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.
Bleichrodt, Han, Martin Filko, Amit Kothiyal, and Peter P. Wakker.
"Making Case-Based Decision Theory Directly Observable."
American Economic Journal: Microeconomics,
Consumer Economics: Empirical Analysis
Criteria for Decision-Making under Risk and Uncertainty
Real Estate Markets, Spatial Production Analysis, and Firm Location: General