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Co-evolution of Optimal Agents for the Alternating Offers Bargaining Game

  • Arjun Chandra
  • Pietro Simone Oliveto
  • Xin Yao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6024)

Abstract

Bargaining, as an instance of sequential games, is a widely studied problem in game theory, experimental and computational economics. We consider the problem of evolving computational agents with optimal (Subgame Perfect Equilibrium) strategies for the Alternating Offers Bargaining Game. Previous work co-evolving agents for this problem has argued that it is not possible to achieve optimal agents at the end of the co-evolutionary process due to the myopic properties of the evolutionary agents. Emphasising the notion of a co-evolutionary solution concept, we show that this conclusion is mis-leading and present a co-evolutionary algorithm that evolves optimal strategies for the bargaining game with one round. We conclude by explaining why, using previous evaluation procedures and strategy representations, the algorithm is not able to converge to optimal strategies for games with more rounds.

Keywords

Solution Concept Subgame Perfect Equilibrium Bargaining Game Agent Strategy Equilibrium Selection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Beyer, H.: The theory of evolution strategies. Springer, New York (2001)Google Scholar
  2. 2.
    van Bragt, D.D.B., Gerding, E.H., La Poutré, J.A.: Equilibrium selection in alternating-offers bargaining models - the evolutionary computing approach. The Electronic Journal of Evolutionary Modeling and Economic Dynamics (2002)Google Scholar
  3. 3.
    Chong, S.Y., Tino, P., Yao, X.: Measuring generalization performance in coevolutionary learning. IEEE Transactions on Evolutionary Computation 12(4), 479–505 (2008)CrossRefGoogle Scholar
  4. 4.
    Darwen, P.J.: Co-evolutionary learning by automatic modularisation with speciation. Ph.D. thesis, University of New South Wales (1996)Google Scholar
  5. 5.
    Ficici, S.G.: Solution concepts in coevolutionary algorithms. Ph.D. thesis, Brandeis University (2004)Google Scholar
  6. 6.
    Gerding, E., van Bragt, D.D.B., La Poutré, J.A.: Multi-issue negotiation processes by evolutionary simulation: validation and social extensions. Tech. Rep. SEN-R0024, CWI, Amsterdam, The Netherlands (2000)Google Scholar
  7. 7.
    Gerding, E., van Bragt, D.D.B., La Poutré, J.A.: Multi-issue negotiation processes by evolutionary simulation, validation and social extensions. Computational Economics 22(1), 39–63 (2003)zbMATHCrossRefGoogle Scholar
  8. 8.
    Jin, N.: Constraint-based co-evolutionary genetic programming for bargaining problems. Ph.D. thesis, University of Essex (2007)Google Scholar
  9. 9.
    Roth, A.: The economist as engineer: game theory, experimentation, and computation as tools for design economics. Econometrica 70(4), 1341–1378 (2002)zbMATHCrossRefGoogle Scholar
  10. 10.
    Rubinstein, A.: Perfect equilibrium in a bargaining model. Econometrica 50(1), 97–109 (1982)zbMATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Selten, R.: Re-examination of the perfectness concept for finite points in extensive games. International Journal of Game Theory 4, 25–55 (1975)zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Arjun Chandra
    • 1
  • Pietro Simone Oliveto
    • 1
  • Xin Yao
    • 1
  1. 1.The Centre of Excellence for Research in Computational Intelligence and Applications (CERCIA), School of Computer ScienceUniversity of BirminghamUK

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