Agent-Based Computational Economics Research Area:
Experiments with Real and Computational Agents

Last Updated: 8 March 2008

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

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

Basic Issues

• What are the strengths and weaknesses of conducting agent-based computational experiments and human-subject experiments as an aid to understanding real-world economic processes?
• Should computational experiments be viewed as a potential substitute for human-subject experiments? A potential complement? Both?
• In what situations is it both feasible and useful to calibrate the behavior of computational agents to the behavior of humans?
• Can computational agents be used to help infer the decision rules used by humans in parallel situations?
• Do computational agents tend to "do better" than humans in some experimental contexts? Do humans have something to learn from computational agents?
• Should human participants in real-world markets delegate decision-making to computational agents (shop-bots, Web-bots, crawlers, etc.)?

Surveys

• John Duffy, "Agent-Based Models and Human-Subject Experiments", in Leigh Tesfatsion and Kenneth L. Judd (editors), Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics, Handbooks in Economics Series, North-Holland/Elsevier, Amsterdam, Spring 2006.
  Abstract: 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.

  Leonidas Spiliopoulos, "Human versus Computer Algorithms in Repeated Mixed Strategy Games" (pdf,1.8M), Munich Personal RePEc Archive (MPRA) Paper No. 6672, January 2008.

  Absract: "This paper is concerned with the modeling of strategic change in humans’ behavior when facing different types of opponents. In order to implement this efficiently a mixed experimental setup was used where subjects played a game with a unique mixed strategy Nash equilibrium for 100 rounds against 3 preprogrammed computer algorithms (CAs) designed to exploit different modes of play. In this context, substituting human opponents with computer algorithms designed to exploit commonly occurring human behavior increases the experimental control of the researcher allowing for more powerful statistical tests. The results indicate that subjects significantly change their behavior conditional on the type of CA opponent, exhibiting within-subjects heterogeneity, but that there exists comparatively little between-subjects heterogeneity since players seemed to follow very similar strategies against each algorithm. Simple heuristics, such as win-stay/lose-shift, were found to model subjects and make out of sample predictions as well as, if not better than, more complicated models such as individually estimated EWA learning models which suffered from overfitting. Subjects modified their strategies in the direction of better response as calculated from CA simulations of various learning models, albeit not perfectly. Examples include the observation that subjects randomized more effectively as the pattern recognition depth of the CAs increased, and the drastic reduction in the use of the win-stay/lose-shift heuristic when facing a CA designed to exploit this behavior."

Other Readings

• James Andreoni and John H. Miller, "Auctions with Artificial Adaptive Agents," Games and Economic Behavior 58 (1995), 211-221.

• Jasmina Arifovic, "The Behavior of the Exchange Rate in the Genetic Algorithm and Experimental Economics," Journal of Political Economy 104(3), 1993, 510-541.

• W. Brian Arthur, "Designing Economic Agents that Act Like Human Agents: A Behavioral Approach to Bounded Rationality," American Economic Review Papers and Proceedings 81(2), 1991, 353-359.

• P. J. Brewer, M. Huang, B. Nelson, and C. R. Plott, "On the Behavioral Foundations of the Law of Supply and Demand: Human Convergence and Robot Randomness," Experimental Economics 5(3), 2002, 179-208.

• Nicholas T. Chan, Blake LeBaron, Andrew W. Lo, and Tomaso Poggio, "Agent-Based Models of Financial Markets: A Comparison with Experimental Markets," Working Paper, September 5, 1999.

• John Duffy, "Agent-Based Models and Human-Subject Experiments", in Leigh Tesfatsion and Kenneth L. Judd (editors), Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics, Handbooks in Economics Series, North-Holland/Elsevier, Amsterdam, Spring 2006.
  Abstract: 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.

  John Duffy, "Learning to Speculate: Experiments with Artificial and Real Agents," Journal of Economic Dynamics and Control 25(3/4), March 2001, pages 295-319.

  Abstract: This paper employs parallel experiments with real and computational agents to explore issues originally raised by Kiyotaki and Wright in their well-known search model of money (JPE, 1989). The primary issue of interest is how individuals might come to accept or learn to adopt a convention in which the particular commodity functioning as "money" is dominated in rate of return by other assets, in the sense that it has a higher storage cost. The key offsetting factor is anticipations ("speculation") concerning the ease with which the "money" good can be turned over in trade for other goods that agents have a higher desire to consume. The author shows how each type of experiment can contribute to the experimental design and interpretation of results for the other.

  D. K. Gode and S. Sunder, "Allocative Efficiency of Markets with Zero Intelligence Traders: Market as a partial substitute for individual rationality", Journal of Political Economy, Vol. 101, Number 1, 1993, pp. 119-137. Published article available at JSTOR.

  Abstract: Gode and Sunder report on continuous double auction experiments with budget-constrained computational traders. They find that market efficiency levels are close to 100 percent even when buyers submit purely random bids and sellers submit purely random offers. The authors conclude that the high market efficiency typically observed in continuous double auction experiments with human subjects is due to the structure of the auction and not to learning. Their seminal work has highlighted an important issue now being actively pursued by many other researchers: what are the relative roles of learning and institutional arrangements in the determination of economic, social, and political outcomes?

