Using Social Choice Function Vs. Social Welfare Function To Aggregate Individual Preferences In Group Decision Support Systems
Using Social Choice Function Vs. Social Welfare Function To Aggregate Individual Preferences In Group Decision Support Systems
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使用社会选择函数与。
作者：
D. Nguyen
在多准则决策中，任何群体决策支持系统（GDSS）都需要一个“社会判断模型”来计算决策方案的权重，并将个人投票结果制成表格。人们可以评估一个社会福利函数--如基尼函数--将个人的基本偏好或效用汇总为一个群体偏好。或者，可以使用社会选择函数--如孔多塞、博尔达、科普兰和本征向量--将个人的序数偏好或排名聚合成一个群体排名。本研究以实证的方式，探讨由不同的整合方法所衍生出的个人偏好与群体偏好之间的一致性。
In multi-criteria decision making, any Group Decision Support System (GDSS) requires a “social judgment model” for calculation of weights on decision alternatives, and tabulation of individual votes toward a consensus. One could assess a Social Welfare Function - such as Keeney’s - to aggregate individual cardinal preferences or utilities into a group preference. Alternatively, one could use Social Choice Functions - such as Condorcet, Borda, Copeland, and Eigenvector - to aggregate individual ordinal preferences or rankings into a group ranking. This study empirically investigates the consensus between individual preferences and the group preference derived from various aggregation methods.