Article2017

Making Case-Based Decision Theory Directly Observable

Han Bleichrodt A · Martin Filko · Amit Kothiyal · Peter P. Wakker

25 citations0/0 files storedAmerican Economic Journal Microeconomics

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Decision-Making and Behavioral Economics Experimental Behavioral Economics Studies AI-based Problem Solving and Planning

Abstract

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. (JEL D12, D81, R30)

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References

1992Advances in prospect theory: Cumulative representation of uncertainty 1994Decisions with Multiple Objectives: Preferences and Value Tradeoffs.2002Risk Aversion and Incentive Effects 2004Decisions from Experience and the Effect of Rare Events in Risky Choice 2000Risk Aversion and Expected-utility Theory: A Calibration Theorem 2013Thirty Years of Prospect Theory in Economics: A Review and Assessment 1994Investigating Generalizations of Expected Utility Theory Using Experimental Data 2000Parameter-Free Elicitation of Utility and Probability Weighting Functions

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2020Experience and rationality under risk: re-examining the impact of sampling experience 2020Theories and cases in decisions under uncertainty 2019Correlation neglect and case-based decisions 2020Case-Based Decision Theory: From the Choice of Actions to Reasoning about Theories 2019A Review of Case-Based Decision Theory

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