On efficiency-oriented support of consensus reaching in a group of agents in a fuzzy environment with a cost based preference updating approach

Abstract

We further develop our model of consensus reaching, and its related decision support system, that is based on the concept of a soft degree of consensus proposed by Kacprzyk and Fedrizzi, the idea of fuzzy preferences, and Kacprzyk's fuzzy majority. We assume that the process of consensus reaching proceeds in a (small) group of agents (decision makers, experts,…) who express their testimonies with respect to a set of options in the form of fuzzy preferences. We develop tools and techniques to extract from those data, and from the consecutive steps (dynamics) of the consensus reaching process, some additional information which is assumed in a human consistent form of linguistic summaries that can be derived by using natural language generation (NLG). This information is meant to accelerate the consensus reaching process by pointing out to those individuals for whom the changed of testimonies, and with respect to specific pairs of options, can have the highest impact on the degree of consensus. It is therefore explicitly efficiency oriented. We assume a moderated consensus reaching process run by a specialized "super-agent", a moderator. In this paper we further extend a model and implementation of such a consensus reaching process proposed in our previous papers. We further develop linguistic tools and techniques, in the form of linguistic summaries, to help grasp relations and interplay between the agents' testimonies and their dynamics numerically analyzed by additional indicators pointing out agents and options that are most promising for the changes of preferences. We proposed a cost based scheme for the evaluation of preference updating so that the agents be not forced to change too often and too many of their preferences, which is not usually welcome by people for psychological reasons, and which should contribute to their better collaboration .