Counterfactuals with Latent Information
- (pp. 343-68)
AbstractWe describe a methodology for making counterfactual predictions in settings where the information held by strategic agents and the distribution of payoff-relevant states of the world are unknown. The analyst observes behavior assumed to be rationalized by a Bayesian model, in which agents maximize expected utility, given partial and differential information about the state. A counterfactual prediction is desired about behavior in another strategic setting, under the hypothesis that the distribution of the state and agents' information about the state are held fixed. When the data and the desired counterfactual prediction pertain to environments with finitely many states, players, and actions, the counterfactual prediction is described by finitely many linear inequalities, even though the latent parameter, the information structure, is infinite dimensional.
CitationBergemann, Dirk, Benjamin Brooks, and Stephen Morris. 2022. "Counterfactuals with Latent Information." American Economic Review, 112 (1): 343-68. DOI: 10.1257/aer.20210496
- D44 Auctions
- D82 Asymmetric and Private Information; Mechanism Design
- D83 Search; Learning; Information and Knowledge; Communication; Belief; Unawareness