Aggregation of preference information using neural networks in group multiattribute decision analysis

摘要

In this paper, we deal with group multiattribute utility analysis incorporating the preferences of multiple interested individuals. Since it is difficult to repeatedly ask these individuals questions for determining parameters of a multiattribute utility function, we gather preference information of them by asking questions that are not difficult to answer, and develop a method for selecting an alternative consistent with the preference information. Assuming that the multiattribute utility function has the multiplicative form and the corresponding single-attribute utility functions are already identified, we evaluate the trade-off between attributes by utilizing neural networks whose inputs and outputs are the preference information elicited from the interested individuals and the scaling constants for the multiattribute utility function, respectively. By performing computational experiments, we verify that the proposed method can generate scaling constants properly. Furthermore, using the preference relations from two groups with different degrees of the preferential heterogeneity, we examine the effectiveness of the proposed method, and then we show that the results of the numerical applications are reasonable and proper.