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, I also assume dynamic consistency and conditions which deliver a static TSE for consumption plans only pay non-zero consequences for one period. 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 an 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 exists uniquely. Due to the "small domain" setup, the agent is short-sighted in the sense that they only process subjective probabilities over events defined on one period uncertainty over next period states, which differs from "far-sighted" assumption in most extant models that assume subjective probabilities exist over events defined on multiple periods. It is shown that under some extra conditions, our DM applies Bayes' rule "updating' her subjective beliefs.