Choquet integral in decision analysis - lessons from the axiomatization
Mikhail Timonin

TL;DR
This paper provides an axiomatic analysis of the Choquet integral, explores its learning challenges, and discusses its applications in decision analysis and social choice, highlighting the importance of proper data scaling.
Contribution
It offers a unified axiomatic framework for the Choquet integral, critiques existing learning methods, and extends its application to state-dependent utility modeling.
Findings
Axiomatic characterization of special cases of the Choquet integral.
Analysis of the impact of data scaling assumptions on learning the model.
Insights into the interpretation of the Choquet integral in social choice and decision analysis.
Abstract
The Choquet integral is a powerful aggregation operator which lists many well-known models as its special cases. We look at these special cases and provide their axiomatic analysis. In cases where an axiomatization has been previously given in the literature, we connect the existing results with the framework that we have developed. Next we turn to the question of learning, which is especially important for the practical applications of the model. So far, learning of the Choquet integral has been mostly confined to the learning of the capacity. Such an approach requires making a powerful assumption that all dimensions (e.g. criteria) are evaluated on the same scale, which is rarely justified in practice. Too often categorical data is given arbitrary numerical labels (e.g. AHP), and numerical data is considered cardinally and ordinally commensurate, sometimes after a simple…
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Taxonomy
TopicsMulti-Criteria Decision Making · Game Theory and Voting Systems · Decision-Making and Behavioral Economics
MethodsMultimodal Fuzzy Fusion Framework
