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Risk-Averse Control of Undiscounted Transient Markov Models

Ozlem Cavus (ozlem_cavus***at***yahoo.com)
Andrzej Ruszczynski (rusz***at***business.rutgers.edu)

Abstract: We use Markov risk measures to formulate a risk-averse version of the undiscounted total cost problem for a transient controlled Markov process. We derive risk-averse dynamic programming equations and we show that a randomized policy may be strictly better than deterministic policies, when risk measures are employed. We illustrate the results on an optimal stopping problem and an organ transplant problem.

Keywords: Dynamic Risk Measures; Markov Risk Measures; Stochastic Shortest Path; Optimal Stopping; Randomized Policy

Category 1: Other Topics (Dynamic Programming )

Category 2: Stochastic Programming


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Entry Submitted: 03/23/2012
Entry Accepted: 03/23/2012
Entry Last Modified: 03/24/2012

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