Counterfactuals with Latent Information

Dirk Bergemann, Benjamin Brooks and Stephen Morris

No 2162R, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: We describe a methodology for making counterfactual predictions when the information held by strategic agents is a latent parameter. The analyst observes behavior which is rationalized by a Bayesian model in which agents maximize expected utility, given partial and differential information about payoff-relevant states of the world. A counterfactual prediction is desired about behavior in another strategic setting, under the hypothesis that the distribution of 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, there is a finite dimensional description of the sharp counterfactual prediction, even though the latent parameter, the type space, is infinite dimensional.

Keywords: Counterfactuals; Bayes correlated equilibrium; Information structure; Type space; Linear program (search for similar items in EconPapers)
JEL-codes: C72 D44 D82 D83 (search for similar items in EconPapers)
Pages: 33 pages
Date: 2019-02, Revised 2019-02
New Economics Papers: this item is included in nep-gth, nep-mic and nep-upt
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Counterfactuals with Latent Information (2022)
Working Paper: Counterfactuals with Latent Information (2021)
Working Paper: Counterfactuals with Latent Information (2021)
Working Paper: Counterfactuals with Latent Information (2021)
Working Paper: Counterfactuals with Latent Information (2019)
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