1. 1 S ystems Analysis Laboratory Helsinki University of Technology Master’s Thesis Antti Punkka “ Uses of Ordinal Preference Information in Interactive Decision Support ”

2. 2 S ystems Analysis Laboratory Helsinki University of Technology Research issues Most methods of hierarchical weighting –Require numerical information in preference elicitation –Do not solicit the DM’s preferences concerning the overall performance of the alternatives –Tend to oblige the DM to give preference statements in a form or about matters he/she does not feel confident with Incomplete ordinal information –Allows the DM to associate sets of ranks to sets of alternatives / attributes –Appears to be flexible, fast if necessary, robust, easy to give decisionmaker’s confidence in the method

3. 3 S ystems Analysis Laboratory Helsinki University of Technology Background Incomplete preference information –Arbel (1989) (LPs, dominance concepts) extends the AHP to incomplete preference statements –PAIRS, Preference Programming, PRIME (Salo and Hämäläinen 1992, 1995, 2001) hierarchical value trees, consistency, decision recommendations software (Winpre, PRIME Decisions), applications Rank Inclusion in Criteria Hierarchies (RICH) (Salo and Punkka 2002) –Incomplete ordinal information about the importance of attributes e.g., the two most important attributes are some of these three Compatible rankings, non-convex feasible regions, extreme points –Manuscript (submitted to EJOR in June 2002) –Software: RICH Decisions (Liesiö and Salo) –Applications evaluation of risk management tools (Ojanen 2002) Wood Wisdom II (Salo and Liesiö 2002)

4. 4 S ystems Analysis Laboratory Helsinki University of Technology Different forms of incomplete ordinal information (Incomplete) ordinal information about the importance of attributes (RICH) (Incomplete) ordinal information about the attribute-specific performances of the alternatives, score information in form of intervals (Incomplete) ordinal information about the overall performances of the alternatives, interpreted as pairwise dominance LPs for 1) maximum and minimum overall values of the alternatives and 2) pairwise dominance structures Constraints for the MCDM problem from the preference statements and initial conditions (interpretation of weights, scores) Decision recommendations and pairwise dominance structures

5. 5 S ystems Analysis Laboratory Helsinki University of Technology Prospects for further research Theory development –Computational evaluation –Procedural recommendations –Scenario-based pricing of investment opportunities Software implementations –Front-end tools –Server tools Case studies –Risk management strategies –Comparative analysis of research themes