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Generalized Partition and Subjective Filtration

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  • David Dillenberger
  • Philipp Sadowski
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    Abstract

    We study an individual who faces a dynamic decision problem in which the process of information arrival is unobserved by the analyst, and hence should be identified from observed choice data. An information structure is objectively describable if signals correspond to events of the objective state space. We derive a representation of preferences over menus of acts that captures the behavior of a Bayesian decision maker who expects to receive such signals. The class of information structures that can support such a representation generalizes the notion of a partition of the state space. The representation allows us to compare individuals in terms of the preciseness of their information structures without requiring that they share the same prior beliefs. We apply the model to study an individual who anticipates gradual resolution of uncertainty over time. Both the filtration (the timing of information arrival with the sequence of partitions it induces) and prior beliefs are uniquely identified.

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    Bibliographic Info

    Paper provided by Duke University, Department of Economics in its series Working Papers with number 12-15.

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    Length: 30
    Date of creation: 2012
    Date of revision:
    Handle: RePEc:duk:dukeec:12-15

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    Postal: Department of Economics Duke University 213 Social Sciences Building Box 90097 Durham, NC 27708-0097
    Phone: (919) 660-1800
    Fax: (919) 684-8974
    Web page: http://econ.duke.edu/

    Related research

    Keywords: Resolution of uncertainty; valuing binary bets more; generalized partition; subjective filtration;

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