Technical Note—Bayesian Decision Making with Ordinal Information

Johnnie R. Charnetski
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Johnnie R. Charnetski: Louisiana Tech University, Ruston, Louisiana

Operations Research, 1977, vol. 25, issue 5, 889-892

Abstract: Conventional Bayesian decision-making models require decision makers to stipulate numerically, either directly or indirectly, a specific set of conditional densities (posterior distributions) on the state/ signal set. The method presented permits decision makers to use a Bayesian approach when only ordinal information, in the form of partial orders (possibly incomplete), exists concerning the likelihood of individual and/or joint state/signal occurrence. From the partial orders provided, the mean values of the reward functions may be statistically approximated and the choice of a best action selected. We present a variation of a well-known Bayesian decision problem.

Date: 1977
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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:25:y:1977:i:5:p:889-892

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