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Comparative Decision Making$
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Thomas R. Zentall and Philip H. Crowley

Print publication date: 2013

Print ISBN-13: 9780199856800

Published to Oxford Scholarship Online: May 2013

DOI: 10.1093/acprof:oso/9780199856800.001.0001

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Computational Decision Support Regret-Based Models for Optimization and Preference Elicitation

Computational Decision Support Regret-Based Models for Optimization and Preference Elicitation

Chapter:
(p.423) Chapter 14 Computational Decision Support Regret-Based Models for Optimization and Preference Elicitation
Source:
Comparative Decision Making
Author(s):

Craig Boutilier

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199856800.003.0041

The goal of decision support is to develop methods that assist decision makers. In this chapter computational methods are brought to bear on a multi-dimensional choice problem with the two-part challenge of efficiently determining the decision-maker’s preferences and then finding the best choice. Computer-aided decision support is growing rapidly. This chapter introduces some technical methods of broad applicability in this area, including specification of utility functions with uncertainty, Markov decision processes, and robust optimization. The author exploits the notion of minimax regret, where the goal is to find an option that minimizes the maximum regret relative to all possible manifestations of an uncertain utility function. This approach is robust to incomplete information and facilitates the important process of preference elicitation from the client. There are opportunities for using such approaches to investigate human decision making based on incomplete information; the impact of cognitive costs, biases, and heuristics; and choices made by groups.

Keywords:   decision support, computational methods, Markov decision processes, robust optimization, minimax regret, preferences, incomplete information

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