Robust inattentive discrete choice

• 成果类型: Article
• 署名作者: Hansen, Lars Peter; Miao, Jianjun; Xing, Hao
• 署名单位: University of Chicago; University of Chicago; Zhejiang University; Zhejiang University; Boston University; Boston University
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-12004
• DOI: 10.1073/pnas.2416643122
• 发表日期: 2025-02-11
• 关键词: rational inattention; MODEL; foundation; capacity

摘要

Rational inattention models characterize optimal decision-making in data-rich environ-ments. In such environments, it can be costly to look carefully at all of the information.Some information is much more salient for the decision at hand and merits closerscrutiny. The inattention decision model formalizes this choice and deduces how bestto navigate through the potentially vast array of data when making decisions. In therational formulation, the decision-maker commits fully to a subjective prior distributionover the possible states of the world that could be realized. We relax this assumptionand look for a robustly optimal solution to the inattention problem by allowing thedecision-maker to be ambiguity averse with respect to this prior. We feature a setup thatis deliberately simple by a) assuming a discrete set of choices, b) using Shannon's mutualinformation to quantify attention costs, and c) imposing relative entropy with respect toa baseline probability distribution to quantify prior divergence. We provide necessaryand sufficient conditions for the robust solution and develop numerical methods tosolve it. In comparison to the rational solution with no prior uncertainty, our decision-maker slants priors in more cautious or pessimistic directions when deducing how toallocate attention over the range of available information. This approach implementsa form of robustness to prior misspecification, or equivalently, a form of ambiguityaversion. We explore some examples that show how the robust solution differs fromthe rational solution with a commitment to a subjective prior distribution and how itdiffers from imposing risk aversion.