2025/06/27 by Elchin Suleymanov, Suleymanov, Elchin
Decision Sciences · Physics and Astronomy · #Decision-Making and Behavioral Economics #FOS: Economics and business #Game Theory and Applications #Statistical Mechanics and Entropy #Theoretical Economics (econ.TH)
paper · pdf · doi:10.48550/arxiv.2506.22649
openalex publication_date 2025/06/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 provide the structural properties of the model that allow for identification of the latent benchmark prior and apply the model to the experimental data from Benjamin et al. (2017).