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April 1, 2026Informatica0 citationsOpen Access

Regret Theory-Driven Method for Addressing Multi-Attribute Decision-Making under Probabilistic Double Hierarchy Linguistic Term Set and Application to Information System Investment Project Selection

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FLFAN LeiHXHaiwen XuYHYaxing He

Key Points

  • This research aims to develop a decision-making method that incorporates regret theory for selecting information system investment projects.
  • Integrated regret theory with TODIM methods for a multi-attribute decision-making framework.
  • Developed a correlation coefficient and standard deviation integral method for objective weight calculations.
  • Applied the method within a probabilistic double hierarchy linguistic environment.
  • Illustrated effectiveness through numerical examples and stability analysis.
  • Demonstrated improved decision outcomes in investment project selection.
  • Confirmed method stability and efficiency through sensitivity analysis.
  • Showed clear benefits over existing decision-making methods.

Abstract

Taking into account the irrational elements and regret aversion of decision makers (DMs) during the decision-making process, regret theory (RT) and the TODIM methods have been integrated into a decision-making framework to develop an enhanced multi-attribute decision-making (MADM) method (PDHL-RT-TODIM) within probabilistic double hierarchy linguistic (PDHL) environment. Specifically, extending the perceived utility function in RT to determine the regret and joy values of the overall advantage flow of alternatives calculated by TODIM method in PDHL environment. Then, a correlation coefficient (CC) and standard deviation (SD) integral (CCSD) method was created using the probabilistic double hierarchy linguistic set (PDHLTS) distance metric and PDHL weight arithmetic operator to establish the objective weights of attributes. Additionally, the effectiveness of this proposed method was illustrated through numerical examples for information system investment project selection, and its stability, efficiency, and benefits were further confirmed through sensitivity analysis and comparisons with existing methods.

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Cite This Study

Lei et al. (2026) studied this question.

synapsesocial.com/papers/69ccb68116edfba7beb881ddhttps://doi.org/10.15388/26-infor624
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