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Group Decision Making with Heterogeneous Preference Structures: An Automatic Mechanism to Support Consensus Reaching

  • Bowen Zhang
  • Yucheng Dong
  • Enrique Herrera-Viedma
Article
  • 63 Downloads

Abstract

In real-world decision problems, decision makers usually express their opinions with different preference structures. In order to deal with the heterogeneous preference information in group decision making, this paper presents an optimization-based consensus model for group decision making with heterogeneous preference structures (utility values, preference orderings, multiplicative preference relations and additive preference relations). This proposal seeks to minimize the information loss between decision makers’ heterogeneous preference information and individual preference vectors and also seeks the collective solution with a consensus. Meanwhile, in order to justify the consensus model, we discuss its internal aggregation operator between the obtained individual and group preference vectors, demonstrate that the proposed model satisfies the Pareto principle of social choice theory, and prove the uniqueness of the solution to the optimization model. Furthermore, based on the proposed optimization-based consensus model, we present an automatic mechanism to support consensus reaching in the group decision making with heterogeneous preference structures. In the consensus reaching process, the obtained individual and group preference vectors are considered as a decision aid which decision makers can use as a reference to adjust their preference opinions. Finally, detailed simulation experiments and comparison analysis are conducted to demonstrate the feasibility and effectiveness of our proposed model.

Keywords

Group decision and negotiation Heterogeneous preference structures Consensus Information loss 

Notes

Acknowledgements

We would like to acknowledge the financial support of the grants (Nos. 71871149 and 71571124) from NSF of China, the grants (Nos. sksyl201705 and 2018hhs-58) from Sichuan University, and the grant TIN2016-75850-R supported by the Spanish Ministry of Economy and Competitiveness with FEDER funds.

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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Bowen Zhang
    • 1
  • Yucheng Dong
    • 2
  • Enrique Herrera-Viedma
    • 3
    • 4
  1. 1.School of Economics and ManagementXidian UniversityXi’anChina
  2. 2.Business SchoolSichuan UniversityChengduChina
  3. 3.Department of Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain
  4. 4.Department of Electrical and Computer EngineeringKing Abdulaziz UniversityJeddahSaudi Arabia

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