Browsing by Author "Krishna, R"
Now showing 1 - 4 of 4
- Results Per Page
- Sort Options
Item Open Access Dynamic preference for flexibility(Econometrica, 2014-01-01) Sadowski, P; Krishna, RWe 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 state contingent infinite horizon consumption problems, where uncertainty about consumption utilities depends on the observable state and the state follows a subjective Markov process. The parameters of the representation are the subjective process that governs the evolution of beliefs over consumption utilities and the discount factor; they are uniquely identified from behavior. We characterize a natural notion of greater preference for flexibility in terms of a dilation of beliefs. An important special case of our representation is a recursive version of the Anscombe-Aumann model with parameters that include a subjective Markov process over states and state-dependent utilities, all of which are uniquely identified. © 2014 The Econometric Society.Item Open Access Randomly Evolving Tastes and Delayed Commitment(Economic Research Initiatives at Duke (ERID), 2016-06-09) Krishna, R; Sadowski, PWe consider a decision maker with randomly evolving tastes who faces dynamic decision situations that involve intertemporal tradeoffs, such as those in consumption savings problems. We axiomatize a recursive representation of choice that features uncertain consumption utilities, which evolve according to a subjective Markov process. The parameters of the representation, which are the subjective Markov process governing the evolution of utilities, and the discount factor, are uniquely identified from behavior. We relate the correlation of tastes over time and the desire to delay commitment to future consumption.Item Open Access Subjective Dynamic Information Constraints(Economic Research Initiatives at Duke (ERID), 2016-04-03) Dillenberger, D; Krishna, R; Sadowski, PWe axiomatize a new class of recursive dynamic models that capture subjective constraints on the amount of information a decision maker can obtain, pay attention to, or absorb, via a Markov Decision Process for Information Choice (MIC). An MIC is a subjective decision process that specifies what type of information about the payoff-relevant state is feasible in the current period, and how the choice of what to learn now affects what can be learned in the future. The constraint imposed by the MIC is identified from choice behavior up to a recursive extension of Blackwell dominance. All the other parameters of the model, namely the anticipated evolution of the payoff-relevant state, state dependent consumption utilities, and the discount factor are also uniquely identified.Item Open Access Supplement to 'Subjective Dynamic Information Constraints'(Economic Research Initiatives at Duke (ERID), 2016-04-01) Dillenberger, D; Krishna, R; Sadowski, PSupplement to "Subjective Dynamic Information Constraints" (http://ssrn.com/abstract=2774300). All references to definitions and results in this Supplement refer to Dillenberger, Krishna, and Sadowski (2016, henceforth DKS) unless otherwise specified. This supplement is organized as follows. Section 1 establishes the Abstract Static Representation that is the starting point for our derivations in Appendix C of DKS. Section 2 reviews relevant notions from convex analysis. Section 3 provides a preference independent notion of minimality on the space of rics, which is referred to in Section 6 of DKS. Section 4 provides a metric on the space of partitions as referred to in Appendix A.3 of DKS. Section 5 extends the existence of the RAA representation, which is established in Krishna and Sadowski (2014) for finite prize spaces, to our domain with a compact set of prizes, as discussed in Appendix A.7 of DKS. Finally, Section 6 provides a detailed proof of the partitional representation introduced in Appendix C.1 of DKS.