PRIME - Preference Ratios in Multiattribute Evaluation

1. PRIME - Preference Ratios in Multiattribute Evaluation Ahti A. Salo and Raimo P. Hämäläinen Systems Analysis Laboratory Helsinki University of Technology http://www.sal.hut.fi/ 7.9.1999

2. Multiattribute weighting SOCIETAL BENEFIT Environment Health Economy Grant permit Deny permit

3. Weighting methods • Tradeoff method • has a sound theoretical foundation • requires continuous measurement scales • may be rather difficult in practice • Ratio-based methods • very popular despite weaker theoretical foundation • SMART (Edwards 1977) • SMARTER (Edwards and Barron 1994) • AHP (Saaty 1980) • How to combine the advantages of both? • cf. preference measurement in the AHP (Salo and Hämäläinen 1997)

4. Incomplete information • Complete information may be hard to acquire • alternatives and their impacts? • relative importance of attributes? • Examples • assessment of environmental impacts • cost of acquiring further information • partial stakeholder involvement • fluctuating preferences • What can be concluded on the basis of available information? • parametric uncertainties covered • structural uncertainties excluded

5. Ratio comparisons • Estimates should not depend on the value representation • Ratios of value differences • not actionable as choices between naturally occurring options • axiomatizations by Dyer and Sarin (1979) and Vansnick (1984) • Direct rating an analogue • positioning on the range [0,100]

6. Score elicitation • Estimates • Procedures • comparisons between pairs of adjacent levels • comparisons with regard to least preferred achievement level

7. Weight elicitation • Estimates • Choice of alternatives • interval SMARTS - least and most preferred alternatives on each attribute • reference alternatives - any two alternatives • Choice of attributes • reference attributes - largest value difference • attribute sequencing - (rank) order attributes and compare adjacent ones

8. Dominance structures • Absolute dominance • Pairwise dominance • Become increasingly conclusive

9. Decision criteria (1) • Max-max • Max-min • Minimax regret

10. Decision criteria (2) • Central values • Central weights • the same w.r.t. weights, assuming that scores are known • Provide guidance when decision rules do not hold • associated loss of value must be examined, however!

11. Elicitation processes (1)

12. Computational convergence • Questions • how effective are imprecise ratios? • which decision rules are best? • Randomly generated problems • 5,10,15 attributes; 5,10,15 alternatives • attribute weighting by interval SMART • error ratios 1.2, 1.5, 2 • 5000 problem instances

13. Results • Central values minimise the expected loss of value • Few imprecise ratios improve performance in relation to ordinal information • As the number and precision of imprecise ratios increases • the number of nondominated alternatives declines • the expected loss of value decreases

14. Genetically modified organisms • Technology assessment study for the Finnish Parliament • commissioned by the Futures Committee • delivered to the Speaker of the Parliament in September 1998 • debated in the plenary session in November 1998 • an extensive two-hour debate, commented on by two ministers • Precautionary Principle in Risk Management • commissioned by JRC/IPTS (ESTO network) • presented to the DG’s by the Forward Studies Unit in May 1999 • Problem characteristics • timely and highly controversial • large uncertainties • many concerns

15. Value tree

16. Ranges of weights

17. Intermediate results

18. Ranges of attribute weights

19. Decision rules

20. Conclusion • Characteristics • acknowledgement of uncertainties • maintenance of consistencies • alternative elicitation processes • guidance through decision rules • PRIME Decisions • full-fledged computer implementation • interactive decision support