Decision-Making Process Customization by using Expected Solution Point

Abstract

Much effort in modern scientific work has been put into improving objective decision-making. However, very often, it turns out that some problems do not have objective solutions that ft everyone—for example, buying a cell phone or a car. If one model could meet the expectations of each user, then we would not have so much diversity in the market. This observation motivates to work on customization in the decision-making process. In this work, we show approaches that help customize the decision-making process for a specific customer. In addition to expert methods for determining importance weights, the characteristic object method or the SPOTIS method could be used for this purpose. The presented research shows how the Expected Solution Point (ESP) can be used to make individual decisions and thus facilitate the process of customization. For this reason, we compare the objective result obtained using Ideal Solution Point (ISP) and results for artificial decision-makers using simulated ESP vectors. Simulation studies on a real database demonstrate the usefulness of the ESP approach to expressing personalized preferences.