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Context-dependent probabilistic linguistic multi-attribute decision-making methods
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AI
DOI：10.1007/s10489-024-06059-9.png)
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Abstract
En 中文
In the field of decision-making, the accurate assessment and integration of multiple attributes, particularly in scenarios characterized by uncertainty and subjectivity, pose a substantial challenge. Traditional decision-making methods within the probabilistic linguistic framework typically treat these as a series of independent single-attribute evaluations, thereby neglecting the crucial contextual information present within the attribute space. This paper introduces a context-dependent multi-attribute decision-making method, specifically designed for environments characterized by uncertainty and linguistic ambiguity. Our primary aim is to establish a decision-making framework that not only recognizes but also effectively utilizes the interdependencies and contextual subtleties among various attributes. To facilitate easier quantification of uncertainty in practical data, we initially define the Gaussian probabilistic linguistic term set and its corresponding generation algorithm. We then establish matrices that elucidate the dominant and dominated relationships between options across different attribute sets. These matrices are then incorporated into prospect theory, providing a comprehensive approach to multi-attribute decision-making. The effectiveness of our proposed method is demonstrated through a case study focusing on investment decision-making for countries participating in the Belt and Road Initiative.
Keywords:
Probabilistic Linguistic Term Set
Multi-attribute decision-making
Context information
Prospect theory
Journal
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3.5
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7.5K
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1.7W
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