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Ruonan Zhang1, Jing Huang1, Yejun Xu2

  • 1Business School, Hohai University, Nanjing, 211100 People's Republic of China.

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Summary
This summary is machine-generated.

This study introduces quadratic cost functions for group decision making (GDM) to better reflect expert opinion changes. This approach optimizes consensus-building by accounting for increasing resistance to greater compromises.

Keywords:
Aggregation operatorGroup decision makingMinimum cost consensus model

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Area of Science:

  • Decision Sciences
  • Operations Research
  • Artificial Intelligence

Background:

  • Group decision making (GDM) involves significant costs in achieving expert consensus.
  • Traditional linear cost functions in GDM assume fixed unit costs, which is unrealistic as experts resist larger opinion changes.

Purpose of the Study:

  • To introduce and analyze novel quadratic cost functions for GDM.
  • To develop minimum cost consensus models using aggregation operators like weighted average (WA) and ordered weighted average (OWA).

Main Methods:

  • Utilizing strictly convex quadratic programming to develop consensus models.
  • Analyzing models under WA and OWA aggregation operators.
  • Developing approaches that account for increasing marginal costs with opinion changes.

Main Results:

  • Quadratic cost functions provide a more realistic model for consensus costs in GDM.
  • The proposed models effectively minimize costs by considering the varying resistance of experts.
  • The validity of the models is demonstrated through examples and comparative analyses.

Conclusions:

  • Quadratic cost functions offer a more accurate representation of consensus costs in GDM.
  • The developed models and approaches enhance the efficiency and realism of consensus-building processes.
  • This research expands the applicability of consensus methods through advanced aggregation operators.