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• W2883345345 abstract "In an iterated two-person game, for instance prisoner's dilemma or the snowdrift game, there exist strategies that force the payoffs of the opponents to be equal. These equalizer strategies form a subset of the more general zero-determinant strategies that unilaterally set the payoff of an opponent. A challenge in the attempts to understand the role of these strategies in the evolution of animal behavior is the lack of iterations in the fights for mating opportunities or territory control. We show that an arbitrary two-parameter strategy may possess a corresponding equalizer strategy which produces the same result: statistics of the fight outcomes in the contests with competitors are the same for each of these two strategies. Therefore, analyzing only the equalizer strategy space may be sufficient to predict animal behavior if nature, indeed, reduces (marginalizes) complex strategies to equalizer strategy space. The work's main finding is that there is a unique equalizer strategy that predicts fight outcomes without symmetric cooperation responses. The lack of symmetric cooperation responses is a common trait in conflict escalation contests that generally require a clear winner. In addition, this unique strategy does not assess information of the opponent's state. The method bypasses the standard analysis of evolutionary stability. The results fit well the observations of combat between male bowl and doily spiders and support an empirical assumption of the war of attrition model that the species use only information regarding their own state during conflict escalation." @default.
• W2883345345 created "2018-08-03" @default.
• W2883345345 creator A5061448067 @default.
• W2883345345 creator A5072816148 @default.
• W2883345345 date "2018-07-26" @default.
• W2883345345 modified "2023-09-26" @default.
• W2883345345 title "Single equalizer strategy with no information transfer for conflict escalation" @default.
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• W2883345345 doi "https://doi.org/10.1103/physreve.98.012415" @default.
• W2883345345 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30110774" @default.
• W2883345345 hasPublicationYear "2018" @default.
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