Scheme selection decision-making method and device based on qualified value adaptive regret theory

Abstract

The invention provides a scheme selection decision-making method and device based on a qualified value adaptive regret theory, and the method comprises the steps: carrying out the matrix standardization of an original evaluation matrix, and obtaining a standardized evaluation matrix; taking intermediate values of all values under each attribute in the standardized evaluation matrix as qualified reference values of the attribute; obtaining a utility value of each attribute under each scheme according to the qualified reference value of each attribute, and forming an attribute interval utility value matrix; calculating a regret value of each scheme under each attribute, and obtaining a perception utility value matrix; obtaining a basic probability distribution matrix; obtaining a phase separation degree deviation matrix according to the standardized evaluation matrix, and obtaining the weight of each attribute; obtaining a weight-based basic probability distribution matrix; and performing evidence synthesis, and sorting all the schemes. Compared with the traditional regret theory, the method is higher in flexibility, and can better describe decision-making habits and risk chasing psychologies of people.

Description

technical field [0001] The invention relates to the field of scheme selection, in particular to a scheme selection decision-making method and device based on qualified value self-adaptive regret theory. Background technique [0002] Multi-attribute decision-making is a decision-making method that integrates evaluation information of multiple specified attributes to find the optimal solution. Due to the complexity and dynamics of objective things and the limited understanding of decision makers, attribute information often has fuzzy and uncertain characteristics, and it is difficult to describe it with an accurate value. Interval numbers, fuzzy numbers, connection numbers, Form table of gray number and so on. Among them, the interval number has become the most commonly used expression form of uncertain information because of its simple and intuitive characteristics. Especially for multiple different schemes with multiple attributes, how to choose an optimal scheme has becom...

AI Technical Summary

Problems solved by technology

First, most decision-making applications of regret theory are based on the basic model, and the ability to describe the psychological behavior of decision-making is not strong enough, especially when the risk aversion coefficient is determined, the curve of the utility function is determined, and the value range of the attribute value It has nothing to do with characteristics such as distribution and lacks flexibility

In many decision-making applications, there are qualified reference values ​​for attribute values. Below this value, the acceptance of decision-makers will decline rapidly, that is, they will show risk-seeking psychology, but the basic model of regret theory is difficult to describe this kind of behavioral characteristics.

Second, the application of many basic models is too deterministic, making it difficult to conduct stability testing and uncertainty analysis on decision-making results, and unable to reflect the possible impact of small changes in decision-maker preferences

Third, how to apply the attribute evaluation with regret-avoiding mentality to the evidence synthesis method based on the identification framework and basic probability distribution, so as to avoid the subjectivity of evidence, that is, the research on the combination of evidence theory and regret theory is still rare

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