Consensus measuring in multicriteria decision making under a environment of uncertainty, with an application in climate policy

Abstract

Apart from multicriteria decision analysis of problems, it is equally important to measure consensus. The purpose of this work is to present, and extensively analyze, methods of consensus measuring mainly in environments arising from TOPSIS multicriteria analysis. Specifically, three methods of measuring consensus are analyzed. At the same time, the goal is to automate the methods, algorithmically as much as possible. Python programming language has been used for implementation. Consensus is also measured from the statistical point of view and reference is made to the individual and collective consistency of the decision makers. Within the methods that are analyzed, ways to improve the level of consensus are explored, and a method of determining the weights of decision-makers are analyzed in order to find a collective decision utilizing he concept of cooperation. At the same time, consensus is analyzed in respect with utility and cost. Finally, the three methods of measuring consensus that are proposed are practically implemented for an application regarding the Austrian steel sector, evaluating certain risks against some criteria, with the aim of reducing the level of carbon dioxide emissions into the atmosphere.