Browsing by Author "Dillenberger, D"

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    A Theory of Subjective Learning
    (Economic Research Initiatives at Duke (ERID), 2012-08-31) Dillenberger, D; Lleras, J; Sadowski, P; Takeoka, N
    We study an individual who faces a dynamic decision problem in which the process of information arrival is unobserved by the analyst. We derive two utility representations of preferences over menus of acts that capture the individual’s uncertainty about his future beliefs. The most general representation identifies a unique probability distribution over the set of posteriors that the decision maker might face at the time of choosing from the menu. We use this representation to characterize a notion of “more preference for flexibility” via a subjective analogue of Blackwell’s (1951, 1953) comparisons of experiments. A more specialized representation uniquely identifies information as a partition of the state space. This result allows us to compare individuals who expect to learn differently, even if they do not agree on their prior beliefs. We conclude by extending the basic model to accommodate an individual who expects to learn gradually over time by means of a subjective filtration.
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    A theory of subjective learning
    (Journal of Economic Theory, 2014-01-01) Dillenberger, D; Lleras, JS; Sadowski, P; Takeoka, N
    We study an individual who faces a dynamic decision problem in which the process of information arrival is unobserved by the analyst. We elicit subjective information directly from choice behavior by deriving two utility representations of preferences over menus of acts. One representation uniquely identifies information as a probability measure over posteriors and the other identifies information as a partition of the state space. We compare individuals who expect to learn differently in terms of their preference for flexibility. On the extended domain of dated-menus, we show how to accommodate gradual learning over time by means of a subjective filtration. © 2014 Elsevier Inc.
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    Inertial Behavior and Generalized Partition
    (Economic Research Initiatives at Duke (ERID), 2016-05-01) Dillenberger, D; Sadowski, P
    We call behavior inertial if it does not react to the apparent arrival of relevant information. In a context where the precise information content of signals is subjective, we formulate an axiom that captures inertial behavior, and provide a representation that explains such behavior as that of a rational decision maker who perceives a particular type of information structure, which we call a generalized partition. We characterize the learning processes that can be described by a generalized partition. We proceed to assume that there is a true underlying information structure that may not be a generalized partition, and investigate different channels that may lead the decision maker to nonetheless perceive a generalized partition (and thus to display inertial behavior): A cognitive bias referred to as cognitive inertia and a bound on rationality which we term shortsightedness.
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    Subjective Dynamic Information Constraints
    (Economic Research Initiatives at Duke (ERID), 2016-04-03) Dillenberger, D; Krishna, R; Sadowski, P
    We 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.
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    Supplement to 'Subjective Dynamic Information Constraints'
    (Economic Research Initiatives at Duke (ERID), 2016-04-01) Dillenberger, D; Krishna, R; Sadowski, P
    Supplement 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.