Dynamic Choice and Common Learning

Working Paper: CEPR ID: DP16160

Authors: Rahul Deb; Ludovic Renou

Abstract: A researcher observes a finite sequence of choices made by multiple agents in a binary-state environment. Agents maximize expected utilities that depend on their chosen alternative and the unknown underlying state. Agents learn about the time-varying state from the same information and their actions change because of the evolving common belief. The researcher does not observe agents' preferences, the prior, the common information and the stochastic process for the state. We characterize the set of choices that are rationalized by this model and generalize the information environments to allow for private information. We discuss the implications of our results for uncovering discrimination and committee decision making.

Keywords: No keywords provided

JEL Codes: No JEL codes provided


Causal Claims Network Graph

Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.


Causal Claims

CauseEffect
agents' choices (L85)rationalizability of choice data (D01)
absence of cycles in choices (C25)rationalizability of choice data (D01)
observed choices (D01)underlying preferences (D01)
agents' beliefs (D83)agents' decisions (D79)

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