Abstract
The Iterated Prisoners Dilemma (IPD) has received much attention because of its ability to demonstrate altruistic behavior. However, most studies focus on the synchronous case, where players make their decisions simultaneously. As this is implausible in most biological contexts, a more generalized approach is required to study the emergence of altruistic behavior in an evolutionary context. Here, we take previous results and present a generalized Markov model for asynchronous IPD, where both, one, or neither player can make a decision at a given time step. We show that the type of asynchronous timing introduced into the model influences the strategy that dominates. The framework presented here is a more biologically plausible scenario through which to investigate altruistic behavior.
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Alexrod, R.: The evolution of cooperation. Basic Books, New York (1984)
Axelrod, R., Hamilton, W.D.: The Evolution of Cooperation. Science 211, 1390–1396 (1981)
Cornforth, D., Green, D.G., Newth, D.: Ordered asynchronous processes in multi-agent systems. Physica D 204, 70–82 (2005)
Frean, M.R.: The prisoner’s dilemma without synchrony. Proceedings of the Royal Society, Series B 257, 75–79 (1994)
May, R.M.: More evolution of cooperation. Nature 327, 15–17 (1987)
Maynard-Smith, J.: Evolution and the theory of games. Cambridge University Press, Cambridge (1982)
Nowak, M., Sigmund, K.: A strategy of win-stay, lose-shift that outperforms tit-for-tat in the Prisoner’s Dilemma game. Nature 364, 56–58 (1993)
Nowak, M., Sigmund, K.: The alternating Prisoner’s Dilemma. Journal of Theoretical Biology 168, 219–226 (1994)
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© 2006 Springer-Verlag Berlin Heidelberg
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Cornforth, D., Newth, D. (2006). The Emergence of Cooperation in Asynchronous Iterated Prisoner’s Dilemma. In: Wang, TD., et al. Simulated Evolution and Learning. SEAL 2006. Lecture Notes in Computer Science, vol 4247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11903697_93
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DOI: https://doi.org/10.1007/11903697_93
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-47331-2
Online ISBN: 978-3-540-47332-9
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