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Purpose: The purpose of this research is to compare the levels of EI of male and female students, EI among students of three different academic faculties: business, engineering and nursing and the degree of change in their EI over the course of study. Additionally, the authors set out to isolate and quantify the effects of gender and field of study, independent of each other. Design/methodology/approach: This empirical research is based on a survey of >750 undergraduate college students, in which participants answered a host of Likert-scale questions concerning perceptions of risk, self-efficacy, career path and entrepreneurial intent (EI). The survey also contained a number of demographic questions, including academic field (major) and year of study. Findings: Business students express the highest levels of EI, followed by engineering students and nursing students respectively. Regardless of discipline, students become no more or less entrepreneurial over their years of study. Overall, males were found to be significantly more entrepreneurial than females. However, a comparison of males and females within a given faculty yielded almost no differences in EI between the genders. Originality/value: These findings suggest that students self-select into fields of study based on traits, personalities and interests. It is these same factors that regulate one's EI and not their gender or field of study. Others have analyzed the effects of gender and field of study, the authors isolated the two and analyzed each independently.

The Russian–Ukrainian conflict is considered as a dynamic system, whose variables are factors affecting the losses of the Russian army and the threat of the use of nuclear weapons. A fuzzy cognitive map (FCM) is used for modeling, that is, a directed graph whose vertices are model variables, and the weights of arcs are the degrees of positive and negative influences of variables on each other. The following factors influencing the losses of the Russian army and the threat of a nuclear strike were selected: resistance of the Ukrainian army, support of Ukraine with weapons, economic sanctions against Russia, opposition to the Russian government and its self-preservation instinct. The degrees of the influence of factors on each other and on the possibility of using nuclear weapons are evaluated by experts using fuzzy terms, which correspond to numeric values. To adjust the FCM, a genetic algorithm is used to select the degrees of influence of factors that minimize the discrepancy between the simulation results and expert estimations. The obtained FCM is used for scenario modeling of the conflict according to the "what if" scheme and ranking of factors according to their degree of influence on the level of nuclear threat. [ABSTRACT FROM AUTHOR]

This article offers a method for analyzing the reliability of a man–machine system (MMS) and ranking of influencing factors based on a fuzzy cognitive map (FCM). The ranking of influencing factors is analogous to the ranking of system elements the probabilistic theory of reliability. To approximate the dependence of "influencing factors—reliability," the relationship of variable increments is used, which ensures the sensitivity of the reliability level to variations in the levels of influencing factors. The novelty of the method lies in the fact that the expert values of the weights of the FCM graph edges (arcs) are adjusted based on the results of observations using a genetic algorithm. The algorithm's chromosomes are generated from the intervals of acceptable values of edge weights, and the selection criterion is the sum of squares of deviations of the reliability simulation results from observations. The method is illustrated by the example of a multifactor analysis of the reliability of the "driver–car–road" system. It is shown that the FCM adjustment reduces the discrepancy between the reliability forecast and observations almost in half. Possible applications of the method can be complex systems with vaguely defined structures whose reliability depends very much on interrelated factors measured expertly. [ABSTRACT FROM AUTHOR]

This article offers a method for analyzing the reliability of a man-machine system (MMS) and ranking of influencing factors based on a fuzzy cognitive map (FCM). The ranking of influencing factors is analogous to the ranking of system elements the probabilistic theory of reliability. To approximate the dependence of "influencing factors-reliability," the relationship of variable increments is used, which ensures the sensitivity of the reliability level to variations in the levels of influencing factors. The novelty of the method lies in the fact that the expert values of the weights of the FCM graph edges (arcs) are adjusted based on the results of observations using a genetic algorithm. The algorithm's chromosomes are generated from the intervals of acceptable values of edge weights, and the selection criterion is the sum of squares of deviations of the reliability simulation results from observations. The method is illustrated by the example of a multifactor analysis of the reliability of the "driver-car-road" system. It is shown that the FCM adjustment reduces the discrepancy between the reliability forecast and observations almost in half. Possible applications of the method can be complex systems with vaguely defined structures whose reliability depends very much on interrelated factors measured expertly., (© 2022 Society for Risk Analysis.)