1. Helsinki University of Technology Systems Analysis Laboratory 1DAS workshop7.9.1999 Ahti A. Salo and Raimo P. Hämäläinen Systems Analysis Laboratory Helsinki University of Technology http://www.sal.hut.fi/ PRIME - Preference Ratios in Multiattribute Evaluation

2. 2 Helsinki University of Technology Systems Analysis Laboratory SOCIETAL BENEFIT EnvironmentEnvironmentEconomyEconomyHealthHealth Grant permit Deny permit Multiattribute weighting

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

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

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

6. 6 Helsinki University of Technology Systems Analysis Laboratory Score elicitation n Estimates n Procedures –comparisons between pairs of adjacent levels –comparisons with regard to least preferred achievement level

7. 7 Helsinki University of Technology Systems Analysis Laboratory Weight elicitation n Estimates n Choice of alternatives Êinterval SMARTS - least and most preferred alternatives on each attribute Ëreference alternatives - any two alternatives n Choice of attributes –reference attributes - largest value difference –attribute sequencing - (rank) order attributes and compare adjacent ones

8. 8 Helsinki University of Technology Systems Analysis Laboratory Dominance structures n Absolute dominance n Pairwise dominance n Become increasingly conclusive

9. 9 Helsinki University of Technology Systems Analysis Laboratory Decision criteria (1) Ê Max-max Ì Max-min Ì Minimax regret

10. 1010 Helsinki University of Technology Systems Analysis Laboratory Decision criteria (2) Í Central values Î Central weights –the same w.r.t. weights, assuming that scores are known n Provide guidance when decision rules do not hold –associated loss of value must be examined, however!

11. 1 Helsinki University of Technology Systems Analysis Laboratory Elicitation processes (1)

12. 1212 Helsinki University of Technology Systems Analysis Laboratory Computational convergence n Questions –how effective are imprecise ratios? –which decision rules are best? n 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. 1313 Helsinki University of Technology Systems Analysis Laboratory Results Ê Central values minimise the expected loss of value Ë Few imprecise ratios improve performance in relation to ordinal information Ì As the number and precision of imprecise ratios increases –the number of nondominated alternatives declines –the expected loss of value decreases

14. 1414 Helsinki University of Technology Systems Analysis Laboratory Genetically modified organisms n 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 n Precautionary Principle in Risk Management –commissioned by JRC/IPTS (ESTO network) –presented to the DG’s by the Forward Studies Unit in May 1999 n Problem characteristics –timely and highly controversial –large uncertainties –many concerns

15. 1515 Helsinki University of Technology Systems Analysis Laboratory Value tree

16. 1616 Helsinki University of Technology Systems Analysis Laboratory Ranges of weights

17. 1717 Helsinki University of Technology Systems Analysis Laboratory Intermediate results

18. 1818 Helsinki University of Technology Systems Analysis Laboratory Ranges of attribute weights

19. 1919 Helsinki University of Technology Systems Analysis Laboratory Decision rules

20. 2020 Helsinki University of Technology Systems Analysis Laboratory Conclusion n Characteristics –acknowledgement of uncertainties –maintenance of consistencies –alternative elicitation processes –guidance through decision rules n PRIME Decisions –full-fledged computer implementation –interactive decision support