DIMENSIONALITY AND DISAGREEMENT: ASYMPTOTIC BELIEF DIVERGENCE IN RESPONSE TO COMMON INFORMATION

• 成果类型: Article
• 署名作者: Loh, Isaac; Phelan, Gregory
• 署名单位: Northwestern University; Williams College
• 刊物名称: INTERNATIONAL ECONOMIC REVIEW
• ISSN/ISSBN: 0020-6598
• DOI: 10.1111/iere.12406
• 发表日期: 2019
• 页码: 1861-1876
• 关键词: polarization; attention; MODEL

摘要

We provide a model of boundedly rational, multidimensional learning and characterize when beliefs will converge to the truth. Agents maintain beliefs as marginal probabilities instead of joint probabilities, and agents' information is of lower dimension than the model. As a result, for some observations, agents may face an identification problem affecting the role of data in inference. Beliefs converge to the truth when these observations are rare, but beliefs diverge when observations presenting an identification problem are frequent. Robustly, two agents with differing priors who observe identical, unambiguous information may disagree forever, with stronger disagreement the more information received.