- what if slightly different weigths were applied?
incomplete ordinal information:
r (3,1, 2)
Different weighting would
likely yield a better ranking
20 % interval
global sensitivity analysis
30 % interval
Rank Inclusion in Criteria Hierarchies (RICH)
RICHER = RICH with Extended Rankings
origins of procedural and behavioral biases
Number of attribute
levels effect in
weighting leads to
Averages over a
group yield even
Rank reversal in
web-sites and selected publications
A. Salo and A. Punkka: Ranking intervals and dominance relations for Ratio-based Efficiency Analysis, manuscript, 2010
A. Punkka and A. Salo: Preference Programming with incomplete ordinal information, manuscript, 2010
A. Salo and R. P. Hmlinen: Preference Programming - multicriteria weighting models under incomplete information,
in: Zopounidis and Pardalos (eds.): Handbook of Multicriteria Decision Analysis, Springer, New York, 2010
J. Liesi, P. Mild and A. Salo: Preference programming for robust multi-criteria portfolio modeling and project selection,
Eur. J. Oper. Res. (EJOR), 2007
J. Mustajoki, R. P. Hmlinen and M. R. K. Lindstedt: Using intervals for global sensitivity and worst case analyses in multiattribute value trees, EJOR, 2006
A. Salo and A. Punkka: Rank inclusion in criteria hierarchies, EJOR, 2005
J. Mustajoki, R. P. Hmlinen and A. Salo: Decision Support by Interval SMART/SWING - Incorporating Imprecision in the SMART and SWING Methods,
Decision Sciences, 2005
A. Salo and R. P. Hmlinen: Preference ratios in multiattribute evaluation (PRIME), IEEE Syst. Man Cybernetics, 2001
R. P. Hmlinen and J. Mntysaari: A dynamic interval goal programming approach to the regulation of a lake-river system, J. Multi-Crit. Dec. Anal., 2001
A. Salo and R. P. Hmlinen: Preference programming through approximate ratio comparisons, EJOR, 1995
A. Salo and R. P. Hmlinen: Preference assessment by imprecise ratio statements, Operations Research, 1992
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