Abstract
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.
| Original language | English |
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| Title of host publication | AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications |
| Editors | V. Devedzic |
| Publisher | ACTA Press |
| Pages | 590-597 |
| Number of pages | 8 |
| ISBN (Print) | 9780889866317 |
| Publication status | Published - 2007 |
| MoE publication type | A4 Conference publication |
| Event | IASTED International Conference on Artificial Intelligence and Applications - Innsbruck, Austria Duration: 12 Feb 2007 → 14 Feb 2007 |
Conference
| Conference | IASTED International Conference on Artificial Intelligence and Applications |
|---|---|
| Country/Territory | Austria |
| City | Innsbruck |
| Period | 12/02/2007 → 14/02/2007 |
Keywords
- Decision support
- Knowledge representation
- Multicriteria analysis
- Preference information