Abstract
In this chapter, to investigate the long-run behavior of players in the generalized matching pennies game, we employ an approach based on adaptive behavioral models. To do so, we develop an agent-based simulation system in which artificial adaptive agents have mechanisms of decision making and learning based on neural networks and genetic algorithms. We analyze the strategy choices of agents and the obtained payoffs in the simulations, and compare the predictions of Nash equilibria, the experimental data, and the results of the simulations with artificial adaptive agents. Moreover, we examine similarities between the behaviors of the human subjects in the experiments and those of the artificial adaptive agents in the simulations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Axelrod, R.: The evolution of strategies in the iterated prisoner’s dilemma. In: Davis, L. (ed.) Genetic Algorithms and Simulated Annealing, pp. 32–41. Pitman, London (1987)
Axelrod, R.: Advancing the art of simulation in the social sciences. In: Conte, R., Hegselmann, R., Terna, P. (eds.) Simulating Social Phenomena, pp. 21–40. Springer, Berlin (1997)
Andreoni, J., Miller, J.H.: Auctions with artificial adaptive agents. Games Econ. Behav. 10, 39–64 (1995)
Banerje, B., Sen, S.: Selecting parters. In: Parsons, S., Gmytrasiewicz, P., Wooldridge, M. (eds.) Game Theory and Decision Theory in Agent-Based Systems, pp. 27–42. Kluwer Academic Publishers, Boston (2002)
Chellapilla, K., Fogel, D.B.: Evolving neural networks to play checkers without expert knowledge. IEEE Trans. Neural Netw. 10, 1382–1391 (1999)
Conte, R., Hegselmann, R., Terna, P. (eds.): Simulating Social Phenomena. Springer, Berlin (1997)
Dorsey, R.E., Johnson, J.D., Van Boening, M.V.: The use of artificial neural networks for estimation of decision surfaces in first price sealed bid auctions. In: Cooper, W.W., Whinston, A.B. (eds.) New Directions in Computational Economics, pp. 19–40. Kluwer (1994)
Downing, T.E., Moss, S., Pahl-Wostl, C.: Understanding climate policy using participatory agent-based social simulation. In: Moss, S., Davidson, P. (eds.) Multi-Agent Based Simulation, pp. 198–213. Springer, Berlin (2001)
Epstein, J.M., Axtell, R.: Glowing Artificial Societies. Brookings Institution Press, Washington, D.C. (1996)
Erev, I., Rapoport, A.: Coordination, “magic,” and reinforcement learning in a market entry game. Games Econ. Behav. 23, 146–175 (1998)
Erev, I., Roth, A.E.: Predicting how people play games: reinforcement learning in experimental games with unique, mixed strategy equilibria. Am. Econ. Rev. 88, 848–881 (1998)
Duffy, J., Feltovich, N.: Does observation of others affect learning in strategic environments?An experimental study. Int. J. Game Theory 28, 131–152 (1999)
Goeree, J.K., Holt, C.A.: Ten little treasures of game theory and ten intuitive contradictions. Am. Econ. Rev. 91, 1402–1422 (2001)
Goeree, J.K., Holt, C.A., Palfrey, T.R.: Risk averse behavior in generalized matching pennies games. Games Econ. Behav. 45, 97–113 (2003)
Holland, J.H., Miller, J.H.: Adaptive intelligent agents in economic theory. Am. Econ. Rev. 81, 365–370 (1991)
Kahneman, D., Tversky, A.: Prospect theory; an analysis of decision under risk. Econometrica 47, 263–291 (1979)
Karmarkar, U.S.: Subjectively weighted utility; a descriptive extension of the expected utility model. Organ. Behav. Hum. Perform. 21, 61–72 (1978)
Leshno, M., Moller, D., Ein-Dor, P.: Neural nets in a group decision process. Int. J. Game Theory 31, 447–467 (2002)
McKelvey, R.D., Palfrey, T.R.: Quantal response equilibria for normal form games. Games Econ. Behav. 10, 6–38 (1995)
McKelvey, R.D., Palfrey, T.R., Weber, R.A.: The effects of payoff magnitude and heterogeneity on behavior in 2×2 games with unique mixed strategy equilibria. J. Econ. Behav. Organ. 42, 523–548 (2000)
Moss, S., Davidson, P. (eds.): Multi-Agent Based Simulation. Springer, Berlin (2001)
Nishizaki, I.: A general framework of agent-based simulation for analyzing behavior of players in games. J. Telecomm. Inf. Technol. 4, 28–35 (2007)
Nishizaki, I., Katagiri, H., Oyama, T.: Simulation analysis using multi-agent systems for social norms. Comput. Econ. 34, 37–65 (2009)
Nishizaki, I., Ueda, Y., Sasaki, T.: Lotteries as a means of financing for preservation of the global commons and agent-based simulation analysis. Appl. Artif. Intell. 19, 721–741 (2005)
Niv, Y., Joel, D., Meilijson, I., Ruppin, E.: Evolution of reinforcement learning in foraging bees: a simple explanation for risk averse behavior. Neurocomputing 44-46, 951–956 (2002)
Ochs, J.: Games with unique, mixed strategy equilibria: an experimental study. Games Econ. Behav. 10, 202–217 (1995)
Parsons, S., Gmytrasiewicz, P., Wooldridge, M. (eds.): Game Theory and Decision Theory in Agent-Based Systems. Kluwer (2002)
Rapoport, A., Seale, D.A., Winter, E.: Coordination and learning behavior in large groups with asymmetric players. Games Econ. Behav. 39, 111–136 (2002)
Roth, A.E., Erev, I.: Learning in extensive form games: experimental data and simple dynamic models in the intermediate term. Games Econ. Behav. 8, 163–212 (1995)
Sichman, J.S., Bousquet, F., Davidsson, P. (eds.): Multi-Agent-Based Simulation II. Springer (2003)
Sundali, J.A., Rapoport, A., Seale, D.A.: Coordination in market entry games with symmetric players. Organ. Behav. Hum. Decis. Process. 64, 203–218 (1995)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Nishizaki, I., Nakakura, T., Hayashida, T. (2013). Simulation Analysis Using Multi-Agent Systems for Generalized Matching Pennies Games. In: Hakansson, A., Hartung, R. (eds) Agent and Multi-Agent Systems in Distributed Systems - Digital Economy and E-Commerce. Studies in Computational Intelligence, vol 462. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35208-9_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-35208-9_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35207-2
Online ISBN: 978-3-642-35208-9
eBook Packages: EngineeringEngineering (R0)