Multiple preferences induced aggregation with uncertainty influences in group evaluation of water resource management

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS(2023)

引用 1|浏览6
暂无评分
摘要
In many multi criteria group decision making problems, the individual evaluation values offered by experts are with uncertainties. Therefore, when assigning weights to those experts using preferences induced weights allocation, we can have two types of bi-polar preferences. The first one is the optimism-pessimism preference over evaluation values; the second one is the uncertainty aversion preference over the attached numerical certainty/uncertainty degrees. When performing preferences induced weights allocation, the certainty/uncertainty degrees will affect the optimism-pessimism preference induced weights allocation because the magnitudes of those evaluation values might not be the exact ones. Moreover, the importance of those experts in multi criteria group decision making can also have influence over the two types of preference induced weights allocation processes, and the importance can also be with uncertainties and can be expressed using basic uncertain information. Therefore, to handle this situation with multiple inducing variables and uncertainties, we simultaneously consider the influence of the uncertainties attached to evaluation values and the influence of uncertain importance of experts, and thus we at the same time adopt the method of confidence threshold and the method of uncertain importance level function to propose some synthesized method to adjust the induced weights allocation processes. We also propose a complete multi criteria group decision making problems to show the feasibility and reasonability of the proposed decision model for the complex situation where both evaluation values and expert importance are expressed by basic uncertain information.
更多
查看译文
关键词
Aggregation operators, basic uncertain information, induced ordered weighted averaging operators, information fusion, multi criteria group decision making, uncertain decision making
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