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Project Citation: 

Bleichrodt, Han, Filko, Martin, Kothiyal, Amit, and Wakker, Peter P. Replication data for: Making Case-Based Decision Theory Directly Observable. Nashville, TN: American Economic Association [publisher], 2017. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2019-10-12. https://doi.org/10.3886/E114343V1

Project Description

Summary:  View help for Summary 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.

Scope of Project

Subject Terms:  View help for Subject Terms Choice under uncertainty
JEL Classification:  View help for JEL Classification
      D12 Consumer Economics: Empirical Analysis
      D81 Criteria for Decision-Making under Risk and Uncertainty
      R30 Real Estate Markets, Spatial Production Analysis, and Firm Location: General
Geographic Coverage:  View help for Geographic Coverage the Netherlands
Time Period(s):  View help for Time Period(s) 5/1/2011 – 12/10/2011 (Experiment was done late 2011. Concerning house markt price of three preceding years)
Data Type(s):  View help for Data Type(s) experimental data


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