Dynamic Choices and Common Learning

TL;DR

This paper models how agents' dynamic choices in a binary environment are driven by evolving common beliefs, even with limited observable information, and explores implications for understanding decision-making processes.

Contribution

It provides a characterization of rationalizable choices in dynamic settings with common and private information, generalizing previous models.

Findings

Characterization of rationalizable choice sequences

Extension to private information environments

Implications for discrimination and committee decisions

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.