Abstract
A scientific, reasonable, and novel talent evaluation index system is the foundation of talent training and selection. Based on the novel “Man-Machine-Environment System Engineering” (hereinafter referred to as MMESE) theory, this paper proposes a novel talent evaluation index system that considers the ontological attributes and the external environment of the object comprehensively for talent evaluation, which could help the evaluator obtain more accurate evaluation results. Since the comprehensive evaluation of MMESE talents is a complex decision-making problem that is both qualitative and quantitative, a corresponding decision-making method that integrates qualitative and quantitative approaches is proposed here based on probabilistic language entropy and the possibility of superior order relationships. First, the weights of quantitative and qualitative attributes are calculated based on entropy theory and probabilistic fuzzy language. Second, the standard weight vectors of qualitative and quantitative attributes are obtained by adjusting the weight integration coefficients, and the change intervals of the pros and cons between the objects to be evaluated are calculated. Third, the pros and cons of the objects to be evaluated are compared to obtain the possibility degree matrix that describes the priority relationships among the objects, and a ranking vector is derived from the possibility degree matrix to reflect the rankings of the objects’ pros and cons. Finally, this system and the decision-making methods have been verified as scientific and effective.
Highlights
The MMESE Talent Evaluation Index System3. Relevant Theories of Qualitative Index Weight Calculation Based on Probabilistic Language e comprehensive evaluation of a talent growth factor index system is a highly complex and uncertain decisionmaking problem, and its quantitative calculation is extremely difficult, making a reasonable and simplified model necessary to solve such a problem
A scientific, reasonable, and novel talent evaluation index system is the foundation of talent training and selection
Based on the novel “Man-Machine-Environment System Engineering” theory, this paper proposes a novel talent evaluation index system that considers the ontological attributes and the external environment of the object comprehensively for talent evaluation, which could help the evaluator obtain more accurate evaluation results
Summary
3. Relevant Theories of Qualitative Index Weight Calculation Based on Probabilistic Language e comprehensive evaluation of a talent growth factor index system is a highly complex and uncertain decisionmaking problem, and its quantitative calculation is extremely difficult, making a reasonable and simplified model necessary to solve such a problem. In actual decision-making process, such language evaluations are summarized into a set of probability language terms, and because expert knowledge is limited, attribute weights are often inaccurate or even completely. Is paper proposes a comprehensive and integrated decision-making method based on probabilistic hesitation language entropy, crossentropy, and the possibility of priority relationship, and this method could provide qualitative and quantitative solutions to complex decision-making problems in talent evaluation. A1, A2, A3, A4 are compared in pairs to get four possibility matrices (Ps) that describe their mutual priority relationships: A1 A2 A3 A4
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