Multiple-Criteria Decision Analysis

Bibliography

With the growing interest in MCDA, there has been a significant increase in research and literature on the topic. Below, we provide a list of selected MCDA-related publications that cover a wide range of applications and methodologies. These publications are intended to serve as a resource for researchers and practitioners interested in the field of MCDA.

  1. Anand, A., & Dey, P. (2021). Distance restricted manipulation in voting. Theoretical Computer Science, 891, 149–165. https://doi.org/10.1016/j.tcs.2021.08.034
  2. Aziz, H., & Lam, A. (2021). Obvious Manipulability of Voting Rules (Vol. 13023, pp. 179–193). https://doi.org/10.1007/978-3-030-87756-9_12
  3. Bac, M. (2001). Corruption, Connections and Transparency: Does a Better Screen Imply a Better Scene? Public Choice, 107(1), 87–96. https://doi.org/10.1023/A:1010349907813
  4. Belton, V., & Gear, T. (1983). On a short-coming of Saaty’s method of analytic hierarchies. Omega, 11(3), 228–230. https://doi.org/10.1016/0305-0483(83)90047-6
  5. Belton, V., & Gear, T. (1985). The legitimacy of rank reversal—A comment. Omega, 13(3), 143–144.
  6. Bommer, M., Gratto, C., Gravander, J., & Tuttle, M. (1987). A behavioral model of ethical and unethical decision making. Journal of Business Ethics, 6(4), 265–280. https://doi.org/10.1007/BF00382936
  7. Bühren, C., Li, S., Frank, B., & Qin, H. (2015). Group Decision Making in a Corruption Experiment: China and Germany Compared. Jahrbücher Für Nationalökonomie Und Statistik, 235. https://doi.org/10.1515/jbnst-2015-0207
  8. Castellan, N. J., & American Psychological Association (Eds.). (1993). Individual and group decision making: Current issues. L. Erlbaum Associates.
  9. Council of Europe. (2013). Designing and Implementing Anti-corruption Policies.
  10. Dong, Q., & Saaty, T. L. (2014). An analytic hierarchy process model of group consensus. Journal of Systems Science and Systems Engineering, 23(3), 362–374. https://doi.org/10.1007/s11518-014-5247-8
  11. Dong, Y., Liu, Y., Liang, H., Chiclana, F., & Herrera-Viedma, E. (2018). Strategic weight manipulation in multiple attribute decision making. Omega, 75, 154–164. https://doi.org/10.1016/j.omega.2017.02.008
  12. Dong, Y., & Xu, J. (2016). Consensus Building in Group Decision Making. Springer Singapore. https://doi.org/10.1007/978-981-287-892-2
  13. Dong, Y., Zha, Q., Zhang, H., & Herrera, F. (2021a). Consensus Reaching and Strategic Manipulation in Group Decision Making With Trust Relationships. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(10), 6304–6318. https://doi.org/10.1109/TSMC.2019.2961752
  14. Dong, Y., Zha, Q., Zhang, H., & Herrera, F. (2021b). Consensus Reaching and Strategic Manipulation in Group Decision Making With Trust Relationships. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(10), 6304–6318. https://doi.org/10.1109/TSMC.2019.2961752
  15. Dong, Y., Zhang, H., & Herrera-Viedma, E. (2016). Integrating experts’ weights generated dynamically into the consensus reaching process and its applications in managing non-cooperative behaviors. Decision Support Systems, 84, 1–15. https://doi.org/10.1016/j.dss.2016.01.002
  16. Faliszewski, P., Hemaspaandra, E., & Hemaspaandra, L. A. (2009). How hard is bribery in elections? Journal of Artificial Intelligence Research, 35(1), 485–532.
  17. Faliszewski, P., Hemaspaandra, E., & Hemaspaandra, L. A. (2010). Using complexity to protect elections. Communications of the ACM, 53(11), 74–82. https://doi.org/10.1145/1839676.1839696
  18. Faliszewski, P., Hemaspaandra, E., Hemaspaandra, L. A., & Rothe, J. (2009). Llull and Copeland voting computationally resist bribery and constructive control. Journal of Artificial Intelligence Research, 35(1), 275–341.
  19. Faliszewski, P., Hemaspaandra, E., Hemaspaandra, L. A., & Rothe, J. (2011). The shield that never was: Societies with single-peaked preferences are more open to manipulation and control. Information and Computation, 209(2), 89–107. https://doi.org/10.1016/j.ic.2010.09.001
  20. Figueira, J. R., & Roy, B. (2009). A note on the paper, “Ranking irregularities when evaluating alternatives by using some ELECTRE methods”, by Wang and Triantaphyllou, Omega (2008). Omega, 37(3), 731–733. https://doi.org/10.1016/j.omega.2008.05.001
  21. Gärdenfors, P. (1976a). Manipulation of social choice functions. Journal of Economic Theory, 13(2), 217–228. https://doi.org/10.1016/0022-0531(76)90016-8
  22. Gärdenfors, P. (1976b). Manipulation of social choice functions. Journal of Economic Theory, 13(2), 217–228. https://doi.org/10.1016/0022-0531(76)90016-8
  23. Gibbard, A. (1973). Manipulation of Voting Schemes: A General Result. Econometrica, 41(4), 587–601. https://doi.org/10.2307/1914083
  24. Gibbard, A. (1977). Manipulation of Schemes that Mix Voting with Chance. Econometrica, 45(3), 665. https://doi.org/10.2307/1911681
  25. Goncalves, A., & Correia, A. (2017). Anti-Bribery Quantitative Model. An approach based on pair-wise information System. International Journal of Economics and Management Systems, 2, 46–56.
