Handbook of Computational Economics, Vol. 2:
Agent-Based Computational Economics

Preface, Topics, Contributors, and Chapter Abstracts

Last Updated: 25 September 2010

Site maintained by:

Leigh Tesfatsion

Department of Economics

Iowa State University

Ames, Iowa 50011-1070

https://faculty.sites.iastate.edu/tesfatsi

tesfatsi AT iastate.edu

Volume Details:

Volume Co-Editors: Leigh Tesfatsion and Kenneth L. Judd

Publisher: Elsevier/North-Holland (Handbooks in Economics Series)

General Handbook Series Editors: Kenneth J. Arrow and Michael D. Intriligator

Executive Publisher Economics: Valerie Teng

Development Editor, Social Science and Economics: Shamus O'Reilly

Publication Date: May 2006

Publisher Book and Order Information for ACE Handbook

ACE Website

PREFACE

"Preface" (58K)

Co-Contributors (Handbook Co-Editors):

Leigh Tesfatsion

Department of Economics

Iowa State University

Ames, Iowa 50011-1070

Office Telephone: +1-515-294-0138

FAX: +1-515-294-0221

Email: tesfatsi AT iastate.edu

Kenneth L. Judd

Hoover Institution

Stanford, CA 94305

Office Telephone: +1-650-725-5866

FAX: +1-650-723-1687

Email: judd@hoover AT stanford.edu

PART 1: ACE Research Reviews

CHAPTER 16. AGENT-BASED COMPUTATIONAL ECONOMICS:
A CONSTRUCTIVE APPROACH TO ECONOMIC THEORY

Contributor:

Leigh Tesfatsion (Professor of Economics and Courtesy Professor of Mathematics, Iowa State University, Ames, Iowa, tesfatsi AT iastate.edu): Agent-based computational economics; Network economics; Market design; Market power, efficiency, and reliability in restructured electricity markets with strategically interacting agents; Labor institutions and the evolution of macroeconomic performance; Evolution of trade networks; The Trade Network Game (TNG) Laboratory.

Abstract:

Economies are complicated systems encompassing micro behaviors, interaction patterns, and global regularities. Whether partial or general in scope, studies of economic systems must consider how to handle difficult real-world aspects such as asymmetric information, imperfect competition, strategic interaction, collective learning, and the possibility of multiple equilibria. Recent advances in analytical and computational tools are permitting new approaches to the quantitative study of these aspects. One such approach is Agent-based Computational Economics (ACE), the computational study of economic processes modeled as dynamic systems of interacting agents. 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, institutional arrangements, and behavioral dispositions.

CHAPTER 17. COMPUTATIONALLY INTENSIVE ANALYSES IN ECONOMICS

Contributor:

Kenneth L. Judd (Paul H. Bauer Senior Fellow, Hoover Institution, Stanford, CA, judd AT hoover.stanford.edu): Computational methods for economic modeling; Economics of taxation and imperfect competition; Mathematical economics.

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.

CHAPTER 18. AGENT LEARNING REPRESENTATION: ADVICE ON MODELLING ECONOMIC LEARNING

Contributor:

Thomas Brenner (Research Associate, Max Planck Institute of Economics, Evolutionary Economics Group, Jena, Germany, brenner AT econ.mpg.de): Learning processes in economics; Evolutionary games; Agent-based computational economics.

Abstract:

This chapter presents an overview of the existing learning models in the economics literature. Furthermore, it discusses the choice of models that should be used under various circumstances and how adequate learning models can be chosen in simulation approaches. It gives advice for using the many existing models and selecting an appropriate model for each application.

CHAPTER 19. AGENT-BASED MODELS AND HUMAN-SUBJECT EXPERIMENTS

Contributor:

John Duffy (Associate Professor of Economics, University of Pittsburgh, Pennsylvania jduffy AT pitt.edu): Incorporation of learning in computational economic models; Using genetic algorithms to model how agents learn and adaptively update their forecasts; Parallel experiments with real and computational agents.

