Recursive Two-Stage Evaluation Model for Dynamic Decision Making Under Ambiguity

In this paper, the two-stage-evaluation (TSE) model for decision making under ambiguity (He 2021) is extended to intertemporal setting in an axiomatic approach. The first set of axioms employed are commonly adopted for dynamic non-expected utility models in the literature. Besides these regular axioms, we also assume dynamic consistency and assumptions which deliver a static TSE for one stage consumption plans. It is shown that these axioms hold if and only if these exists a recursively defined evaluation utility model representing decision maker (DM)’s preferences over consumption plans conditional on arriving at any node in the event tree. Such a recursive form implies that one can apply dynamic programming technique (rolling back the decision tree) to solve a dynamic decision making problem under TSE model. It can be shown that the solution for the recursively defined dynamic TSE model exits uniquely. Due to the “small domain” setup, the agent is short-sighted. Under “far-sighted” assumption, the paper also derives conditions which imply Bayesian updating in the dynamic setting