Learning with limited memory: Bayesianism vs heuristics

• 成果类型: Article
• 署名作者: Chatterjee, Kalyan; Hu, Tai -Wei
• 署名单位: Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of Bristol
• 刊物名称: JOURNAL OF ECONOMIC THEORY
• ISSN/ISSBN: 0022-0531
• DOI: 10.1016/j.jet.2023.105642
• 发表日期: 2023
• 关键词: Imperfect recall; bounded rationality; Bounded memory; heuristics; Behavioral biases

摘要

Bayesian analysis is considered the optimal way of processing information. However, it often leads to problems for decision-makers with constrained cognitive capacity. Modeling such constrained capacity by finite automata, we answer two questions in the context of Wald's (1947) sequential analysis, namely in what environments is optimal Bayesian analysis possible even with constraints; also, when it is not possible what simplifications in the analysis enable us to obtain a satisfactory outcome. We identify two features of the simplified analysis: information stickiness (ignoring information) and rule stickiness (ignoring small differences in the environment).(c) 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons .org /licenses /by /4 .0/).