Abstract:

The problems of decision theory include voting tasks. Contemporary science knows a lot of methods to determine the collective order, each of them having its advantages and disadvantages. Practical voting tasks often suggest cases that consider voters’ individual rankings as well as their subjective characteristics such as nihilism, extreme optimism or inability to clearly determine the best candidate for themselves. There is also an interesting case when the winner of the voting task is a candidate rejected by the majority of voters. To analyze such cases, we should consider a fuzzy voting problem in which voters indicate both individual rankings and the degree of each candidate’s affiliation to the fuzzy set characterizing the degree of the candidate’s proximity to the victory. The study focuses on a fuzzy voting problem. We suggest heuristics for preliminary processing of input data. At the first stages of solving the problem, the data allow weeding out the worst candidates for a specified number of voters and excluding the rankings of voters characterized by extreme nihilism or extreme optimism, etc. We have worked out rules for establishing the collective order in a fuzzy voting problem. The rules are similar to some of the rules in a clear case. We have devised examples that demonstrate usefulness of the suggested heuristics and rules to solve voting problems. Author Biography Оксана Юріївна Мулеса, Uzhgorod national university Narodna 3, Uzhgorod, Ukraine, 88000 Associate professor, Candidate of technical science The department of cybernetics and applied mathematics

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