JOURNAL ARTICLE

Reliability Evaluation of Binary Group Decision-Making Mechanism.

  • Published In: International Journal of Information Technology & Decision Making, 2025, v. 24, n. 5. P. 1329 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: LIU, Qiang; Peng, Xinyu; Liu, Qingmiao; Li, Qiao 3 of 3

Abstract

Decision-making is an important management activity. This study evaluates the reliability of group decision-making (GDM) and multi-attribute GDM (MAGDM) mechanisms for a class of 0–1 binary decision-making problem. We define the reliability of GDM and MAGDM, use the weighted voting system to model the GDM and MAGDM mechanisms, and propose two algorithms to evaluate the reliability of GDM and MAGDM considering the participation of general or professional decision makers. Additionally, the influence of some system parameters, such as the number of decision makers or attributes, cognitive accuracy of decision makers, and threshold of weighted majority voting rule, on the reliability of GDM and MAGDM was analyzed using random simulation experiments. The results of the random experiment show that: increasing the number of decision makers or attributes could improve the decision accuracy; the reduction in the individual subjective accuracy reduces the overall decision accuracy, which was difficult to compensate for by increasing the number of DMs; guiding DMs to reach consensus through group discussion decreased the decision accuracy of GDM and MAGDM. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Information Technology & Decision Making. 2025/07, Vol. 24, Issue 5, p1329
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2025
  • ISSN:0219-6220
  • DOI:10.1142/S021962202450007X
  • Accession Number:186914283
  • Copyright Statement:Copyright of International Journal of Information Technology & Decision Making is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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