To be Big Picture Thinker or Detail-Oriented? Utilizing Perceived Gist Information to Achieve Efficient Convention Emergence with Bilateralism and Multilateralism

Recently, the study of social conventions (or norms) has attracted much attention. In this paper, we study the emergence of conventions from agents' repeated coordination games via bilateralism and multilateralism. We assume that agents can perceive the gist information, i.e., a big picture of how popular each action is in their neighbourhood. A novel reinforcement learning approach which utilizes the gist information is proposed. Experiment verifies that the proposed approach significantly outperforms the baseline and the state-of-the-art approaches, in terms of the speed of convention emergence.