R. Lahdelma and P. Salminen (Finland)
Knowledge representation, Decision support, Preference information, Multicriteria analysis
Preference information in real-life multi-criteria decision aiding (MCDA) problems is always more or less inaccurate, imprecise or uncertain. Sometimes preference information can be missing. We discuss methods for representing different kinds of incomplete preference information through probability distributions for preference parameters and show how to treat this information in MCDA methods through simulation techniques. The techniques are suitable for different kinds of decision models, such as utility/value function models, prospect theory, reference point methods, and outranking methods.
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