Information Density in Decision Analysis

Gordon Hazen (), Emanuele Borgonovo () and Xuefei Lu ()
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Gordon Hazen: Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois 60208
Emanuele Borgonovo: Bocconi Institute for Data Science and Analytics, 20136 Milan, Italy; Department of Decision Sciences, Bocconi University, 20136 Milan, Italy
Xuefei Lu: SKEMA Business School, Université Côte d’Azur, Paris, France

Decision Analysis, 2023, vol. 20, issue 2, 89-108

Abstract: Information value has been proposed and used as a probabilistic sensitivity measure, the idea being that uncertain parameters having higher information value are precisely those to which an optimal decision is more sensitive. In this paper, we study the notion of information density as a graphical complement to information value analysis, one that augments an information value calculation with associated directions of information gain. We formally examine mathematical details absent from its earlier presentation that guarantee information density exists and is well posed and describe its relationship to alternate measures of information value. We present its application in the context of a realistic case study and discuss the associated insights.

Keywords: information value; sensitivity analysis; decision analysis theory (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ordeca:v:20:y:2023:i:2:p:89-108

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