在不重复的同时移动游戏中预测人类行为
Predicting Human Behavior in Unrepeated, Simultaneous-Move Games
摘要：
人们通常会假设智能体会采取纳什均衡策略;然而，实验研究表明，纳什均衡往往是一个穷人的描述人类球员的行为在不重复的正常形式的游戏。在本文中，我们分析了五个广泛研究的模型（量子响应平衡，k级，认知层次，QLk，和嘈杂的内省），旨在描述实际的，而不是理想化的，人类的行为在这样的游戏。我们对这些模型进行了我们认为是最全面的荟萃分析，利用了记录人类玩双人游戏的文献中的10个不同数据集。我们首先评估模型的泛化或预测性能，询问模型在基于单独的训练数据校准其参数后，与未知测试数据的拟合程度。令人惊讶的是，我们发现我们称之为Stahl & Wilson（1994）的QLk模型始终获得最佳性能。基于这一发现，我们描述了分析模型参数后验分布的方法。我们发现QLk的参数被设置为与其预期的经济解释不一致的值。因此，我们探索了QLk的变化，最终确定了一个新的模型家族，该家族具有更少的参数，产生了更简约的参数值，并实现了更好的预测性能。
英文摘要：
It is common to assume that agents will adopt Nash equilibrium strategies; however, experimental studies have demonstrated that Nash equilibrium is often a poor description of human players' behavior in unrepeated normal-form games. In this paper, we analyze five widely studied models (Quantal Response Equilibrium, Level-k, Cognitive Hierarchy, QLk, and Noisy Introspection) that aim to describe actual, rather than idealized, human behavior in such games. We performed what we believe is the most comprehensive meta-analysis of these models, leveraging ten different data sets from the literature recording human play of two-player games. We began by evaluating the models' generalization or predictive performance, asking how well a model fits unseen test data after having had its parameters calibrated based on separate training data. Surprisingly, we found that what we dub the QLk model of Stahl & Wilson (1994) consistently achieved the best performance. Motivated by this finding, we describe methods for analyzing the posterior distributions over a model's parameters. We found that QLk's parameters were being set to values that were not consistent with their intended economic interpretations. We thus explored variations of QLk, ultimately identifying a new model family that has fewer parameters, gives rise to more parsimonious parameter values, and achieves better predictive performance.
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。
