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Approaches to assess the group consensus in Yes-or-No type experts’ group decision making

机译:在是或否类型专家的小组决策中评估小组共识的方法

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Group consensus indicators provide an important insight and information about how to combine a group of expert judgments. This paper is concerned with the development of a set of indicators to be used in analyzing the group consensus in evaluating Yes-or-No type’s decision problems. The opinions of the experts are in the form of a real number between 0 and 10 expressing the degree of answers Yes or No (0 for sharp No and 10 for sharp Yes). Two methods for obtaining the consensus indicators are developed. The first of them is based on configuring the one previously developed by (Ngwenyama et al. 1996), which is reviewed in this paper. The other one is an improved one that does not rely on the existence of the known or desired similarity significance levels or thresholds. A new measure of consensus is introduced, the standard deviation. An experiment is conducted to get acquainted with the relationship between the standard deviation of group decisions and one of the developed group consensus indicators, which measures the agreement level within the group of decisions. This research is intended to develop more consistent indicators and measures group consensus and position of each individual relative to others for Yes-or-No type group decisions. This is aimed at the exploitation of such important and relevant consensus information for developing a new consensus-based heuristic algorithm to combine the multiple experts’ judgments or to be able to select the adequate combining criteria. Finally, the presented approach could be usefully utilized in critical “Yes – or – No” GDM problems in business and industry.
机译:群体共识指标提供了有关如何结合专家判断的重要见解和信息。本文关注于一组指标的开发,该指标可用于分析小组共识以评估是或否类型的决策问题。专家的意见采用0到10之间的实数形式,表示答案的程度为是或否(0代表尖锐的否,而10代表尖锐的是)。开发了两种获取共识指标的方法。它们中的第一个是基于对先前由(Ngwenyama et al。1996)开发的模型的配置,本文对此进行了概述。另一个是不依赖已知或期望的相似性显着性水平或阈值的存在的改进的。引入了一种新的共识度量,即标准偏差。进行实验以了解组决策的标准偏差与已开发的组共识指标之一之间的关系,该指标衡量组决策中的协议水平。这项研究旨在开发更一致的指标,并衡量是或否类型组决策中每个人相对于其他人的组间共识和位置。这旨在利用这些重要且相关的共识信息来开发新的基于共识的启发式算法,以结合多个专家的判断或能够选择适当的结合标准。最后,所提出的方法可以有效地用于企业和行业中严重的“是–或–否” GDM问题中。

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