标题： 机器学习来信任 显示英文标题

标题： Machine-Learning to Trust

摘要： 当玩家的均衡信念由惩罚复杂性的机器学习方法塑造时，玩家能否维持长期信任？我研究了一个游戏，在这个游戏中，一个无限序列的具有单期回忆的参与者决定是否信任他们的直接后继者。信任的成本是状态相关的。每个玩家的最佳反应基于对他人行为的信念，这是相对于相关情况的划分对真实总体策略的粗略拟合。在均衡中，这个划分最小化了均方预测误差的总和和一个与其大小成比例的复杂性惩罚。与对称混合策略纳什均衡相比，这个解概念显著缩小了信任的范围。 显示英文摘要

摘要： Can players sustain long-run trust when their equilibrium beliefs are shaped by machine-learning methods that penalize complexity? I study a game in which an infinite sequence of agents with one-period recall decides whether to place trust in their immediate successor. The cost of trusting is state-dependent. Each player's best response is based on a belief about others' behavior, which is a coarse fit of the true population strategy with respect to a partition of relevant contingencies. In equilibrium, this partition minimizes the sum of the mean squared prediction error and a complexity penalty proportional to its size. Relative to symmetric mixed-strategy Nash equilibrium, this solution concept significantly narrows the scope for trust.