This paper studies the interaction between savagean uncertainty and timepreferences. We introduce a variation of the discounted subjective expected utility model, where time preferences are state dependent. Before uncertainty is resolved, the individual is unsure about the discount factor that will be used, even when evaluating certain payoffs. The model can account for the present bias and diminishing impatience, even if the future is discounted geometrically. The present bias disappears when the immediate payoff becomes uncertain. Although preferences are not stationary, choices may be time consistent.

Exposure to environmental cues triggers sudden preference reversals in several choice contexts, including consumption and intertemporal, social, and risky choices. This paper introduces a dual-self model of cue-triggered behavior that (1) is based on a general mechanism that makes it applicable to many choice contexts, (2) allows a sharp comparative analysis of the responsiveness to cues, (3) can explain a wide range of behavioral anomalies, from a cue-triggered present bias to high-frequency variations in social and risk preferences, and (4) can inform the design of managerial interventions and advertising strategies employing environmental cues. Testable restrictions combining choice and nonchoice data fully characterize the model. This paper was accepted by Manel Baucells, decision analysis.

This paper studies the choice of an individual who acquires information before choosing an action from a set of actions, whose consequences depend on the realization of a state of nature. Information processing can be costly, for example, due to limited attention. We show that the preference of the individual is completely characterized by a preference for early resolution of uncertainty, which becomes indifference when facing degenerate choices. When information acquisition is no longer part of the decision process, the individual is indifferent to the timing of resolution of uncertainty and she behaves according to the subjective learning model of Dillenberger et al. (2014).