This is a dedicated webpage for the datasets from the research project “Small Artificial Human Agents for Virtual Economies“, implemented by Professor David Levine and Professor Yixin Chen at the Washington University of St. Louis and funded by the National Science Foundation.
The authors who have participated in the study have given their permission to publish their data online. We hope that through making this data available online we will aid researchers working in this field. This initiative is motivated by our belief that in order for economic research to be reliable and trusted, it should be possible to reproduce research findings – which is difficult or even impossible without the availability of the data and code. Making material openly available reduces to a minimum the barriers for doing reproducible research.
Small Artificial Human Agents for Virtual Economies
An important need for developing better economic policy prescriptions is an improved method of validating theories. Originally economics depended on field data from surveys and laboratory experiments. An alternative method of validating theories is through the use of artificial or virtual economies. If a virtual world is an adequate description of a real economy, then a good economic theory ought to be able to predict outcomes in that setting. An artificial environment offers enormous advantages over the field and laboratory: complete control – for example, over risk aversion and social preferences – and great speed in creating economies and validating theories. In economics the use of virtual economies can potentially enable us to deal with heterogeneity, with small frictions, and with expectations that are backward looking rather than determined in equilibrium. These are difficult or impractical to combine in existing calibrations or Monte Carlo simulations.
The goal of this project is to build artificial agents by developing computer programs that act like human beings in the laboratory. We focus on the simplest type of problem of interest to economists: the simple one-shot two-player simultaneous move games. There is a wide variety of existing published data on laboratory behavior that will be our primary testing ground for our computer programs. As we achieve greater success with this we want to see if our programs can adapt themselves to changes in the rules: for example, if payments are changed in a certain way, the computer programs will play differently: do people do the same? In some cases we may be able to answer these questions with data from existing studies; in others we will need to conduct our own experimental studies.
There is a great deal of existing research relevant to the current project. The state of the art in the study of virtual economies is agent-based modeling (Bonabeau (2002)). In addition, crucially related are both the theoretical literature on learning in games, and the empirical literature on behavior in the experimental laboratory. From the perspective of theory, the most relevant economic research is Foster and Vohra’s (1999) work on calibrated play and the related work on smooth fictitious play (Fudenberg and Levine (1998)) and regret algorithms (Hart and Mas-Colell (2000)). There is also a relevant literature in the computational game theory literature on regret optimization such as Nisan et al. (2007). Empirical work on human play in the laboratory has two basic threads: the research on first time play such as Nagel (1995) and the hierarchical models of Stahl and Wilson (1994), Costa-Gomes, Crawford, and Broseta (2001) and Camerer, Ho, and Chong (2004). Second are the learning models, most notably the reinforcement learning model of Erev and Roth (1998) and the EWA model (Ho, Camerer, and Chong (2007)). This latter model can be considered state of the art, including as it does both reinforcement and fictitious play type learning and initial play from a cognitive hierarchy.
Data on One-Shot Two-Player Simultaneous Move Games
| Andreoni, J. & Miller, J.H., 1993. Rational cooperation in the finitely repeated prisoner’s dilemma: Experimental evidence. The Economic Journal, pp.570–585. Link to publication Link to data |
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Bó, P.D., 2005. Cooperation under the shadow of the future: experimental evidence from infinitely repeated games. The American Economic Review, 95(5), pp.1591–1604. Link to publication Link to data | |
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Charness, G., Frechette, G.R. & Qin, C.-Z., 2007. Endogenous transfers in the Prisoner’s Dilemma game: An experimental test of cooperation and coordination. Games and Economic Behavior, 60(2), pp.287–306. Link to publication Link to data |
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Clark, K., Kay, S. & Sefton, M., 2001. When are Nash equilibria self-enforcing? An experimental analysis. International Journal of Game Theory, 29(4), pp.495–515. Link to publication Link to data |
