Belief Updating Beyond the Two-State Setting

• 成果类型: Article
• 署名作者: Mohrschladt, Hannes; Baars, Maren; Langer, Thomas
• 署名单位: University of Munster
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2022.00513
• 发表日期: 2024
• 页码: 6761-6777
• 关键词: two-state setting; information weight; over-and underinference

摘要

Heuristics and biases in probabilistic belief updating have typically been examined in simple two-state experimental settings. We argue that the two-state setting has probabilistic properties that do not extend to settings with more states. With three states, we find that individuals apply similar heuristics, such as representativeness and anchoring, when providing posterior probability distributions. However, because of the different normative benchmark, the use of these heuristics results in different biases for point estimates. In particular, we demonstrate that the well-known finding of stronger underinference for larger signal sets does not translate from the two-state to the three-state setting. Our findings caution against an indiscriminate transfer of updating biases observed in two-state settings to a broad set of real-world applications.