Counterfactuals with Latent Information
- (pp. 343-68)
Abstract
We 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.Citation
Bergemann, Dirk, Benjamin Brooks, and Stephen Morris. 2022. "Counterfactuals with Latent Information." American Economic Review, 112 (1): 343-68. DOI: 10.1257/aer.20210496Additional Materials
JEL Classification
- D44 Auctions
- D82 Asymmetric and Private Information; Mechanism Design
- D83 Search; Learning; Information and Knowledge; Communication; Belief; Unawareness