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Clark, K. & Sefton, M., 2001. Repetition and signalling: experimental evidence from games with efficient equilibria. Economics Letters, 70(3), pp.357–362. Link to publication Link to data |
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Costa-Gomes, M. and Crawford, V. 2006. “Cognition and Behavior in Two-Person guessing Games: An Experimental Study.” The American Economic Review, 96(5), pp.1737-1768 Link to publication Link to data |
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Costa-Gomes, M., Crawford, V. and Bruno Broseta. 2001. “Cognition and Behavior in Normal-Form Games: An Experimental Study.” Econometrica, 69(5), pp.1193-1235 Link to publication Link to data |
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Crawford, V., Gneezy, U. and Yuval Rottenstreich. 2008. “The Power of Focal Points is Limited: Even Minute Payoff Asymmetry May Yield Large Coordination Failures.” The American Economic Review, 98(4), pp.1443-1458 Link to publication Link to data |
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Duffy, J. and Feltovich, N., 2002. Do Actions Speak Louder Than Words? An Experimental Comparison of Observation and Cheap Talk. Games and Economic Behavior, 39(1), pp.1–27. Link to publication Link to data |
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Duffy, J. & Ochs, J., 2009. Cooperative behavior and the frequency of social interaction. Games and Economic Behavior, 66(2), pp.785–812. Link to publication Link to data |
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Feltovich, N., Iwasaki, A. and Oda, S., 2012. Payoff levels, loss avoidance, and equilibrium selection in games with multiple equilibria: an experimental study. Economic Inquiry , 50(4), pp.932-952. Link to publication Link to data |
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Feltovich, N., & Oda, S., 2013. The effect of matching mechanism on learning in games played under limited information, Working paper Link to publication Link to data |
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Knez, M. & Camerer, C., 2000. Increasing cooperation in prisoner’s dilemmas by establishing a precedent of efficiency in coordination games. Organizational Behavior and Human Decision Processes, 82(2), pp.194–216. Link to publication Link to data |
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Ochs, J., 1995. Games with unique, mixed strategy equilibria: An experimental study. Games and Economic Behavior, 10(1), pp.202–217. Link to publication Link to data |
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Ong, D. & Chen, Z., 2012. Tiger Women: An All-Pay Auction Experiment on Gender Signaling of Desire to Win. Available at SSRN 1976782. Link to publication Link to data |
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Schmidt D., Shupp R., Walker J.M., and Ostrom E. 2003. “Playing Safe in Coordination Games: The Roles of Risk Dominance, Payoff Dominance, and History of Play.” Games and Economic Behaviour, 42(2), pp.281–299. Link to publication Link to data |
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Vlaev, I. & Chater, N., 2006. Game relativity: How context influences strategic decision making. Journal of Experimental Psychology: Learning, Memory, and Cognition; Journal of Experimental Psychology: Learning, Memory, and Cognition, 32(1), p.131. Link to publication Link to data |
References
Bonabeau, Eric. 2002. “Agent-Based Modeling: Methods and Techniques for Simulating Human Systems.” Proceedings of the National Academy of Sciences of the United States of America 99 (3):7280–7287.
Camerer, Colin F., Teck-Hua Ho, and Juin-Kuan Chong. 2004. “A Cognitive Hierarchy Model of Games.” The Quarterly Journal of Economics 119 (3):861–898.
Costa-Gomes, Miguel A., Vincent P. Crawford, and Bruno Broseta. 2001. “Cognition and Behavior in Normal-Form Games: An Experimental Study.” Econometrica 69 (5):1193– 1235.
Erev, Ido and Alvin E. Roth. 1998. “Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria.” American Economic Review 88 (4):848–881.
Foster, Dean P. and Rakesh Vohra. 1999. “Regret in the On-Line Decision Problem.” Games and Economic Behavior 29 (1-2):7–35.
Fudenberg, Drew and David K. Levine. 1998. The Theory of Learning In Games. Cam- bridge MA: MIT Press.
Hart, Sergiu and Andreu Mas-Colell. 2000. “A Simple Adaptive Procedure Leading to Correlated Equilibrium.” Econometrica 68:1127–1150.
Ho, Teck H., Cplin F. Camerer, and Juin-Kuan Chong. 2007. “Self-Tuning Experience Weighted Attraction Learning in Games.” Journal of Economic Theory 133:177–198.
Nagel, Rosemarie. 1995. “Unraveling in Guessing Games: An Experimental Study.” The American Economic Review 85 (5):1313–1326.
Nisan, Noam, Tim Roughgarden, Eva Tardos, and Vijay V. Vazirani. 2007. Algorithmic Game Theory. New York NY: Cambridge University Press.
Stahl, Dale O. and Paul W. Wilson. 1994. “Experimental Evidence on Players’ Models of Other Players.” Journal of Economic Behavior & Organization 25 (3):309–327.