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From Incomplete Information to Strict Rankings: Methods to Exploit Probabilistic Preference Information

In: Dynamic Perspectives on Managerial Decision Making

Author

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  • Rudolf Vetschera

    (University of Vienna)

Abstract

Decision makers are often not able to provide precise preference information, which is required to solve a multicriteria decision problem. Thus, many methods of decision making under incomplete information have been developed to ease the cognitive burden on decision makers in providing preference information. One popular class of such methods, exemplified by the SMAA family of methods, uses a volume-based approach in parameter space and generates probabilistic statements about relations between alternatives. In the present paper, we study methods to transform this probabilistic information into a strict preference relation among alternatives, as such strict preferences are needed to actually make a decision. We compare these methods in a computational study, which indicates a trade-off between accuracy and robustness.

Suggested Citation

  • Rudolf Vetschera, 2016. "From Incomplete Information to Strict Rankings: Methods to Exploit Probabilistic Preference Information," Dynamic Modeling and Econometrics in Economics and Finance, in: Herbert Dawid & Karl F. Doerner & Gustav Feichtinger & Peter M. Kort & Andrea Seidl (ed.), Dynamic Perspectives on Managerial Decision Making, pages 379-394, Springer.
    • Handle: RePEc:spr:dymchp:978-3-319-39120-5_21
    DOI: 10.1007/978-3-319-39120-5_21
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