DEMATEL-ISM model dynamic threshold calculation method based on centrality feature

By introducing factor centrality ranking and Spearman correlation coefficient, the threshold of the DEMATEL-ISM model is dynamically optimized, which solves the problems of subjectivity in threshold determination and poor noise resistance, and achieves more scientific and accurate causal relationship analysis.

CN122153229APending Publication Date: 2026-06-05WUHAN UNIV OF SCI & TECH
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Patent Information

Application Number
CN202610279655.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-09
Publication Date
2026-06-05

AI Technical Summary

Technical Problem

The threshold determination in the existing DEMATEL-ISM model relies on a single empirical formula, which is highly subjective, yields simplistic results, is prone to losing key factor relationships, has poor noise resistance, and cannot adapt to the factor characteristics of complex systems.

Method used

We introduce factor centrality ranking, dynamically optimize threshold selection by quantifying the similarity between node degree ranking and centrality ranking, and use Spearman correlation coefficient to select the optimal threshold to construct a reachability matrix.

Benefits of technology

It improves the scientific validity and robustness of the threshold, enhances the model's noise resistance, fully preserves the interaction relationships between key factors, and improves the accuracy and applicability of the analysis results.

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Abstract

The invention discloses a DEMATEL-ISM model dynamic threshold calculation method based on a centrality characteristic. According to the method, for the subjectivity problem of threshold determination during reachable matrix construction of a traditional DEMATEL-ISM model, factor centrality sorting distribution characteristics are introduced, and a centrality-influence intensity correlation model is established to realize threshold dynamic optimization; the method comprises the following steps: determining an initial threshold through an empirical formula, expanding the range of candidate thresholds, calculating reachable matrixes corresponding to the candidate thresholds and a factor node degree sequence, introducing a Spearman correlation coefficient to quantify the similarity between the node degree sequence and a centrality sequence, and finally screening out an optimal threshold. Compared with the prior art, the method gets rid of the dependence on a single empirical formula, improves the scientificity and objectivity of threshold determination, enhances the robustness of the threshold and the anti-noise capability of the model, effectively retains the interaction relationship among key factors, enables a reachable matrix to be more fit with an actual causal relationship network, and improves the robustness of the threshold. The method is suitable for scenes of complex system factor analysis, risk assessment, hierarchical structure division and the like.
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