Generalized Chebychev inequalities: Theory and applications in decision analysis

Generalized Chebychev inequalities: Theory and applications in decision analysis

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Article ID: iaor19982346
Country: United States
Volume: 43
Issue: 5
Start Page Number: 807
End Page Number: 825
Publication Date: Sep 1995
Journal: Operations Research
Authors:
Keywords: probability, programming: linear
Abstract:

In many decision analysis problems, we have only limited information about the relevant probability distributions. In problems like these, it is natural to ask what conclusions can be drawn on the basis of this limited information. For example, in the early stages of analysis of a complex problem, we may have only limited fractile information for the distributions in the problem; what can we say about the optimal strategy or certainty equivalents given these few fractiles? This paper describes a very general framework for analyzing these kinds of problems where, given certain ‘moments’ of a distribution, we can compute bounds on the expected value of an arbitrary ‘objective’ function. By suitable choice of moment and objective functions we can formulate and solve many practical decision analysis problems. We describe the general framework and theoretical results, discuss computational strategies, and provide specific results for examples in dynamic programming, decision analysis with incomplete information, Bayesian statistics, and option pricing.

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