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A Consensus Model to Detect and Manage Noncooperative Behaviors in Large-Scale Group Decision Making

Palomares, Iván, Martínez, Luis, Herrera, Francisco·2014·IEEE Transactions on Fuzzy Systems

Abstract

Consensus reaching processes in group decision making attempt to reach a mutual agreement among a group of decision makers before making a common decision. Different consensus models have been proposed by different authors in the literature to facilitate consensus reaching processes. Classical models focus on solving group decision making problems where few decision makers participate. However, nowadays, societal and technological trends that demand the management of larger scales of decision makers, such as e-democracy and social networks, add a new requirement to the solution of consensus-based group decision making problems. Dealing with such large groups implies the need for mechanisms to detect decision makers' noncooperative behaviors in consensus, which might bias the consensus reaching process. This paper presents a consensus model suitable to manage large scales of decision makers, which incorporates a fuzzy clustering-based scheme to detect and manage individual and subgroup noncooperative behaviors. The model is complemented with a visual analysis tool of the overall consensus reaching process based on self-organizing maps, which facilitates the monitoring of the process performance across the time. The consensus model presented is aimed to the solution of consensus processes involving large groups.

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Research works printed but not linked: 45

  1. klir, 1995, Fuzzy Sets and Fuzzy Logic Theory and Applications
  2. macqueen, 1967, Some methods for classification and analysis of multivariate observations, Proc 5th Berkeley Symp on Math Stat and Prob, 281
  3. 10.1016/j.asoc.2012.08.009 · DOI · Paper page
  4. 10.1016/j.ins.2010.08.005 · DOI · Paper page
  5. beliakov, 2007, Aggregation Functions A Guide for Practitioners
  6. 10.1002/(SICI)1098-111X(199901)14:1<63::AID-INT5>3.0.CO;2-S · doi:10.1002/(sici)1098-111x(199901)14:1<63::aid-int5>3.0.co;2-s
  7. 10.1002/9780470140529 · DOI · Paper page
  8. 10.1002/0471708607 · doi:10.1002/0471708607
  9. 10.1016/j.eswa.2012.07.029 · doi:10.1016/j.eswa.2012.07.029
  10. 10.1016/j.ins.2010.08.002 · doi:10.1016/j.ins.2010.08.002
  11. 10.1109/TFUZZ.2009.2013457 · DOI · Paper page
  12. 10.1109/3477.809032 · DOI · Paper page
  13. 10.1016/j.ins.2009.11.046 · doi:10.1016/j.ins.2009.11.046
  14. 10.1016/j.ejor.2008.10.034 · doi:10.1016/j.ejor.2008.10.034
  15. 10.1007/s10726-007-9075-9 · DOI
  16. 10.1371/journal.pone.0032566 · DOI
  17. yager, 2008, Intelligent social network analysis using granular computing, Int J Intell Syst, 23, 1197, 10.1002/int.20314 · doi:10.1002/int.20314
  18. 10.1109/91.928736 · doi:10.1109/91.928736
  19. kohonen, 1995, Self-Organizing Maps, 10.1007/978-3-642-97610-0 · doi:10.1007/978-3-642-97610-0 · Paper page
  20. 10.1016/S1088-467X(99)00013-X · doi:10.1016/s1088-467x(99)00013-x
  21. 10.1016/0020-0255(95)00025-K · DOI
  22. 10.1016/0165-0114(95)00107-7 · DOI
  23. tocqueville, 1840, Democracy in America (2nd Edition)
  24. 10.1016/0377-2217(88)90152-X · doi:10.1016/0377-2217(88)90152-x
  25. 10.1142/S1793005707000720 · DOI
  26. saint, 1994, Rules for Reaching Consensus A Modern Approach to Decision Making
  27. 10.1007/s00500-009-0475-4 · DOI
  28. 10.1109/TFUZZ.2005.856561 · DOI · Paper page
  29. 10.1109/TSMCA.2002.802821 · DOI · Paper page
  30. butler, 2006, On Conflict and Consensus A Handbook on Formal Consensus Decisionmaking
  31. 10.1016/0165-0114(92)90107-F · DOI · Paper page
  32. 10.1016/0165-0114(86)90014-X · doi:10.1016/0165-0114(86)90014-x
  33. 10.1109/TFUZZ.2011.2170076 · DOI · Paper page
  34. vesanto, 2000, Self-organizing map in Matlab: The SOM toolbox, Proc Matlab DSP Conf, 35
  35. 10.1109/TFUZZ.2011.2173581 · doi:10.1109/tfuzz.2011.2173581
  36. 10.1109/TFUZZ.2011.2172795 · doi:10.1109/tfuzz.2011.2172795
  37. 10.1016/0165-0114(78)90001-5 · DOI
  38. 10.1016/0167-8655(95)00119-0 · DOI
  39. tanino, 1988, Non-conventional Preference Relations in Decision Making, 54, 10.1007/978-3-642-51711-2_4 · doi:10.1007/978-3-642-51711-2_4
  40. bezdek, 2007, Pattern Recognition with Fuzzy Objective Function Algorithms
  41. 10.1016/0305-0548(96)00002-H · doi:10.1016/0305-0548(96)00002-h
  42. 10.1109/72.846731 · DOI · Paper page
  43. 2011, Consensual Processes Studies in Fuzziness and Soft Computing, 267
  44. 10.1109/97.329844 · DOI
  45. 10.1016/S0165-0114(97)00087-0 · doi:10.1016/s0165-0114(97)00087-0

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Metadata

Authors
Palomares, I., Martínez, L., & Herrera, F.
Venue
IEEE Transactions on Fuzzy Systems
Published
2014-06
Volume
22
Issue
3
Pages
516-530
ISSN
  • 1063-6706 (ISSN-L)
  • 1941-0034
OpenAlex
W2042508843
Cited by
553
References
0
FWCI
21.76
Type
journal article
Language
en
Open access
closed
Funders
Research Project TIN-2009-08286 and European Regional Development Fund

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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. https://doi.org/10.1109/tfuzz.2013.2262769

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