Noisy Directional Learning and the Logit Equilibrium
构建了一个动态模型，其中代理人受正态误差影响，向更高收益方向调整决策，该过程由福克-普朗克方程描述，并在所有势博弈中稳定。均衡时，决定期望收益的分布与对数函数应用于这些期望收益产生的分布一致，形成纳什均衡的随机推广，可解释实验室异常数据。
Abstract We specify a dynamic model in which agents adjust their decisions toward higher payoffs, subject to normal error. This process generates a probability distribution of players’ decisions that evolves over time according to the Fokker–Planck equation. The dynamic process is stable for all potential games, a class of payoff structures that includes several widely studied games. In equilibrium, the distributions that determine expected payoffs correspond to the distributions that arise from the logit function applied to those expected payoffs. This “logit equilibrium” forms a stochastic generalization of the Nash equilibrium and provides a possible explanation of anomalous laboratory data.