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Learning to cooperate in the Iterated Prisoner’s Dilemma by means of social attachments

Journal of the Brazilian Computer Society volume 17, pages 163–174 (2011)Cite this article

Abstract

The Iterated Prisoner’s Dilemma (IPD) has been used as a paradigm for studying the emergence of cooperation among individual agents. Many computer experiments show that cooperation does arise under certain conditions. In particular, the spatial version of the IPD has been used and analyzed to understand the role of local interactions in the emergence and maintenance of cooperation. It is known that individual learning leads players to the Nash equilibrium of the game, which means that cooperation is not selected. Therefore, in this paper we propose that when players have social attachment, learning may lead to a certain rate of cooperation. We perform experiments where agents play the spatial IPD considering social relationships such as belonging to a hierarchy or to coalition. Results show that learners end up cooperating, especially when coalitions emerge.

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Author information

Affiliations

  1. PPGC/Instituto de Informática, Universidade Federal do Rio Grande do Sul, Caixa Postal 15064, CEP 91501-970, Porto Alegre, RS, Brazil

    Ana L. C. Bazzan

  2. Telematics Engineering Department, University of Vigo, 36310, Vigo, Spain

    Ana Peleteiro & Juan C. Burguillo

Authors
  1. Ana L. C. Bazzan
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  2. Ana Peleteiro
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  3. Juan C. Burguillo
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Corresponding author

Correspondence to Ana L. C. Bazzan.

Additional information

A previous version of this paper appeared at BWSS 2010, the Brazilian Symposium on Social Simulation.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Bazzan, A.L.C., Peleteiro, A. & Burguillo, J.C. Learning to cooperate in the Iterated Prisoner’s Dilemma by means of social attachments. J Braz Comput Soc 17, 163–174 (2011). https://doi.org/10.1007/s13173-011-0038-2

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Keywords

  • Game theory
  • Iterative Prisoner’s Dilemma
  • Reinforcement learning
  • Agent-based simulation