On policy capturing with fuzzy measures

Policy capturing methods generally apply linear regression analysis to model human judgment. In this paper, we examine the application of fuzzy set and fuzzy measure theories to obtain subjective descriptions of cue importance for policy capturing. At the heart of the approach is a method of learning fuzzy measures. The Shapley values associated with the fuzzy measures provide a basis for comparison with the results of linear regression. However, the fuzzy measure-theoretical approach provides additional insight into interaction effects corresponding to the nonlinear, noncompensatory nature of the underlying decision model. To illustrate the methodology, we estimated the importance of factors and the interactions among them that influence decisions related to strategic investments in telecommunications infrastructure and compared the results from the fuzzy approach to those obtained from traditional statistical methods.