Making Case-Based Decision Theory Directly Observable
提出一种无需参数假设的方法直接观测基于案例的决策理论，通过房地产投资实验验证其可行性，并发现记忆案例可分离性假设的一个例外。
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.