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arXiv 1101.4378physics.soc-phcs.SInlin.AO
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不完全学习中的合作与背叛循环

Cycles of cooperation and defection in imperfect learning

Tobias Galla

摘要:

当人们玩重复游戏时,他们通常会根据过去的观察来预测对手的行动,然后决定下一步采取什么行动。行为经济学研究的是在这些适应性学习过程中做出战略决策的机制。我们在这里研究一个学习迭代囚徒困境博弈的模型。玩家可以在三种策略之间进行选择,总是背叛(ALLD),总是合作(ALLC)和针锋相对(TFT)。在这种情况下,唯一严格的纳什均衡是ALLD。当玩家学会玩这个游戏时,不能保证收敛到均衡,例如,如果玩家在遥远的过去贴现观察,我们会发现合作行为。当代理使用小样本的观察到的动作,以估计他们的对手的策略的学习过程是随机的,和合作和背叛之间的持续振荡可能会出现。这些周期类似于随机进化过程中发现的周期,但维持振荡的噪声的来源是不同的,在于对手策略的不完美采样。基于一个系统的扩展技术,我们能够预测这些学习周期的属性,提供了一个分析工具,更一般的随机适应过程的结果可以被表征。

英文摘要:

When people play a repeated game they usually try to anticipate their opponents' moves based on past observations, and then decide what action to take next. Behavioural economics studies the mechanisms by which strategic decisions are taken in these adaptive learning processes. We here investigate a model of learning the iterated prisoner's dilemma game. Players have the choice between three strategies, always defect (ALLD), always cooperate (ALLC) and tit-for-tat (TFT). The only strict Nash equilibrium in this situation is ALLD. When players learn to play this game convergence to the equilibrium is not guaranteed, for example we find cooperative behaviour if players discount observations in the distant past. When agents use small samples of observed moves to estimate their opponent's strategy the learning process is stochastic, and sustained oscillations between cooperation and defection can emerge. These cycles are similar to those found in stochastic evolutionary processes, but the origin of the noise sustaining the oscillations is different and lies in the imperfect sampling of the opponent's strategy. Based on a systematic expansion technique, we are able to predict the properties of these learning cycles, providing an analytical tool with which the outcome of more general stochastic adaptation processes can be characterised.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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