Game Behavior-Driven Consensus Models With Maximum Linear-Quadratic Payoffs and Minimum Adjustment

Abstract

Interaction behaviors play a core role in the process of reaching a consensus. In this article, a network game is employed to model the interplay between the behaviors of decision makers (DMs) and Stackelberg game architecture is used to design an interactive mechanism between the DMs and the moderator. An optimization model based on these two games results in a consensus model with maximum linear-quadratic payoffs and minimum adjustment (MPMACM). In the proposed MPMACM, the moderator provides compensation strategies and feedback suggestions to guide the DMs to reach the desired consensus level with minimum adjustment, while the DMs adjust their opinions aiming to obtain their maximum payoffs. We present the equilibrium analysis for the MPMACM, and an adaptive differential evolution algorithm is offered to enact this optimization model. Finally, an example application is conducted to illustrate and justify the performance of the MPMACM.