  26. Gong, Z., Xu, X., Zhang, H., Aytun Ozturk, U., Herrera-Viedma, E., & Xu, C. (2015). The consensus models with interval preference opinions and their economic interpretation. Omega, 55, 81–90. https://doi.org/10.1016/j.omega.2015.03.003
  27. Hoyt, P. D. (1997). The Political Manipulation of Group Composition: Engineering the Decision Context. Political Psychology, 18(4), 771–790. https://doi.org/10.1111/0162-895X.00078
  28. Keller, O., Hassidim, A., & Hazon, N. (2019). New Approximations for Coalitional Manipulation in Scoring Rules. Journal of Artificial Intelligence Research, 64, 109–145. https://doi.org/10.1613/jair.1.11335
  29. Kelly, J. S. (1993). Almost all social choice rules are highly manipulable, but a few aren’t. Social Choice and Welfare, 10(2), 161–175.
  30. Koczkodaj, W. W., Smarzewski, R., & Szybowski, J. (2020). On Orthogonal Projections on the Space of Consistent Pairwise Comparisons Matrices. Fundamenta Informaticae, 172(4), 379–397. https://doi.org/10.3233/FI-2020-1910
  31. Kułakowski, K., Mazurek, J., & Strada, M. (2021). On the similarity between ranking vectors in the pairwise comparison method. Journal of the Operational Research Society. https://www.tandfonline.com/doi/abs/10.1080/01605682.2021.1947754
  32. Lev, O., & Lewenberg, Y. (2019). “Reverse Gerrymandering”: Manipulation in Multi-Group Decision Making. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 2069–2076. https://doi.org/10.1609/aaai.v33i01.33012069
  33. Li, L., Qiu, L., Liu, X., Xu, Y., & Herrera-Viedma, E. (2022). An improved HK model-driven consensus reaching for group decision making under interval-valued fuzzy preference relations with self-confidence. Computers & Industrial Engineering, 108438. https://doi.org/10.1016/j.cie.2022.108438
  34. Liu, Y., Dong, Y., Liang, H., Chiclana, F., & Herrera-Viedma, E. (2019). Multiple Attribute Strategic Weight Manipulation With Minimum Cost in a Group Decision Making Context With Interval Attribute Weights Information. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49(10), 1981–1992. https://doi.org/10.1109/TSMC.2018.2874942
  35. Liu, Y., Zhang, H., Wu, Y., & Dong, Y. (2019). Ranking range based approach to MADM under incomplete context and its application in venture investment evaluation. Technological and Economic Development of Economy, 25(5), Article 5. https://doi.org/10.3846/tede.2019.10296
  36. Lootsma, F. A. (Ed.). (1999). Multi-Criteria Decision Analysis via Ratio and Difference Judgement (Vol. 29). Springer US. https://doi.org/10.1007/b102374
  37. Macintyre, I. D. A. (1993). Manipulation under majority decision-making when no majority suffers and preferences are strict. Theory and Decision, 35(2), 167–177. https://doi.org/10.1007/BF01074957
  38. Maoz, Z. (1990). Framing the National Interest: The Manipulation of Foreign Policy Decisions in Group Settings. World Politics, 43(1), 77–110. https://doi.org/10.2307/2010552
  39. Moulin, H. (2016). Handbook of Computational Social Choice (F. Brandt, V. Conitzer, U. Endriss, J. Lang, & A. D. Procaccia, Eds.; 1st edition). Cambridge University Press.
  40. Palomares, I., Martínez, L., & Herrera, F. (2014). A Consensus Model to Detect and Manage Noncooperative Behaviors in Large-Scale Group Decision Making. IEEE Transactions on Fuzzy Systems, 22(3), 516–530. https://doi.org/10.1109/TFUZZ.2013.2262769
  41. Pelta, D. A., & Yager, R. R. (2010). Decision strategies in mediated multiagent negotiations: An optimization approach. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, 40(3), 635–640. https://doi.org/10.1109/TSMCA.2009.2036932
  42. Rabl, T. (2011). The Impact of Situational Influences on Corruption in Organizations. Journal of Business Ethics, 100(1), 85–101. https://doi.org/10.1007/s10551-011-0768-2
