Part-Time Bayesians: Incentives and Behavioral Heterogeneity in Belief Updating

• 成果类型: Article
• 署名作者: Alos-Ferrer, Carlos; Garagnani, Michele
• 署名单位: University of Zurich
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2022.4584
• 发表日期: 2023
• 页码: 5523-5542
• 关键词: Bayesian updating; incentives; reinforcement; Heterogeneity; finite mixture models; Machine Learning

摘要

Decisions in management and finance rely on information that often includes win lose feedback (e.g., gains and losses, success and failure). Simple reinforcement then suggests to blindly repeat choices if they led to success in the past and change them otherwise, which might conflict with Bayesian updating of beliefs. We use finite mixture models and hidden Markov models, adapted from machine learning, to uncover behavioral heterogeneity in the reliance on difference behavioral rules across and within individuals in a belief-updating experiment. Most decision makers rely both on Bayesian updating and reinforcement. Paradoxically, an increase in incentives increases the reliance on reinforcement because the win lose cues become more salient.