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Decision Making with Ordinal Payoffs Under Dempster–Shafer Type Uncertainty
Author(s) -
Yager Ronald R.,
Alajlan Naif
Publication year - 2013
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.21615
Subject(s) - dempster–shafer theory , ordinal scale , computer science , ordinal data , focus (optics) , artificial intelligence , scale (ratio) , mathematical economics , type (biology) , ordinal optimization , ordinal regression , operations research , machine learning , mathematics , statistics , ecology , physics , quantum mechanics , optics , biology
Our focus is on decision making in uncertain environments. We first introduce the Dempster–Shafer framework to model the uncertainty associated with possible outcomes. We then describe an approach for decision making when our uncertainty is captured using the Dempster–Shafer model and where the payoffs are numeric values. An important part of this approach is the role of the decision attitude as well as the aggregation of the possible payoffs. We then look at the situation where the payoffs, rather than being numbers, are values drawn from an ordinal scale. This requires us to provide appropriate operations for combining payoffs drawn from an ordinal scale.