Making Case-Based Decision Theory Directly Observable

• 成果类型: Article
• 署名作者: Bleichrodt, Han; Filko, Martin; Kothiyal, Amit; Wakker, Peter P.
• 署名单位: Erasmus University Rotterdam - Excl Erasmus MC; Erasmus University Rotterdam; Max Planck Society
• 刊物名称: AMERICAN ECONOMIC JOURNAL-MICROECONOMICS
• ISSN/ISSBN: 1945-7669
• DOI: 10.1257/mic.20150172
• 发表日期: 2017
• 页码: 123-151
• 关键词: expected-utility-theory; prospect-theory; risk-aversion; Similarity; preferences; uncertainty; INFORMATION; CHOICE

摘要

Case-based decision theory (CBDT) provided a new way of revealing preferences, with decisions under uncertainty determined by similarities with cases in memory. This paper introduces a method to measure CBDT that requires no commitment to parametric families and that relates directly to decisions. Thus, CBDT becomes directly observable and can be used in prescriptive applications. Two experiments on real estate investments demonstrate the feasibility of our method. Our implementation of real incentives not only avoids the income effect, but also avoids interactions between different memories. We confirm CBDT's predictions except for one violation of separability of cases in memory.