  43. Reyhani, R. (n.d.). Strategic Manipulation in Voting Systems. 160.
  44. Roland, J., De Smet, Y., & Verly, C. (2012). Rank Reversal as a Source of Uncertainty and Manipulation in the PROMETHEE II Ranking: A First Investigation. In S. Greco, B. Bouchon-Meunier, G. Coletti, M. Fedrizzi, B. Matarazzo, & R. R. Yager (Eds.), Advances in Computational Intelligence (pp. 338–346). Springer. https://doi.org/10.1007/978-3-642-31724-8_35
  45. Rzążewski, K., Słomczyński, W., & Zyczkowski, K. (2014). Każdy głos się liczy! Wędrówka przez krainę wyborów. https://www.semanticscholar.org/paper/Ka%C5%BCdy-g%C5%82os-si%C4%99-liczy!-%3A-w%C4%99dr%C3%B3wka-przez-krain%C4%99-Rz%C4%85%C5%BCewski-S%C5%82omczy%C5%84ski/4e3708c481b6aba4028e5d140490c05f11c61431
  46. Saaty, T. L., & Vargas, L. G. (1984). The legitimacy of rank reversal. Omega, 12(5), 513–516. https://doi.org/10.1016/0305-0483(84)90052-5
  47. Sasaki, Y. (2023). Strategic manipulation in group decisions with pairwise comparisons: A game theoretical perspective. European Journal of Operational Research, 304(3), 1133–1139. https://doi.org/10.1016/j.ejor.2022.05.015
  48. Schoner, B., Wedley, W. C., & Choo, E. U. (1993). A unified approach to AHP with linking pins. European Journal of Operational Research, 64(3), 384–392. https://doi.org/10.1016/0377-2217(93)90128-A
  49. Smith, D. A. (1999). Manipulability measures of common social choice functions. Social Choice and Welfare, 16(4), 639–661. https://doi.org/10.1007/s003550050166
  50. Sun, B., & Ma, W. (2015). An approach to consensus measurement of linguistic preference relations in multi-attribute group decision making and application. Omega, 51, 83–92. https://doi.org/10.1016/j.omega.2014.09.006
  51. Sun, Q., Wu, J., Chiclana, F., Wang, S., Herrera-Viedma, E., & Yager, R. R. (2022). An approach to prevent weight manipulation by minimum adjustment and maximum entropy method in social network group decision making. Artificial Intelligence Review. https://doi.org/10.1007/s10462-022-10361-8
  52. Taylor, A. D. (2005, May). Social Choice and the Mathematics of Manipulation. Cambridge Core; Cambridge University Press. https://doi.org/10.1017/CBO9780511614316
  53. Tian, Z.-P., Nie, R.-X., Wang, J.-Q., & Long, R.-Y. (2021). Adaptive Consensus-Based Model for Heterogeneous Large-Scale Group Decision-Making: Detecting and Managing Noncooperative Behaviors. IEEE Transactions on Fuzzy Systems, 29(8), 2209–2223. https://doi.org/10.1109/TFUZZ.2020.2995229
  54. Tosunoğlu, B., & Yazan, Ö. (2011). An Alternative Approach For Accounting Evaluating Accounting Manipulation Methods with AHP. 7th International Conference on Business, Management and Economics ICBME 2011. https://www.academia.edu/28735225/AN_ALTERNATIVE_APPROACH_FOR_ACCOUNTING_EVALUATING_ACCOUNTING_MANIPULATION_METHODS_WITH_AHP
  55. Wang, X., & Triantaphyllou, E. (2008). Ranking irregularities when evaluating alternatives by using some ELECTRE methods. Omega, 36(1), 45–63. https://doi.org/10.1016/j.omega.2005.12.003
  56. Wu, J., Cao, M., Chiclana, F., Dong, Y., & Herrera-Viedma, E. (2021). An Optimal Feedback Model to Prevent Manipulation Behavior in Consensus Under Social Network Group Decision Making. IEEE Transactions on Fuzzy Systems, 29(7), 1750–1763. https://doi.org/10.1109/TFUZZ.2020.2985331
  57. Xu, W., Chen, X., Dong, Y., & Chiclana, F. (2021). Impact of Decision Rules and Non-cooperative Behaviors on Minimum Consensus Cost in Group Decision Making. Group Decision and Negotiation, 30(6), 1239–1260. https://doi.org/10.1007/s10726-020-09653-7
  58. Yager, R. R. (2001). Penalizing strategic preference manipulation in multi-agent decision making. IEEE Transactions on Fuzzy Systems, 9(3), 393–403. https://doi.org/10.1109/91.928736
  59. Yager, R. R. (2002). Defending against strategic manipulation in uninorm-based multi-agent decision making. European Journal of Operational Research, 141(1), 217–232. https://doi.org/10.1016/S0377-2217(01)00267-3
  60. Zhang, G., Dong, Y., Xu, Y., & Li, H. (2011). Minimum-Cost Consensus Models Under Aggregation Operators. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 41(6), 1253–1261. https://doi.org/10.1109/TSMCA.2011.2113336
  61. Zuckerman, M., Procaccia, A. D., & Rosenschein, J. S. (2009). Algorithms for the coalitional manipulation problem. Artificial Intelligence, 173(2), 392–412. https://doi.org/10.1016/j.artint.2008.11.005