Entropy Regularized Belief Reporting

Elchin Suleymanov
Authors registered in the RePEc Author Service: Elchin Suleymanov and Elchin Suleymanov

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Abstract: This paper investigates a model of partition dependence, a widely reported experimental finding where the agent's reported beliefs depend on how the states are grouped. In the model, called Entropy Regularized Belief Reporting (ERBR), the agent is endowed with a latent benchmark prior that is unobserved by the analyst. When presented with a partition, the agent reports a prior that minimizes Kullback-Leibler divergence from the latent benchmark prior subject to entropy regularization. This captures the intuition that while the agent would like to report a prior that is close to her latent benchmark prior, she may also have a preference to remain noncommittal. I axiomatically characterize the model and apply it to the experimental data from Benjamin et al. (2017).

Date: 2025-06
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