Statistical Inference in Games
研究了玩家通过小样本统计推断对手行动并选择最优策略的均衡，刻画了大规模两行动博弈中的抽样均衡，并应用于竞争市场、网络效应市场、垄断定价和搜索匹配市场。
We consider statistical inference in games. Each player obtains a small random sample of other players' actions, uses statistical inference to estimate their actions, and chooses an optimal action based on the estimate. In a sampling equilibrium with statistical inference (SESI), the sample is drawn from the distribution of players' actions based on this process. We characterize the set of SESIs in large two‐action games, and compare their predictions to those of Nash equilibrium, and for different sample sizes and statistical inference procedures. We then study applications to competitive markets, markets with network effects, monopoly pricing, and search and matching markets.