  Wander Jager and Marco A. Janssen, "Using Artificial Agents to Understand Laboratory Experiments of Common-Pool Resources with Real Agents," Chapter 6 in Janssen, M. A. (ed.), Complexity and Ecosystem Management: The Theory and Practice of Multi-Agent Systems, Edward Elgar Publishers, Cheltenham UK/Northampton, MA, 2003.

  John H. Kagel and Alvin E. Roth (eds.), Handbook of Experimental Economics, Princeton University Press, Princeton, NJ, 1995.

  Abstract: This book presents a comprehensive critical survey of the results and methods of human-subject laboratory experiments in economics. The first chapter provides an introduction to human-subject experimental economics as a whole, while the remaining chapters provide surveys by leading practitioners in areas of economics that have a concentration of human-subject experimental research: public goods; coordination problems; bargaining; industrial organization; asset markets; auctions; and individual decision making. For more information about this book and related topics, visit Al Roth's website on Game Theory and Experimental Economics.

  Sheri Markose, Jasmina Arifovic, and Shyam Sunder, "Advances in experimental and agent-based modelling: Asset markets, economic networks, computational mechanism design, and evolutionary game dynamics," Journal of Economic Dynamics and Control 31 (2007), pp. 1801-07.

  Claudia Pahl-Wostl and Eva ebenhoh, Heuristics to Characterise Human Behaviour in Agent-Based Models (pdf,7pp), Working Paper, Institute of Environmental Systems Research, University of Osnabruck, Germany, downloaded 1/29/05.

  Abstract: The authors pursue a pragmatic approach to the representation of human behavior in agent-based models, assuming that agents can be characterized by a set of attributes and their behavior can be described by a set of simple decision heuristics. These assumptions are tested and refined by using data from human-subject experiments describing the behaviors of players in simple resource allocation games.

  Mark Pingle and Leigh Tesfatsion, "Evolution of Worker-Employer Networks and Behaviors Under Alternative Non-Employment Benefits: An Agent-Based Computational Study", pp. 254-283 in Anna Nagurney (ed.), Innovations in Financial and Economic Networks, Edward Elgar Publishers, 2003 (pdf preprint,269K).

  Note: The results of this ACE labor market study are briefly compared against results from a parallel human-subject experiment.

  Matteo G. Richiardi and Roberto Leombruni, "Exploring a New ExpAce: The Complementarities between Experimental Economics and Agent-Based Computational Economics, Journal of Complexity, 2006. Available from the Social Science Research Network.

  Abstact: This paper addresses whether experimental economics and agent-based computational economics can complement and integrate with each other. The authors argue that the answer is yes, that there are many benefits to be gained by both communities of researchers from increased interactions.

  Juliette Rouchier, "Re-Implementation of a Multi-Agent Model Aimed at Sustaining Experimental Economic Research: The Case of Simulations with Emerging Speculation", Journal of Artificial Societies and Social Simulation, Vol. 6(4), 2003, (html).

  Note: This paper explores replication issues for the article by John Duffy (JEDC,2001) cited above.

  Special issue on Developments in Experimental and Agent-Based Computational Economics (ACE), Journal of Economic Interaction and Coordination,Volume 1, Number 2, December 2006 (guest-edited by Sheri Markose).

Software, Toolkits, and Demos

• Annotated list of pointers to ACE/CAS General Software and Toolkits

• Annotated list of pointers to ACE/CAS Computational Laboratories and Demonstration Software

• Trade Network Game Lab used to run ACE labor market experiments (Pingle/Tesfatsion).

Resource Sites and Groups

• ACE/CAS Research Sites and Groups

• The Institute for Empirical Research in Economics (University of Zurich, Switzerland), headed by Prof.Dr. Ernst Fehr, provides links to a variety of resources related to microeconomics and experimental economics on its home page. Institute researchers combine insights from modern economic theory with results from social psychology and sociology to understand important economic phenomena. Topics stressed include the functioning of labor markets, the organization of the modern corporation, the private and public provision of public goods, and intertemporal choice problems.

• Al Roth (Economics Department, Harvard University) maintains an extensive informative website on Game Theory and Experimental Economics.

Individual Researchers

• Individual Researchers using ACE/CAS Tools

• Jasmina Arifovic (Economics Department, Simon Fraser U, Canada): Learning and Adaptation, Experimental Economics, Macroeconomics, Monetary Economics, Evolutionary Game Theory, Computational Mechanism Design

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

• Herbert Gintis (Department of Economics, University of Massachusets, Amherst): Agent-based evolutionary game dynamics; Evolution of strong reciprocity; Moral economy of communities; Evolution of social norms; Experimental games.

• Blake LeBaron (Graduate School of International Economics and Finance, Brandeis University, Waltham, Massachusetts): 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; Parallel experiments with real and computational agents.

• M. Utku Ünver (Department of Economics, University of Pittsburg, Pennsylvania): Social learning in market games using genetic algorithms; Experimental economics; Game theory; Two-sided and one-sided matching; Auctions; Parallel experiments with real and computational agents.

Copyright © 2008 Leigh Tesfatsion. All Rights Reserved.