Time Preferences
Paper Session
Sunday, Jan. 3, 2021 3:45 PM - 5:45 PM (EST)
- Chair: Mu Zhang, Princeton University
Time Consistency, Temporal Resolution Indifference and the Separation of Time and Risk
Abstract
For general choice spaces, standard dynamic preference models cannot simultaneously satisfy the desirable properties of time consistency, temporal resolution of risk indifference and the separation of time and risk preferences. However in the context of the dynamic consumption-portfolio optimization underlying a number of asset pricing and macro models, we derive necessary and sufficient conditions such that all three properties are satisfied. Only under these conditions, can one unambiguously separate the specific roles of time and risk preferences in explaining asset demand and intertemporal consumption-saving behavior.The Evolution of Ineptitude: A Conundrum
Abstract
The abilities that produce evolutionary advantages can be systematically impaired after repeated competitions and highly hereditarian transmission of the winners' abilities. Economically relevant abilities may therefore not emerge after multiple competitions. Instead, ability may gradually deteriorate over time (i.e., converge to complete ineptitude) or change with no ultimate direction. This evolutionary antithesis is counterintuitive, but holds under general premises of a core model of repeated competition.Relative Maximum Likelihood Updating of Ambiguous Beliefs
Abstract
This paper proposes and axiomatizes a new updating rule: Relative Maximum Likelihood (RML) for ambiguous beliefs represented by a set of priors (C). This rule takes the form of applying Bayes' rule to a subset of the set C. This subset is a linear contraction of C towards its subset assigning maximal probability to the observed event. The degree of contraction captures the extent of willingness to discard priors based on likelihood when updating. Two well-known updating rules, Full Bayesian (FB) and Maximum Likelihood (ML), are included as special cases of RML. An axiomatic characterization of conditional preferences generated by RML updating is provided when the preferences admit Maxmin Expected Utility representations. The axiomatization relies on weakening the axioms characterizing FB and ML. The axiom characterizing ML is identified for the first time in this paper, addressing a long-standing open question in the literature.Optimistic Dynamic Inconsistency
Abstract
We model a decision maker who anticipates her preference to change in the future, and optimistically evaluates each menu according to the best choice that could possibly be made by her future self. We characterize this menu preference by axioms weak order, independence, semi-continuity, extremity and concavity. We prove the uniqueness of our representation and introduce comparative measures of optimism. We illustrate how our model connects optimism with the naive quasi-hyperbolic discounting model introduced by O’Donoghue and Rabin (1999, 2001). Our model generates novel predictions on decision makers’ procrastination behavior. In particular, we show that uncertainty about future payoffs can inhibit procrastination.JEL Classifications
- D8 - Information, Knowledge, and Uncertainty
- D9 - Micro-Based Behavioral Economics