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Super-extremal processes and the argmax process

Published online by Cambridge University Press:  14 July 2016

Sidney I. Resnick*
Affiliation:
Cornell University
Rishin Roy*
Affiliation:
University of Toronto
*
Postal address: School of Operations Research and Industrial Engineering, ETC Building, Cornell University, Ithaca, NY 14853, USA.
∗∗Present address: Paribas Corporation, Equitable Tower, 787 7th Avenue, New York, NY 10019, USA.

Abstract

In this paper, we develop the probabilistic foundations of the dynamic continuous choice problem. The underlying choice set is a compact metric space E such as the unit interval or the unit square. At each time point t, utilities for alternatives are given by a random function . To achieve a model of dynamic continuous choice, the theory of classical vector-valued extremal processes is extended to super-extremal processesY= {Yt, t > 0}. At any t > 0, Yt is a random upper semicontinuous function on a locally compact, separable, metric space E. General path properties of Y are discussed and it is shown that Y is Markov with state-space US(E). For each t > 0, Yt is associated.

For a compact metric E, we consider the argmax process M = {Mt, t > 0}, where . In the dynamic continuous choice application, the argmax process M represents the evolution of the set of random utility maximizing alternatives. M is a closed set-valued random process, and its path properties are investigated.

Type
Research Article
Copyright
Copyright © Applied Probability Trust 1994 

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Footnotes

Partially supported by NSF Grant DMS 9100027.

Partially supported by the Natural Sciences and Engineering Research Council of Canada.

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