• DocumentCode
    1244285
  • Title

    Behavioral diversity, choices and noise in the iterated prisoner´s dilemma

  • Author

    Chong, Siang Y. ; Yao, Xin

  • Author_Institution
    Sch. of Comput. Sci., Univ. of Birmingham, UK
  • Volume
    9
  • Issue
    6
  • fYear
    2005
  • Firstpage
    540
  • Lastpage
    551
  • Abstract
    Real-world dilemmas rarely involve just two choices and perfect interactions without mistakes. In the iterated prisoner´s dilemma (IPD) game, intermediate choices or mistakes (noise) have been introduced to extend its realism. This paper studies the IPD game with both noise and multiple levels of cooperation (intermediate choices) in a coevolutionary environment, where players can learn and adapt their strategies through an evolutionary algorithm. The impact of noise on the evolution of cooperation is first examined. It is shown that the coevolutionary models presented in this paper are robust against low noise (when mistakes occur with low probability). That is, low levels of noise have little impact on the evolution of cooperation. On the other hand, high noise (when mistakes occur with high probability) creates misunderstandings and discourages cooperation. However, the evolution of cooperation in the IPD with more choices in a coevolutionary learning setting also depends on behavioral diversity. This paper further investigates the issue of behavioral diversity in the coevolution of strategies for the IPD with more choices and noise. The evolution of cooperation is more difficult to achieve if a coevolutionary model with low behavioral diversity is used for IPD games with higher levels of noise. The coevolutionary model with high behavioral diversity in the population is more resistant to noise. It is shown that strategy representations can have a significant impact on the evolutionary outcomes because of different behavioral diversities that they generate. The results further show the importance of behavioral diversity in coevolutionary learning.
  • Keywords
    evolutionary computation; game theory; iterative methods; noise; behavioral diversity; coevolutionary learning; evolutionary algorithm; intermediate choices; iterated prisoner´s dilemma game; noise; Biological system modeling; Bridges; Computer science; Evolution (biology); Evolutionary computation; Helium; Immune system; Noise level; Noise robustness; Working environment noise; Behavioral diversity; coevolution; coevolutionary learning; evolutionary computation; iterated prisoner´s dilemma (IPD); representation;
  • fLanguage
    English
  • Journal_Title
    Evolutionary Computation, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1089-778X
  • Type

    jour

  • DOI
    10.1109/TEVC.2005.856200
  • Filename
    1545933