• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Decision Sciences >Distribution of Aggregate Utility Using Stochastic Elements of Additive Multiattribute Utility Models
【24h】

Distribution of Aggregate Utility Using Stochastic Elements of Additive Multiattribute Utility Models

机译：使用加性多属性效用模型的随机元素分配总效用

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Conventionally, elements of a multiattribute utility model characterizing a decision maker's preferences, such as attribute weights and attribute utilities, are treated as deter- ministic, which may be unrealistic because assessment of such elements can be impre- cise and erroneous, or differ among a group of individuals. Moreover, attempting to make precise assessments can be time consuming and cognitively demanding. We pro- pose to treat such elements as stochastic variables to account for inconsistency and imprecision in such assessments. Under these assumptions, we develop procedures for computing the probability distribution of aggregate utility for an additive multiattribute utility function (MAUF), based on the Edgewoorth expansion. When the distributions of aggregate utility for all alternatives in a decision problem are known, stochastic domi- nance can then be invoked to filter inferior alternatives. We show that, under certain mild conditions, the aggregate utility distribution approaches normality as the number of attributes increases. Thus, only a few terms from the Edgeworth expansion with a stan- dard normal density as the base function will be sufficient for approximating an aggre- gate utility distribution in practice. Moreover, the more symmetric the attribute utility distributions, the fewer the attributes to achieve normality. The Edgeworth expansion thus can provide a basis for a computationally viable approach for representing an aggregate utility distribution with imprecisely specified attribute weights and utilities assessments (or differing weights and utilities across individuals). Practical guidelines for using the Edgeworth approximation are given. The proposed methodology is illus- trated using a vendor selection problem.
机译：传统上，表征决策者偏好的多属性效用模型的元素（例如属性权重和属性效用）被视为确定性的，这可能是不现实的，因为对此类元素的评估可能是不准确和错误的，或者在一群人。此外，尝试进行精确的评估可能既耗时又需要认知。我们建议将此类元素视为随机变量，以解决此类评估中的不一致和不精确之处。在这些假设下，我们基于Edgewoorth展开，开发了用于计算加法多属性效用函数（MAUF）的聚合效用概率分布的过程。当知道决策问题中所有备选方案的集合效用分布时，便可以调用随机优势来过滤劣等备选方案。我们表明，在某些温和条件下，随着属性数量的增加，总效用分布接近正态。因此，从Edgeworth扩展中仅以标准正态密度作为基本函数的几个项就足以在实践中近似集聚效用分布。此外，属性效用分布越对称，实现正态性的属性越少。因此，Edgeworth扩展可以为表示具有不精确指定的属性权重和效用评估（或各个人的权重和效用不同）的总体效用分布的计算可行方法提供基础。给出了使用Edgeworth逼近的实用准则。使用供应商选择问题说明了所提出的方法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号