We study a decision maker (DM) who has preferences over choice problems, which are sets of payoff-allocations between herself and a passive recipient. An example of such a set is the collection of possible allocations in the classic dictator game. The choice of an allocation from the set is...

We consider a decision maker who faces dynamic decision situations that involve intertemporal trade‐offs, as in consumption–savings problems, and who experiences taste shocks that are transient contingent on the state of the world. We axiomatize a recursive representation of choice over...

We study a two-stage choice problem, where alternatives are allocations between the decision maker (DM) and a passive recipient. The recipient observes choice behavior in stage two, while stage one choice is unobserved. Choosing selfishly in stage two, in the face of a fairer available...

We study an individual who faces a dynamic decision problem in which the process of information arrival is unobserved by the analyst. We derive a sequence of representations of preferences over menus of acts that capture the individual's uncertainty about his future beliefs. Using the most...

We study a decision maker who faces a dynamic decision problem in which the process of information arrival is subjective. By studying preferences over menus of acts, we derive a sequence of utility representations that captures the decision maker’s uncertainty about the beliefs he will hold...

We consider a decision maker who experiences transient preference shocks when faced with dynamic decision situations that involve intertemporal tradeoffs, such as those in consumption savings problems. We axiomatize a recursive representation of choice over infinite horizon consumption problems...

We study an individual who faces a dynamic decision problem in which the process of information arrival is unobserved by the analyst. We derive a sequence of representations of preferences over menus of acts that capture the individual's uncertainty about his future beliefs. Using the most...