A general framework of agent-based simulation for analyzing behavior of players in games

• Autorzy: Nishizaki I.
• Języki publikacji: EN

Abstrakty

EN

In this paper, we give a general framework of agent-based simulation for analyzing behavior of players in various types of games. In our simulation model, artificial adaptive agents have a mechanism of decision making and learning based on neural networks and genetic algorithms. The synaptic weights and thresholds characterizing the neural network of an artificial agent are revised in order that the artificial agent obtains larger payoffs through a genetic algorithm. The proposed framework is illustrated with two examples, and, by giving some simulation result, we demonstrate availability of the simulation analysis by the proposed framework of agent-based simulation, from which a wide variety of simulation settings can be easily implemented and detailed data and statistics are obtained.

Słowa kluczowe

EN

artificial adaptive agents; simulation; games; behavior of players

• Wydawca: Instytut Łączności - Państwowy Instytut Badawczy
• Czasopismo: Journal of Telecommunications and Information Technology
• Rocznik: 2007
• Tom: nr 4
• Strony: 28--35
• Opis fizyczny: Bibliogr. 23 poz., rys.

Twórcy

autor

Nishizaki I.

• Department of Artificial Complex Systems Engineering, Graduate School of Engineering, Hiroshima University, 1-4-1 Kagamayama, Higashi-Hiroshima, 739-8527, Japan,

Bibliografia

• [1] J. Andreoni and J. H. Miller, “Auctions with artificial adaptive agents”, Games Econom. Behav., vol. 10, pp. 39–64, 1995.
• [2] R. Axelrod, “Advancing the art of simulation in the social sciences”, in Simulating Social Phenomena, R. Conte, R. Hegselmann, and R. Terna, Eds. Berlin, Heidelberg: Springer-Verlag, 1997, pp. 21–40.
• [3] G. E. Bolton and R. Zwick, “Anonymity versus punishment in ultimatum bargaining”, Games Econom. Behav., vol. 10, pp. 95–121, 1995.
• [4] R. E. Dorsey, J. D. Johnson, and M. V. Van Boening, “The use of artificial neural networks for estimation of decision surfaces in first price sealed bid auctions”, in New Directions in Computational Economics, W. W. Cooper and A. B. Whinston, Eds. Dordrecht: Kluwer, 1994, pp. 19–40.
• [5] J. Duffy and N. Feltovich, “Does observation of others affect learning in strategic environments? An experimental study”, Int. J. Game Theory, vol. 28, pp. 131–152, 1999.
• [6] I. Erev and A. Rapoport, “Coordination, “magic”, and reinforcement learning in a market entry game”, Games Econom. Behav., vol. 23, pp. 146–175, 1998.
• [7] I. Erev and A. E. Roth, “Predicting how people play games: reinforcement learning in experimental games with unique, mixed strategy equilibria”, Amer. Econom. Rev., vol. 88, pp. 848–881, 1998.
• [8] D. Fundenberg and D. K. Levine, The Theory of Learning in Games. Cambridge: The MIT Press, 1998.
• [9] J. K. Goeree, C. A. Holt, and T. R. Palfrey, “Risk averse behavior in generalized matching pennies games”, Games Econom. Behav., vol. 45, pp. 97–113, 2003.
• [10] D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning. Reading: Addison Wesley, 1989.
• [11] M. H. Hassoun, Fundamentals of Artificial Neural Networks. Cambridge: The MIT Press, 1995.
• [12] J. H. Holland and J. H. Miller, “Adaptive intelligent agents in economic theory”, Amer. Econom. Rev., vol. 81, pp. 365–370, 1991.
• [13] M. Leshno, D. Moller, and P. Ein-Dor, “Neural nets in a group decision process”, Int. J. Game Theory, vol. 31, pp. 447–467, 2002.
• [14] I. Nishizaki, H. Katagiri, T. Hayashida, and N. Hara, “Agent-based simulation analysis for equilibrium selection and coordination failure in coordination games characterized by the minimum strategy”, Mimeo, 2007.
• [15] I. Nishizaki, T. Nakakura, and T. Hayashida, “Agent-based simulation for generalized matching pennies games”, in Proc. 3nd Int. Conf. Soft Comp. Intell. Syst. 7th Int. Symp. Adv. Intell. Syst. SCIS & ISIS 2006, Yokohama, Japan, 2006, pp. 1530–1535.
• [16] J. Ochs, “Games with unique, mixed strategy equilibria: an experimental study”, Games Econom. Behav., vol. 10, pp. 202–217, 1995.
• [17] A. Rapoport, D. A. Seale, and E.Winter. “Coordination and learning behavior in large groups with asymmetric players”, Games Econom. Behav., vol. 39, pp. 111–136, 2002.
• [18] A. E. Roth and I. Erev, “Learning in extensive form games: experimental data and simple dynamic models in the intermediate term”, Games Econom. Behav., vol. 8, pp. 163–212, 1995.
• [19] A. Roth, V. Prasnikar, M. Okuno-Fujiwara, and S. Zamir, “Bargaining and market behavior in Jerusalem, Ljubljana, Pittsburgh, and Tokyo: an experimental study”, Amer. Econom. Rev., vol. 81, pp. 1068–1095, 1991.
• [20] D. Schmidt, R. Shupp, J. M. Walker, and E. Ostrom, “Playing safe in coordination games: the role of risk dominance, payoff dominance, and history of play”, Games Econom. Behav., vol. 42, pp. 281–299, 2003.
• [21] J. A. Sundali, A. Rapoport, and D. A. Seale, “Coordination in market entry games with symmetric players”, Organiz. Behav. Hum. Decis. Process., vol. 64, pp. 203–218, 1995.
• [22] J. B. Van Huyck, R. C. Battalio and R. O. Beil, “Tacit coordination games, strategic uncertainty, and coordination failure”, Amer. Econom. Rev., vol. 80, pp. 234–248, 1990.
• [23] H. P. Young, Individual Strategy and Social Structure. Princeton: Princeton University Press, 1998.