Non-Bayesian updating: a theoretical framework

• 成果类型: Article
• 署名作者: Epstein, Larry G.; Noor, Jawwad; Sandroni, Alvaro
• 署名单位: Boston University; University of Pennsylvania
• 刊物名称: THEORETICAL ECONOMICS
• ISSN/ISSBN: 1933-6837
• 发表日期: 2008-06-01
• 页码: 193-229
• 关键词: Non-Bayesian updating; temptation and self-control; overreaction; underreaction; learning; law of small numbers

摘要

This paper models an agent in a multi-period setting who does not update according to Bayes' Rule, and who is self-aware and anticipates her updating behavior when formulating plans. Choice-theoretic axiomatic foundations are provided to capture updating biases that reflect excessive weight given to either prior beliefs, or, alternatively, to observed data. A counterpart of the exchangeable Bayesian learning model is also described.