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Consensus-Based Linguistic Distribution Large-Scale Group Decision Making Using Statistical Inference and Regret

Feifei Jin1, Jinpei Liu1, Ligang Zhou2

  • 1School of Business, Anhui University, Hefei, 230601 Anhui China.

Group Decision and Negotiation
|May 10, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel consensus-based approach for large-scale group decision-making (LSGDM) that incorporates decision-makers' (DMs) regret aversion. The method uses statistical inference and regret theory to achieve consensus and rank alternatives effectively.

Keywords:
Consensus reaching processLarge-scale group decision makingLinguistic distribution informationRegret theoryStatistical inference principle

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Area of Science:

  • Operations Research
  • Decision Analysis
  • Behavioral Economics

Background:

  • Large-scale group decision-making (LSGDM) involves numerous decision-makers (DMs) and complex, uncertain environments.
  • Existing LSGDM methods often overlook the psychological behavior of DMs, specifically their aversion to regret.
  • Linguistic information is frequently used by DMs, requiring robust modeling techniques.

Purpose of the Study:

  • To develop a novel consensus-based linguistic distribution LSGDM (CLDLSGDM) approach.
  • To integrate decision-makers' regret aversion psychological characteristics into the LSGDM process using regret theory.
  • To achieve agreed-upon solutions in complex decision-making scenarios.

Main Methods:

  • Application of a statistical inference principle to consensual information derived during the consensus process.
  • Derivation of DM and attribute weights using consensus matrices and adjusted decision-making matrices.
  • Utilization of regret theory to calculate comprehensive perceived utility values and rank alternatives.

Main Results:

  • The CLDLSGDM approach successfully incorporates regret aversion into LSGDM.
  • DM and attribute weights are derived based on consensus and statistical inference.
  • A practical example of public hospital performance evaluation in China demonstrates the method's applicability.

Conclusions:

  • The proposed CLDLSGDM method provides a robust framework for LSGDM problems with regret-averse DMs.
  • The approach enhances decision-making by considering psychological factors and achieving consensus.
  • Sensitivity and comparative analyses confirm the method's stability and advantages.