Abstract:

This chapter examines the relationship between agent-based modeling and economic decision-making experiments with human subjects. Both approaches exploit controlled "laboratory" conditions as a means of isolating the sources of aggregate phenomena. Research findings from laboratory studies of human subject behavior have inspired studies using artificial agents in "computational laboratories" and vice versa. In certain cases, both methods have been used to examine the same phenomenon. The focus of this chapter is on the empirical validity of agent-based modeling approaches in terms of explaining data from human subject experiments. We also point out synergies between the two methodologies that have been exploited as well as promising new possibilities.

CHAPTER 20. ECONOMIC ACTIVITY ON FIXED NETWORKS

Contributor:

Allen W. Wilhite (Professor and Chair of Economics, University of Alabama in Huntsville, wilhitea@uah.edu): Autonomous agents and artificial economics; Protection and social order; Self-organizing production and exchange; Bilateral trade and small world networks; Public choice.

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.

CHAPTER 21. ACE MODELS OF ENDOGENOUS INTERACTIONS

Contributor:

Nicolaas J. Vriend (Reader in Microeconomics, Department of Economics, Queen Mary and Westfield College, University of London, UK, n.vriend AT qmul.ac.uk): Dynamics of interactive market processes; Emergent properties of evolving market structures and outcomes; Learning algorithms; History of economic thought.

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 some (market or other) game, but also learn to) decide with whom to do that (or not).

CHAPTER 22. SOCIAL DYNAMICS: THEORY AND APPLICATIONS

Contributor:

H. Peyton Young (Scott and Barbara Black Professor of Economics, Johns Hopkins University, Baltimore, Maryland, pyoung AT jhu.edu): Individual strategy and social structure; Learning and evolution in games; Bargaining and negotiation; Public finance; Political representation and voting; Distributive justice.

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.

CHAPTER 23. HETEROGENEOUS AGENT MODELS IN ECONOMICS AND FINANCE

Contributor:

Cars Hommes (Professor of Economic Dynamics and Director of the Center for Nonlinear Dynamics in Economics and Finance, University of Amsterdam, The Netherlands, C.H.Hommes AT uva.nl): Complex adaptive systems; Multi-agent systems; Evolutionary dynamics; Expectations and learning; Bounded rationality; Bifurcations and chaos.

Abstract:

This chapter surveys work on dynamic heterogeneous agent models (HAMs) in economics and finance. Emphasis is given to simple models that, at least to some extent, are tractable by analytic methods in combination with computational tools. Most of these models are behavioral models with boundedly rational agents using different heuristics or rule of thumb strategies that may not be perfect, but perform reasonably well. Typically these models are highly nonlinear, e.g. due to evolutionary switching between strategies, and exhibit a wide range of dynamical behavior ranging from a unique stable steady state to complex, chaotic dynamics. Aggregation of simple interactions at the micro level may generate sophisticated structure at the macro level. Simple HAMs can explain important observed stylized facts in financial time series, such as excess volatility, high trading volume, temporary bubbles and trend following, sudden crashes and mean reversion, clustered volatility and fat tails in the returns distribution.

CHAPTER 24. AGENT-BASED COMPUTATIONAL FINANCE

Contributor:

Blake LeBaron (Abram L. and Thelma Sachar Professor of International Economics, Graduate School of International Economics and Finance, Brandeis University, Waltham, Massachusetts, blebaron AT brandeis.edu): Quantitative dynamics of interacting systems of adaptive agents, and how these systems replicate real world phenomena; Behavior of traders in financial markets; Nonlinear behavior of financial and macroeconomic time series.

Abstract:

This chapter surveys research on agent-based models used in finance. It concentrates on models where the use of computational tools is critical for the process of crafting models that give insights into the importance and dynamics of investor heterogeneity in many financial settings.

CHAPTER 25. AGENT-BASED MODELS OF INNOVATION AND TECHNOLOGICAL CHANGE

Contributor:

Herbert Dawid (Chair for Economic Policy, University of Bielefeld, Germany, hdawid AT wiwi.uni-bielefeld.de): Simulation studies of imitation and innovation in markets; Genetic algorithms as a model of social learning; Adaptive learning in games; Comparison of adaptive and optimal behavior.

