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

Rudolf Vetschera ()
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Rudolf Vetschera: University of Vienna

A chapter in Dynamic Perspectives on Managerial Decision Making, 2016, pp 379-394 from Springer

Abstract: 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.

Keywords: Stochastic Multiobjective Acceptability Analysis (SMAA); Rank Acceptability Indices; Anomaly Information; Admissible Parameter Vectors; Partial Utility Values (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:spr:dymchp:978-3-319-39120-5_21

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DOI: 10.1007/978-3-319-39120-5_21

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