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.
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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
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DOI: https://doi.org/10.1007/978-3-642-35208-9_10
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