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Categorization and Cooperation across Games

机译:跨游戏的分类与合作

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We study a model where agents face a continuum of two-player games and categorize them into a finite number of situations to make sense of their complex environment. Agents need not share the same categorization. Each agent can cooperate or defect, conditional on the perceived category. The games are fully ordered by the strength of the temptation to defect and break joint cooperation. In equilibrium agents share the same categorization, but achieve less cooperation than if they could perfectly discriminate games. All the equilibria are evolutionarily stable, but stochastic stability selects against cooperation. We model agents’ learning when they imitate successful players over similar games, but lack any information about the opponents’ categorizations. We show that imitation conditional on reaching an intermediate aspiration level leads to a shared categorization that achieves higher cooperation than under perfect discrimination.
机译:我们研究了一个模型,其中特工面对连续的两人游戏,并将他们分为有限的几种情况,以了解其复杂的环境。代理不必共享相同的分类。每个代理可以根据感知的类别进行合作或背叛。凭借缺陷和中断联合合作的诱惑力,这些比赛得到了充分的安排。在均衡状态下,特工共享相同的分类,但是与完全区分博弈的合作相比,合作较少。所有平衡都是进化稳定的,但是随机稳定性不利于合作。当代理商模仿相似游戏中的成功玩家时,我们会模拟他们的学习,但缺乏有关对手分类的任何信息。我们表明,以达到中等期望水平为条件的模仿会导致共享类别,与完全歧视相比,该类别可以获得更高的合作。

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