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英文文献：Learning from Inferred Foregone Payoffs-从推断的预先付出的代价中学习
英文文献作者：Ralph-C. Bayer,Hang Wu
英文文献摘要：
A player's knowledge of her own actions and the corresponding own payoffs may enable her to infer or form belief about what the payoffs would have been if she had played differently. In studies of low-information game settings, however, players' ex-post inferences and beliefs have been largely ignored by quantitative learning models. For games with large strategy spaces, the omission may seriously weaken the predictive power of a learning model. We propose an extended payoff assessment learning model which explicitly incorporates players' ex-post inferences and beliefs about the foregone payoffs for unplayed strategies. We use the model to explain the pricing and learning behavior observed in a Bertrand market experiment. Maximum likelihood estimation shows that the extended model organizes the data remarkably well at both aggregate level and individual level.
一个玩家对自己的行为和相应的回报的了解，可能使她能够推断或形成一种信念，即如果她以不同的方式玩游戏，她的回报会是什么。然而，在低信息游戏设置的研究中，定量学习模型在很大程度上忽略了参与者的事后推断和信念。对于策略空间较大的博弈，缺失可能会严重削弱学习模型的预测能力。我们提出了一个扩展的收益评估学习模型，该模型明确地包含了玩家对未使用策略的预期收益的事后推断和信念。我们使用这个模型来解释在伯特兰市场实验中观察到的定价和学习行为。最大似然估计表明，扩展模型在总体和个体层面上都能很好地组织数据。
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