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IEEE SMC 2018 Paper Presentation (Miyazaki, Japan. October 2018)

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Dual Consensus Measure for Multi-Perspective Multi-Criteria Group Decision Making

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IEEE SMC 2018 Paper Presentation (Miyazaki, Japan. October 2018)

  1. 1. Dual Consensus Measure for Multi-Perspective Multi- Criteria Group Decision Making Ivan Palomares Carrascosa Lecturer (Assistant Professor) in Data Science and AI Decision Support and Recommender Systems (DSRS) Research Group University of Bristol, United Kingdom E-mail: i.palomares@bristol.ac.uk Twitter: @ivan_uob
  2. 2. CONTENTS •DECISION-MAKING FRAMEWORK •MOTIVATION •DUAL CONSENSUS MEASURE •APPLICATION EXAMPLE •CONCLUDING REMARKS
  3. 3. DECISION-MAKING FRAMEWORK •MCGDM • Alternatives: ! = #$, #&, … , #( , ) ≥ 2, • Participants (experts): , = -$, -&, … , -. , / ≥ 2 • Criteria: 0 = 1$, 1&, … , 12 , 3 ≥ 2 • Criteria have associated importance weights • Individual preferences à decision matrices Location Price Condition Apt 1 0.8 0.5 0.2 Apt 2 0.3 0.7 0.5 Apt 3 0.55 0.25 0.7 Apt 4 0.5 0.5 0.6 DECISION MATRIX IN [0,1] INTERVAL
  4. 4. MOTIVATION • Most MCGDM problems assume a common setting of the relative importance of criteria for the whole group, BUT… • In many real problems, different participants have different perspectives about the relative importance of such criteria.
  5. 5. MOTIVATION • Consensus building processes • Introduced in GDM problems and their extensions to find highly accepted solutions • Consensus measures • Currently not suitable to measure agreement level in multi-perspective decision groups
  6. 6. DUAL CONSENSUS MEASURE • Captures level of agreement among participants on: 1. Global satisfaction on each alternative 2. Partial satisfaction on each alternative under each criterion 3. Similarity between perspectives of participants (weighted given to criteria)
  7. 7. DUAL CONSENSUS MEASURE 1. Global satisfaction on each alternative • Distance based on global alternative performance • Calculated for each alternative and pair of experts W1 = [.4 .2 .4]T W2 = [.2 .4 .4]T P1(x1) = 0·0.4+0.75·0.2+1·0.4 = 0.55 P2(x1) = 0.75·0.2+1·0.4+0·0.4 = 0.55 dG(p1, p12) = |0.55 − 0.55| = 0
  8. 8. DUAL CONSENSUS MEASURE 2. Partial satisfactions on each alternative • Distance based on alternative performances per criterion, and individual perspectives on criteria weights • Calculated for each alternative and pair of experts W1 = [.4 .2 .4]T W2 = [.2 .4 .4]T dP (p1 , p12 ) = (0.794 + 0.330 + 1)/3 = 0.708
  9. 9. DUAL CONSENSUS MEASURE 3. Putting it all together • Consensus degree between two experts <i, i’> on an alternative xj a = 1 à only global performance is taken into account a = 0.5 à global and partial performances are equally considered
  10. 10. DUAL CONSENSUS MEASURE 3. Putting it all together
  11. 11. APPLICATION EXAMPLE – LOGISTICS SECURITY •Hazardous material transportation • 4 candidate routes (alternatives), 3 criteria (efficiency, population density, road condition), 6 experts on secure logistics (i) increasing the relative importance of dG wrt dP (by increasing α), the dual consensus measure behaves more optimistically (ii) Considering distances between partial performances of alternatives à stronger sensitivity towards disagreement positions (iii) the highest (resp. lowest) variability in the consensus degrees as α increases are observed for x1 (resp. x3).
  12. 12. CONCLUDING REMARKS • First characterization of consensus measure for multi-perspective MCGDM • Individual perspectives on the relative importance of criteria are integrated in the measure of agreement among preferences • FUTURE WORK: • Generalize to MCGDM frameworks with different preference formats • define a complete consensus model based on proposed measure • Large-group decision making application (diversity, non-cooperative behaviors…)
  13. 13. COMING NOVEMBER 2018! GET YOUR COPY HERE
  14. 14. VISIT OUR DECISION SUPPORT AND RECOMMENDER SYSTEMS WEBSITE: https://dsrs.blogs.bristol.ac.uk

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