economics with Marshall’s spirit: `Economics, like biology, deals with a matter, of which the inner nature and constitution, as well as outer form, are constantly changing.'"
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.
Kalman J. Cohen, "Simulation of the Firm", The American Economic Review, 50(2), 1960, 534–540.
Abstract: This perceptive article, written in 1960,
forcefully and clearly sets out the conceptual case for "agent-based computational economics" years in advance of the software tools permitting its practical realization.
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
at the PNAS website (www.pnas.org).
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 booKB), 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,269KB),
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 relationship 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 Models,
Quantitative Applications in the Social Sciences, Volume 153, SAGE Publications, Inc., 2008.
Abstract: The author reviews a range of illustrative agent-based modeling (ABMB) applications. He also provides practical advice regarding the designing and building of ABMs, model verification and empirical validation, planning a modeling project, and the writing of scholarly ABM articles. Last but not least, he provides a glossary of terms, an annotated list of resources, advice on programming languages and toolkits, and a step-by-step worked out example of an ABM implementation.
Agent-Based Social Simulation: Dealing with Complexity(pdf,281KB),
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,253KB),
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.
Cars Hommes, "Behavioral Rationality and Heterogeneous Expectations in Complex Economic Systems", Cambridge University Press, January 2013. Introductory materials (including Chapter 1) can be viewed
"Recognising that the economy is a complex system with boundedly rational interacting agents, the book presents a theory of behavioral rationality and heterogeneous expectations in complex economic systems and confronts the nonlinear dynamic models with empirical stylized facts and laboratory experiments.
... The complexity tools - bifurcations, chaos, multiple equilibria - discussed in this book will help students, researchers and policy makers to build more realistic behavioral models with heterogeneous expectations to describe financial market movements and macro-economic fluctuations, in order to better manage crises in a complex global economy."
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
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
Aki Lehtinen and Jaakko Kuorikoski, "Computing the Perfect Model: Why Do Economists Shun Simulation?"(pdf,115KB),
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,134KB),
Annual Review of Sociology, Vol. 28, 2002, pp. 143-166. Published article available (in
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
Robert E. Marks and Nicolaas J. Vriend,
"Agent-Based Computational Economics Overview"(pdf,71KB),
The Knowledge Engineering Review 27(2), 2012, 115-122.
Abstract "The Knowledge Engineering Review is an outstanding journal in Computer Science. The guest editors and contributors to these two Special Issues are economists. Why is this so? In recent years there has been a growing dialogue between economists and computer scientists, to our mutual benefit. The Special Issues are devoted to nine papers (five in Part 1 and four in Part 2) in which economists survey aspects of the field of agent-based computational economics (ACE) models, and in some cases report on new findings in several areas of application. As such, we hope they have something to offer both computer scientists and economists."
John Miller and Scott E. Page, "The Standing Ovation
Complexity, Vol. 9, No. 5, May/June 2004, pp. 8-16.
Published article available at journal back-issues site at
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.
"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 booKB) 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."
Steven F. Railsback and Volker Grimm,
Agent-based and Individual-based Modeling:
A Practical Introduction,
Princeton University Press, 2012, 321 pp, ISBN 978-0-691-13673-8.
(Book Page at PUP)
This textbook is designed for university courses
and self-instruction by physical, biological, and
social scientists. Its four parts provide: an introduction to the basics of modeling
and individual- or agent-based modeling; in-depth experience with an established
conceptual framework for designing models and with programming models
in NetLogo software; familiarity with the "pattern-oriented modeling" strategy for developing
theory for agent behavior and calibrating models; and experience analyzing models
to produce both applied and theoretical science. The authors support the book with on-line materials and occasional short-courses; visit
to access this book resource site.
Jonathan Rauch, Seeing Around Corners(pdf,4.2MB),
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.
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.
Abstract: The idea of this book is that most of the intriguing social phenomena of our time, such as international terrorism, social inequality, and urban ethnic segregation, are consequences of complex forms of agent interaction that are difficult to observe methodically and experimentally. This book looks at a new research stream that makes use of advanced computer simulation modelling techniques to spotlight agent interaction that allows us to explain the emergence of social patterns. It presents a method to pursue analytical sociology investigations that look at relevant social mechanisms in various empirical situations, such as markets, urban cities, and organisations.
Leigh Tesfatsion, Agent-Based Computational Economics: A Constructive
Approach to Economic Theory(pdf,253KB),
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.
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, Economic Agents and Markets as Emergent
Proceedings of the National Academy of Sciences U.S.A.,
Vol. 99, Suppl. 3, 2002, 7191-7192.
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
Volume 8, Number 1, 2002, 55-82, published by the MIT Press.
Also available is a
modified version (pdf,269KB)
("Agent-Based Computational Economics," Economics Working Paper No. 1, Iowa
State University, Ames, IA, June 2002, Revised August 2003) directed more
specifically to economists.
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.
to the special ACE double-issue for the Journal of Economic Dynamics and
Control (Volume 25, Numbers 3-4, March 2001, pp. 281-293),
to the special ACE issue for Computational Economics (Volume 18,
Number 1, October 2001), and
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).
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.
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."
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.
IMPORTANT NOTE: The Batten book is unfortunately out of print. However, a pdf file for the entire Batten book (including figures) can be accessed
Shu-Heng Chen, Lakhmi Jain, and Chung-Ching Tai, Computational Economics: A Perspective from Computational Intelligence, Idea Group Inc., 318pp., 2006.
Abstract: "(This booKB) 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:
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:
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: 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:
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:
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
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
Leigh Tesfatsion (Economics, ISU) offers an introductory course each
Spring on Agent-Based Computational Economics (Econ 308).
The syllabus for this course, available
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
Geoffrey M. Hodgson, Darwinism in Economics: From Analogy to
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.
What Economists Can Learn from Evolutionary Theorists(html),
Presentation, European Association for Evolutionary Political Economy,
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
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
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
maintained (1995-2004) 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.