A peer-to-peer dynamic adaptive consensus reaching model for the group AHP decision making

Consensus reaching models are widely applied in group decision making problems to improve the group’s consensus level before making a common decision. Within the context of the group Analytic Hierarchy Process (AHP), a novel consensus reaching model in a dynamic decision environment is proposed. A Markov chain method can be used to determine the decision makers’ weights of importance for the aggregation process with respect to the group members’ opinion transition probabilities. The proposed group consensus reaching model facilitates a peer to peer opinion exchange process which relieves the group of the need for a moderator by using an automatic feedback mechanism. Moreover, as the elements in the group decision framework change in a dynamic decision making problem, this model provides feedback suggestions that adaptively adjust for each of the decision makers depending on his credibility in each round. The full process of the dynamic adaptive consensus reaching model is presented and its properties are discussed. Finally, a numerical example is given to demonstrate the effectiveness of our model.