- 期刊
On the Similarity Evaluation of Candidates in Ranked Voting Model
並列摘要
In preference voting model, it has been shown that the single voting has some issues. So, the model that each voter has two or more votes is recommended so far. Ranked voting data arise when voters vote candidates with their ranking of preference. Such data are often processed after summing up the votes in each candidate and each rank. Many methods to order all candidates or to identify the most preferable candidate from these data have been proposed recently. However, these data have no information about which candidate tends to be ranked as the second by the voters who ranked a certain candidate as top. The candidates who are ranked highly by the same voter seem to be similarly evaluated for her/him. Therefore, if many voters support a pair of candidates, we can judge that the pair has high similarity. On this hypothesis, we propose a method to estimate the similarity and the configuration of the candidates with multidimensional scaling, under ranked voting model. We also propose a model underlying voting behavior, and investigate the validity of our estimating method with the model. Further we mention the possibility of the mathematical method that is efficient for the voting model of multiple selections among candidates.
參考文獻
延伸閱讀
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