A Study on Varying Degrees of Attention in Multi-Attribute Decision Making Processes

Abstract

Recently many research studies using fuzzy theory have been done for the modelings of complex systems including human beings. Especially in the field of decision-makings, and human emotions, the researches have been developing. The authors have proposed a multi-attlibute decision making model based on fuzzy inference and realized the model with a fuzzy neural network (FNN). In this paper, we compare our model with the conjoint analysis which is a practical tool for measuring consumers' perception of the products, and clarify the remarkable points as well as the limits of our model.In multi-attribute decision making, human beings influenced with various factors often change their decisions. There have been few researches that studied the change of human decisions. This paper presents a new approach to identify the change in a decision making process. The new approach is based on the model which the authors have proposed. The FNN identifies the weights to the attributes with the back propagation learning.Through experiments, it is shown that the changes of subjects' decisions can be described by the changes of their weights to the attributes. It is also studied how the change will be when some new information are added during the decision making.