Digital Library of the European Council for Modelling and Simulation

Title:	Co-Evolutionary Learning Of Contextual Asymmetric Actors
Authors:	Siang Yew Chong, Christopher Hill, Xin Yao
Published in:	(2009).ECMS 2009 Proceedings edited by J. Otamendi, A. Bargiela, J. L. Montes, L. M. Doncel Pedrera. European Council for Modeling and Simulation. doi:10.7148/2009 ISBN: 978-0-9553018-8-9 23^rd European Conference on Modelling and Simulation, Madrid, June 9-12, 2009
Citation format:	Chong, S. Y., Hill, C., & Yao, X. (2009). Co-Evolutionary Learning Of Contextual Asymmetric Actors. ECMS 2009 Proceedings edited by J. Otamendi, A. Bargiela, J. L. Montes, L. M. Doncel Pedrera (pp. 827-833). European Council for Modeling and Simulation. doi:10.7148/2009-0827-0833
DOI:	http://dx.doi.org/10.7148/2009-0827-0833
Abstract:	Co-evolutionary learning of the iterated prisoner’s dilemma (IPD) has been used to model and simu- late interactions, which may not be realistic due to assumptions of a fixed and symmetric payoff matrix for all players. Recently, we proposed to extend the co-evolutionary learning framework for any two-player repeated encounter game to model more realistic be- havioral interactions. One issue we studied is to endow players with individual and self-adaptive payoff matrix to model individual variations in their utility expecta- tions of rewards for making certain decisions. Here, we study a different issue involving contextual asymmetric actors. The differences in the utility expectations (payoff matrix) are due to contextual circumstances (external) such as political roles rather than variations in individual preferences (internal). We emphasize the model of inter- actions among contextually asymmetric actors through a multi-population structure in the co-evolutionary learning framework where different populations rep- resenting different actor roles interact. We study how different actor roles modelled by fixed and asymmetric payoff matrices can have an impact to the outcome of co-evolutionary learning. As an illustration, we apply co-evolutionary learning of two contextually asymmetric actors from the spanish democratic transition.
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