A sensitivity analysis procedure for Bayesian decision making

Faizul Huq, Clarence “Red” Martin, Ken Cutright and Trevor S. Hale
Additional contact information
Faizul Huq: Groupe ESC Pau
Clarence “Red” Martin: Ohio University
Ken Cutright: Ohio University
Trevor S. Hale: University of Houston

No 1103, Working Papers from Groupe ESC Pau, Research Department

Abstract: In an effort to see how analytical model outputs change with respect to variations in model inputs, sensitivity analysis procedures have been widely used in applications such as mathematical programming and classical optimization. However, until recently, sensitivity analysis has seen only limited application in the area of decision theory and support. This paper investigates the use of sensitivity analysis in the realm of classical Bayesian reasoning, where the probabilities of the states of nature are revised based on additional information. These updated probabilities only become useful, however, if they lead to an optimal decision different from that obtained on the basis of prior probabilities. This paper develops a novel sensitivity analysis procedure for Bayesian decision making and proposes a set of criteria for the ranges of the model input parameters over which the current solution will remain optimal.

Keywords: Decision support; sensitivity analysis; Bayesian decision making. (search for similar items in EconPapers)
Pages: 15 pages
Date: 2009-01, Revised 2009-01
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