Non-parametric Bayesian inference of strategies in repeated games
重复博弈策略的非参数贝叶斯推断AI 解读 ↓
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分类依据与纠错
AI 根据所提供的论文材料归类：摘要将潜在博弈策略表示为有限自动机，并由行为数据估计其概率模型，直接关联微观策略行为的结构估计。
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重复博弈策略的非参数贝叶斯推断
- 研究问题
- 当重复博弈行为数据稀疏且有噪声、潜在策略空间迅速扩大时，如何推断人类使用的合作与竞争策略并发现未预设策略？
- 方法与思路
- 作者以有限自动机表示策略，建立非参数贝叶斯概率模型，避免预先限定有限自动机假设集合，并使用模拟和重复囚徒困境实验数据。
- 材料陈述的贡献
- 摘要称，模型在模拟中能从有限数据较快恢复真实策略并预测后续行为；在人类实验数据中识别出复杂程度和形式各异的策略。
- 阅读关注点
- 应核对策略空间与先验的构造、行为噪声处理和后验计算，以及真实策略恢复与行为预测分别采用的评价标准。
- 阅读边界
- 仅据所给版本摘要，无法判断策略可识别性、先验敏感性和计算成本；模拟中的恢复表现不能直接保证真实实验中的策略被正确识别。
原文摘要
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.