Tootfinder

Sequential Non-Bayesian Persuasion
Yaron Azrieli, Rachana Das
https://arxiv.org/abs/2508.09464 https://arxiv.org/pdf/2508.09464

Sequential Non-Bayesian Persuasion
We study a model of persuasion in which the receiver is a `conservative Bayesian' whose updated belief is a convex combination of the prior and the correct Bayesian posterior. While in the classic Bayesian case providing information sequentially is never valuable, we show that the sender gains from sequential persuasion in many of the environments considered in the literature on strategic information transmission. We also consider the case in which the sender and receiver are both biased and pr…

Entropy Regularized Belief Reporting
Elchin Suleymanov
https://arxiv.org/abs/2506.22649 https://arxiv.org/pdf/2506.22649

Entropy Regularized Belief Reporting
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 capt…

Plausible GMM: A Quasi-Bayesian Approach
Victor Chernozhukov, Christian B. Hansen, Lingwei Kong, Weining Wang
https://arxiv.org/abs/2507.00555 https://

Plausible GMM: A Quasi-Bayesian Approach
Structural estimation in economics often makes use of models formulated in terms of moment conditions. While these moment conditions are generally well-motivated, it is often unknown whether the moment restrictions hold exactly. We consider a framework where researchers model their belief about the potential degree of misspecification via a prior distribution and adopt a quasi-Bayesian approach for performing inference on structural parameters. We provide quasi-posterior concentration results, …

Misspecified Bayesianism
Pooya Molavi
https://arxiv.org/abs/2507.22775 https://arxiv.org/pdf/2507.22775

Misspecified Bayesianism
An agent is a misspecified Bayesian if she updates her belief using Bayes' rule given a subjective, possibly misspecified model of her signals. This paper shows that a belief sequence is consistent with misspecified Bayesianism if the prior contains a grain of the average posterior, i.e., is a mixture of the average posterior and another distribution. A partition-based variant of the grain condition is both necessary and sufficient. Under correct specification, the grain condition reduces to th…