Development of multi-person multi-attribute matchmaking decision system

Abstract

This dissertation reports on the development of an algorithm based on an existing matchmaking method to solve diverse decision problems in a multi-person environment. The capacity to effectively achieve a lucrative and accurate decision making is a critical aspect of resource management. But the accuracy of a decision making process can be highly compromised because of the high subjectivity and multiple conflicting attributes that are present in human judgement. multi-person decision making is an effective approach for achieving a lucrative and accurate decision making process. The multi-person decision process has proven to be tedious mainly because the existing multi-person decision making methods are extensions of single decision making methods. This imposes additional computational resources, especially for a large number of decision makers because they aggregate the preferences of several decision makers into a unified format.This work therefore seeks to improve the multi-person decision making process using a matchmaking approach. In doing so, the Hunt ForTune matchmaking algorithm was investigated and improved for this purpose. Thus, the preferences of decision makers for each attribute are collected as an attribute description vector. The attribute, its description vector, flexibility and priority vector are compactly represented as a 4-tuple profile. The improved Hunt ForTune matchmaking algorithm is applied to different sets of multi-person decision problems and offered as an effective way of enhancing decision accuracy. The improved matchmaking decision algorithm is compared with a novel mathematical technique of Hausdorff distance. Results generally show that multi-person matchmaking algorithm is suitable and efficient for diverse decision making in the presence of multiple decision makers. The practical implication of the proposed multi-person matchmaking algorithm for decision making is that it provides a less complicated way to capture and represent the preferences of multiple decision makers irrespective of decision domain. The originality of the work reported in this dissertation is built on a matchmaking algorithm by introducing effective profile representation using vector analysis approach to capture the preferences of multiple decision makers and similarity metrics to provide an efficient and robust way to accurately perform a multi-person decision process.