Geometric methods for finite rational inattention

• 成果类型: Article
• 署名作者: Armenter, Roc; Mueller-Itten, Michele; Stangebye, Zachary R.
• 署名单位: Federal Reserve System - USA; Federal Reserve Bank - Philadelphia; University of St Gallen; University of Notre Dame
• 刊物名称: QUANTITATIVE ECONOMICS
• ISSN/ISSBN: 1759-7323
• DOI: 10.3982/QE2050
• 发表日期: 2024
• 页码: 115-144
• 关键词: rational inattention; Shannon entropy; information acquisition; learning; consideration sets; C63; D81; D83

摘要

We present a geometric approach to the finite Rational Inattention (RI) model, recasting it as a convex optimization problem with reduced dimensionality that is well suited to numerical methods. We provide an algorithm that outperforms existing RI computation techniques in terms of both speed and accuracy in both static and dynamic RI problems. We further introduce methods to quantify the impact of numerical inaccuracy on the model's outcomes and to produce robust predictions regarding the most frequently implemented actions.