Inferring underlying cooperative and competitive strategies from human behaviour in repeated games is important for accurately characterizing human behaviour and understanding how people reason strategically. Finite automata, a bounded model of computation, have been extensively used to compactly represent strategies for these games and are a standard tool in game theoretic analyses. However, inference over these strategies in repeated games is challenging since the number of possible strategies grows exponentially with the number of repetitions yet behavioural data are often sparse and noisy. As a result, previous approaches start by specifying a finite hypothesis space of automata that does not allow for flexibility. This limitation hinders the discovery of novel strategies that may be used by humans but are not anticipated a priori by current theory. Here we present a new probabilistic model for strategy inference in repeated games by exploiting non‐parametric Bayesian modelling. With simulated data, we show that the model is effective at inferring the true strategy rapidly and from limited data, which leads to accurate predictions of future behaviour. When applied to experimental data of human behaviour in a repeated prisoner's dilemma, we uncover strategies of varying complexity and diversity.

中文翻译：

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重复博弈中策略的非参数贝叶斯推理

从重复游戏中的人类行为中推断出潜在的合作和竞争策略，对于准确表征人类行为并理解人们如何进行战略性推理非常重要。有限自动机是一种有限的计算模型，已广泛用于紧凑地表示这些游戏的策略，并且是游戏理论分析中的标准工具。但是，在重复游戏中对这些策略的推论是具有挑战性的，因为可能策略的数量会随着重复次数的增加而呈指数增长，而行为数据通常很少且嘈杂。结果，先前的方法开始于指定自动机的有限假设空间，该空间不允许灵活性。这种局限性阻碍了可能被人类使用但未被当前理论先验地预测的新颖策略的发现。在这里，我们通过利用非参数贝叶斯模型为重复游戏中的策略推理提供了一种新的概率模型。通过模拟数据，我们表明该模型可有效地从有限的数据中快速推断出真正的策略，从而可以对未来的行为进行准确的预测。当将其用于反复犯人困境中人类行为的实验数据时，我们发现了各种复杂性和多样性的策略。从而可以准确预测未来的行为。当将其用于反复犯人困境中人类行为的实验数据时，我们发现了各种复杂性和多样性的策略。从而可以准确预测未来的行为。当将其用于反复犯人困境中人类行为的实验数据时，我们发现了各种复杂性和多样性的策略。