Statistical Inference in Games
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. An application to search and matching markets demonstrates that statistical inference from small samples leads to significantly larger unemployment --- and significantly larger employment fluctuations in response to exogenous shocks --- than in Nash Equilibrium.
Yuval Salant, Joshua Cherry
Salant, Yuval, and Joshua Cherry. 2019. Statistical Inference in Games.