Abstract:

This chapter discusses the potential of the agent-based computational economics approach for the analysis of processes of innovation and technological change. It is argued that, on the one hand, several genuine properties of innovation processes make the possibilities offered by agent-based modelling particularly appealing in this field, and that, on the other hand, agent-based models have been quite successful in explaining sets of empirical stylized facts, which are not well accounted for by existing representative-agent equilibrium models. An extensive survey of agent-based computational research dealing with issues of innovation and technological change is given and the contribution of these studies is discussed. Furthermore a few pointers towards potential directions of future research are given.

CHAPTER 26. AGENT-BASED MODELS OF ORGANIZATIONS

Co-Contributors:

Myong-Hun Chang (Professor of Economics, Cleveland State University, Cleveland, Ohio, m.chang AT csuohio.edu): Computational modeling of multi-level/multi-unit organizations; Decentralized learning and endogenous networks; Centralization versus decentralization in a multi-unit organization; Merger dynamics in asymmetric Cournot oligopoly.

Joseph E. Harrington, Jr., Corresponding Author (Professor of Economics, Johns Hopkins University, Baltimore, Maryland, joe.harrington AT jhu.edu): Industrial organization; Evolutionary economics; Organizations; Political economy; Game theory.

Abstract:

The agent-based approach views an organization as a collection of agents, interacting with one another in their pursuit of assigned tasks. The performance of an organization in this framework is determined by the formal and informal structures of interactions among agents, which define the lines of communication, allocation of information processing tasks, distribution of decision-making authorities, and the provision of incentives. This chapter provides a synthesis of various agent-based models of organizations and surveys some of the new insights that are being delivered. The ultimate goal is to introduce the agent-based approach to economists in a methodological manner and provide a broader and less idiosyncratic perspective to those who are already engaging in this line of work. The chapter is organized around the set of research questions that are common to this literature: 1) What are the determinants of organizational behavior and performance? 2) How does organizational structure influence performance? 3) How do the skills and traits of agents matter and how do they interact with structure? 4) How do the characteristics of the environment -- including its stability, complexity, and competitiveness -- influence the appropriate allocation of authority and information? 5) How is the behavior and performance influenced when an organization is coevolving with other organizations from which it can learn? 6) Can an organization evolve its way to a better structure?

CHAPTER 27. MARKET DESIGN USING AGENT-BASED MODELS

Contributor:

Robert Marks (Professor of Management, Australian Graduate School of Management, University of New South Wales, Sydney, Australia, r.marks AT unsw.edu.au): Strategic behavior in markets with small numbers of sellers; Application of economic theory to various social issues (e.g., illicit use of drugs, environmental impacts of energy use); Learning and adaptive behavior in oligopolies.

Abstract:

This chapter explores the state of the emerging practice of designing markets by the use of agent-based modeling, with special reference to electricity markets and computerized (on-line) markets, perhaps including real-life electronic agents as well as human traders. The chapter first reviews the use of evolutionary and agent-based techniques of analyzing market behaviors and market mechanisms, and economic models of learning, comparing genetic algorithms with reinforcement learning. Ideal design would be direct optimization of an objective function, but in practice the complexity of markets and traders' behavior prevents this, except in special circumstances. Instead, iterative analysis, subject to design criteria trade-offs, using autonomous self-interested agents, mimics the bottom-up evolution of historical market mechanisms by trial and error. The chapter highlights ten papers that exemplify recent progress in agent-based evolutionary analysis and design of markets in silico, using electricity markets and on-line double auctions as illustrations. A monopoly sealed-bid auction is examined in the tenth paper, and a new auction mechanism is evolved and analyzed. The chapter concludes that, as modeling the learning and behavior of traders improves, and as the software and hardware available for modeling and analysis improves, the techniques will provide ever greater insights into improving the designs of existing markets, and facilitating the design of new markets.

CHAPTER 28. AUTOMATED MARKETS AND TRADING AGENTS

Co-Contributors:

Jeffrey K. MacKie-Mason, Corresponding Author (Arthur W. Burks Professor of Information and Computer Science, School of Information, and Professor of Economics and Public Policy, Department of Economics and School of Public Policy Studies, University of Michigan, Ann Arbor, Michigan, jmm AT umich.edu): Computational market mechanisms and their applications to various distributed environments; Dynamic agent learning in information economies; Economically-intelligent artificial agents.

