谷歌Chrome浏览器插件
订阅小程序
在清言上使用

A Credibility and Strategic Behavior Approach in Hesitant Multiple Criteria Decision-Making With Application to Sustainable Transportation

IEEE Transactions on Fuzzy Systems(2023)

引用 5|浏览27
暂无评分
摘要
Multiple criteria decision-making (MCDM) methods do not account for the potentially strategic evaluations of experts. Once the ranking is delivered, decision makers (DMs) select the first alternative without questioning the credibility of the evaluations received from the experts. We formalize the selection problem of a DM who must choose from a set of alternatives according to both their characteristics and the credibility of the reports received. That is, we transform an MCDM setting into a game-theoretical scenario. We build our analysis on a recent extension of hesitant fuzzy numbers incorporated within the formal structure of technique for order of preference by similarity to ideal solution. We define the restrictions that must be imposed regarding the credibility of the evaluations and the capacity of experts to form coalitions and manipulate rankings based on their subjective preferences. This feature constitutes a considerable drawback in real-life scenarios, mainly when dealing with environmental and sustainable strategic problems. In this regard, sustainable transportation problems incorporate both technical variables and subjective assessments whose values can be strategically reported by experts. We extend a real-life study case accounting for the evaluations of several experts to demonstrate the importance of strategic incentives for the rankings obtained when implementing MCDM techniques. We numerically illustrate the interactions between the experts’ reporting strategies and the formal tools available for the DMs to counteract potential manipulations of the final ranking.
更多
查看译文
关键词
Credibility,hesitant fuzzy numbers (HFNs),strategic behavior,sustainable transportation,technique for order of preference by similarity to ideal solution (TOPSIS)
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要