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My Way or the Highway: a More Naturalistic Model of Altruism Tested in an Iterative Prisoners' Dilemma

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There are three prominent solutions to the Darwinian problem of altruism, kin selection, reciprocal altruism, and trait group selection. Only one, reciprocal altruism, most commonly implemented in game theory as a TIT FOR TAT strategy, is not based on the principle of conditional association. On the contrary, TIT FOR TAT implements conditional altruism in the context of unconditionally determined associates. Simulations based on Axelrod's famous tournament have led many to conclude that conditional altruism among unconditional partners lies at the core of much human and animal social behavior. But the results that have been used to support this conclusion are largely artifacts of the structure of the Axelrod tournament, which explicitly disallowed conditional association as a strategy. In this study, we modify the rules of the tournament to permit competition between conditional associates and conditional altruists. We provide evidence that when unconditional altruism is paired with conditional association, a strategy we called MOTH, it can out compete TIT FOR TAT under a wide range of conditions.

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  • David Joyce & John Kennison & Owen Densmore & Stephen Guerin & Shawn Barr & Eric Charles & Nicholas S. Thompson, 2006. "My Way or the Highway: a More Naturalistic Model of Altruism Tested in an Iterative Prisoners' Dilemma," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 9(2), pages 1-4.
  • Handle: RePEc:jas:jasssj:2005-50-2
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    1. Robert Axtell & Robert Axelrod & Joshua M. Epstein & Michael D. Cohen, 1995. "Aligning Simulation Models: A Case Study and Results," Working Papers 95-07-065, Santa Fe Institute.
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    2. İlker Yıldırım & Pınar Yolum, 2009. "Hybrid models for achieving and maintaining cooperative symbiotic groups," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 8(2), pages 243-258, December.
    3. Giangiacomo Bravo, 2007. "Imitation and Cooperation in Different Helping Games," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 11(1), pages 1-8.
    4. Sakura Arai & John Tooby & Leda Cosmides, 2022. "Motivations to reciprocate cooperation and punish defection are calibrated by estimates of how easily others can switch partners," PLOS ONE, Public Library of Science, vol. 17(4), pages 1-28, April.
    5. Kurokawa, Shun & Zheng, Xiudeng & Tao, Yi, 2019. "Cooperation evolves more when players keep the interaction with unknown players," Applied Mathematics and Computation, Elsevier, vol. 350(C), pages 209-216.
    6. Qu, Xinglong & Zhou, Changli & Cao, Zhigang & Yang, Xiaoguang, 2016. "Conditional dissociation as a punishment mechanism in the evolution of cooperation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 449(C), pages 215-223.
    7. Annie TUBADJI & Vassilis ANGELIS & Peter NIJKAMP, 2019. "Micro-Cultural Preferences and Macro-Percolation of New Ideas: A NetLogo Simulation," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 10(1), pages 168-185, March.
    8. Luis R. Izquierdo & Segismundo S. Izquierdo & José Manuel Galán & José Ignacio Santos, 2009. "Techniques to Understand Computer Simulations: Markov Chain Analysis," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(1), pages 1-6.

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