Michael Wellman (Professor of Electrical Engineering and Computer Science and Director of the Artificial Intelligence Laboratory, University of Michigan, Ann Arbor, MI, wellman AT umich.edu): Computational market mechanisms for distributed decision making and electronic commerce; Configurable auction technology.

Abstract:

Computer automation has the potential, just starting to be realized, of transforming the design and operation of markets, and the behaviors of agents trading in them. We discuss the possibilities for automating markets, presenting a broad conceptual framework covering resource allocation as well as enabling marketplace services such as search and transaction execution. One of the most intriguing opportunities is provided by markets implementing computationally sophisticated negotiation mechanisms, for example combinatorial auctions. An important theme that emerges from the literature is the centrality of design decisions about matching the domain of goods over which a mechanism operates to the domain over which agents have preferences. When the match is imperfect (as is almost inevitable), the market game induced by the mechanism is analytically intractable, and the literature provides an incomplete characterization of rational bidding policies. A review of the literature suggests that much of our existing knowledge comes from computational simulations, including controlled studies of abstract market designs (e.g., simultaneous ascending auctions), and research tournaments comparing agent strategies in a variety of market scenarios. An empirical game-theoretic methodology combines the advantages of simulation, agent-based modeling, and statistical and game-theoretic analysis.

CHAPTER 29. COMPUTATIONAL METHODS AND MODELS OF POLITICS

Co-Contributors:

Kenneth Kollman (Professor of Political Science, University of Michigan, Ann Arbor, MI, kkollman AT umich.edu): Computational political economy; Political parties and electoral landscapes; Interest groups, ideological bias, and Congressional committees; Development of national political parties; Effects of multi-layered electoral competition in federal political systems.

Scott E. Page (Professor of Complex Systems, Political Science, and Economics, University of Michigan, Ann Arbor, MI, spage AT umich.edu : Problem solving by heterogeneous agents; On the emergence of cities; Diversity and optimality; Political institutions and sorting in a Tiebout model.

Abstract:

In this chapter, we assess recent contributions of computational models to the study of politics. We focus primarily on agent-based models developed by economists and political scientists. These models address collective action problems, questions related to institutional design and performance, issues in international relations, and electoral competition. In our view, complex systems and computational techniques will have a large and growing impact on research on politics in the near future. This optimism follows from the observation that the concepts used in computational methodology in general and agent-based models in particular resonate deeply within political science because of the domains of study in the discipline and because early findings from agent-based models align with widely known empirical regularities in the political world. In the process of making our arguments, we survey a portion of the growing literature within political science.

CHAPTER 30. GOVERNING SOCIAL-ECOLOGICAL SYSTEMS

Co-Contributors:

Marco A. Janssen, Corresponding Author (Assistant Professor, School of Human Evolution and Social Change, Arizona State University, Tempe, AZ, Marco.Janssen AT asu.edu): The consumat approach (multi-agent modeling of consumer behavior); Complex adaptive systems; Modeling human dimensions of global environmental change; Self-organization of institutions; Interactive models for science-policy dialogue; Multi-agent modeling and evolutionary computation; The collapse of ancient societies.

Elinor Ostrom (Arthur F. Bentley Professor of Government, Co-Director of the Workshop in Political Theory and Policy Analysis, and Co-Director of the Center for the Study of Institutions, Population,and Environmental Change, Indiana University, Bloomington, Indiana, ostrom AT indiana.edu): Common pool resource usage; Collective decision-making.

Abstract:

Social-ecological systems are complex adaptive systems where social and biophysical agents are interacting at multiple temporal and spatial scales. The main challenge for the study of governance of social-ecological systems is improving our understanding of the conditions under which cooperative solutions are sustained, how social actors can make robust decisions in the face of uncertainty and how the topology of interactions between social and biophysical actors affect governance. We review the contributions of agent-based modeling to these challenges for theoretical studies, studies which combines models with laboratory experiments and applications of practical case studies.

