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Generalized Ordered Weighted Reference Dependent Utility Aggregation Operators and Their Applications to Group Decision-Making

机译:广义有序加权参考相关效用聚集算子及其在群体决策中的应用

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To quantify the influence of decision makers' psychological factors on the group decision process, this paper develops a new class of aggregation operators based on reference-dependent utility functions (RUs) in multi-attribute group decision analysis. RUs include S-shaped RU and non-S-shaped RU. Each RU affords a framework where the psychological factors explicitly enter the decision problem via the basic utility function, reference point and loss aversion coefficient. Under the general framework, we derive a generalized ordered weighted S-shaped RU proportional averaging (GOSP) operator and a generalized ordered weighted non-S-shaped RU proportional averaging (GONSP) operator, respectively. The GOSP operator implies the risk attitude of the DM for relative losses is risk-seeking, while GONSP operator indicates the risk attitude in this case is risk-averse. As a special case, GONSP operator can degenerate into GOWPA operator which means that the attitude of the DM is risk-neutral. Each operator satisfies the desirable properties of general operator, i.e., monotonicity, commutativity, idempotency and boundedness. Furthermore, we consider hyperbolic absolute risk aversion (HARA) function as the basic utility function, and define an S-shaped HARA and a non-S-shaped HARA utility functions. Based on the two new RUs, we propose GOSP-HARA operator and GONSP-HARA operator. Every operator covers many existing aggregation operators. To ascertain weights of such operators, the paper builds an attribute-deviation weight model and a DMs-deviation weight model. Based on these RU operators and weight models, an approach is addressed for solving multiple attribute group decision-making problem. At last, an example is provided to show the feasible of our approach.
机译:为了量化决策者的心理因素对群体决策过程的影响,本文在多属性群体决策分析的基础上,开发了一种基于参考依赖效用函数的新型聚合算子。 RU包括S形RU和非S形RU。每个RU提供了一个框架,其中心理因素通过基本效用函数,参考点和损失厌恶系数明确地输入决策问题。在一般框架下,我们分别导出了广义有序加权S形RU比例平均算子(GOSP)和广义有序加权非S形RU比例平均算子(GONSP)。 GOSP运营商表示DM对相对损失的风险态度是寻求风险,而GONSP运营商表示在这种情况下的风险态度是规避风险的。作为一种特殊情况,GONSP运算符可以退化为GOWPA运算符,这意味着DM的态度与风险无关。每个算子满足一般算子的期望特性,即单调性,可交换性,幂等性和有界性。此外,我们将双曲线绝对风险规避(HARA)函数作为基本效用函数,并定义了S形HARA和非S形HARA实用函数。基于这两个新的RU,我们提出了GOSP-HARA运算符和GONSP-HARA运算符。每个运算符都涵盖许多现有的聚合运算符。为了确定此类算子的权重,本文建立了属性偏差权重模型和DMs偏差权重模型。基于这些RU算子和权重模型,提出了一种解决多属性群决策问题的方法。最后,通过一个例子说明了该方法的可行性。

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