CHAPTER 31. COMPUTATIONAL LABORATORIES FOR SPATIAL AGENT-BASED MODELS

Contributor:

Catherine Dibble (Assistant Professor of Geography, University of Maryland, College Park, MD, cdibble AT geog.umd.edu): Agent-based simulation; Computational laboratories in economic geography; Formation and effects of socio-economic networks in spatial landscapes; Small-world networks.

Abstract:

An agent-based model is a virtual world comprising distributed heterogeneous agents who interact over time. In a spatial agent-based model the agents are situated in a spatial environment and are typically assumed to be able to move in various ways across this environment. Some kinds of social or organizational systems may also be modeled as spatial environments, where agents move from one group or department to another and where communications or mobility among groups may be structured according to implicit or explicit channels or transactions costs. This chapter focuses on the potential usefulness of computational laboratories for spatial agent-based modeling. Speaking broadly, a computational laboratory is any computational framework permitting the exploration of the behaviors of complex systems through systematic and replicable simulation experiments. A narrower definition, used here, refers more specifically to specialized software tools to support a wide range of tasks associated with agent-based modeling. These tasks include model development, model evaluation through controlled experimentation, and both the descriptive and normative analysis of model outcomes. This chapter examines how computational laboratory tools and activities facilitate the systematic exploration of spatial agent-based models embodying complex social processes critical for social welfare. Examples include the spatial and temporal coordination of human activities, the diffusion of new ideas or of infectious diseases, and the emergence and ecological dynamics of innovative ideas or of deadly new diseases.

PART 2: Perspectives on the ACE Methodology

CHAPTER 32. OUT-OF-EQUILIBRIUM ECONOMICS AND AGENT-BASED MODELING

Contributor:

W. Brian Arthur (External Professor, Santa Fe Institute, New Mexico): Economic theory under increasing returns; Cognition and complexity in the economy; Artificial financial markets; Technology in the economy.

CHAPTER 33. AGENT-BASED MODELING AS A BRIDGE BETWEEN DISCIPLINES

Contributor:

Robert Axelrod (Arthur W. Bromage Distinguished University Professor of Political Science and Public Policy, School of Public Policy Studies, University of Michigan, Ann Arbor, Michigan, axe AT umich.edu): Complexity of cooperation; Evolution of cooperation.

CHAPTER 34. REMARKS ON THE FOUNDATIONS OF AGENT-BASED GENERATIVE SOCIAL SCIENCE

Contributor:

Joshua M. Epstein (Senior Fellow, Economic Studies, The Brookings Institution, Washington, D.C., and External Faculty Member, Santa Fe Institute, New Mexico, jepstein AT brookings.edu): Agent-based computational modeling, with applications to economics, conflict, epidemiology, and other fields.

CHAPTER 35. COORDINATION ISSUES IN LONG-RUN GROWTH

Contributor:

Peter Howitt (Lyn Crost Professor of Social Sciences and Professor of Economics, Brown University, Providence, Rhode Island, Peter_Howitt AT brown.edu): The emergence of economic organization; Monetary exchange; Job creation and destruction; Endogenous growth.

CHAPTER 36. AGENT-BASED MACRO

Contributor:

Axel Leijonhufvud (Professor of Economics, Universita degli Studi di Trento, Italy, and Professor Emeritus, Department of Economics, UCLA, axel AT economia.unitn.it): Computable economics; Evolution of modern macroeconomics; High inflations; Alternative monetary regimes; Transformation of socialist systems.

CHAPTER 37. SOME FUN, THIRTY-FIVE YEARS AGO

Contributor:

Thomas C. Schelling (2005 Nobel Laureate and Distinguished University Professor of Economics, University of Maryland, ts57 AT umail.umd.edu): Micromotives and macrobehavior; Conflict and bargaining theory; Military strategy and arms control; Policy issues (energy and environment, foreign aid, international trade, racial segregation and integration, ...).

PART III: Guideline for Newcomers to Agent-Based Modeling

APPENDIX: A Guide for Newcomers to Agent-Based Modeling in the Social Sciences

Contributors:

Robert Axelrod and Leigh Tesfatsion

Web support materials (readings and demonstration software) for this handbook appendix are provided in a parallel on-line guide (html,44K).