under the social learning framework [@sen2007emergence; @villatoro2011social; @yu2013emergence; @hao2013dynamics; @hao2014reinforcement; @hao2017dynamics; @airiau2014emergence; @mihaylov2014decentralized], where agents learn through pair-wise interactions by playing the same game. Sen and Airiau [@sen2007emergence] propose the social learning model and investigate the emergence of consistent coordination policies (norms) in MASs (e.g., conflicting-interest games) under this social learning framework. Hao and Leung investigate the multiagent coordination problems in cooperative environments under the social learning framework. They demonstrate individual action learners (IALs) and joint action learners (JALs) and achieve better coordination performance by leveraging the observation mechanism. Most of previous works assume that agents learn through playing the same cooperative game with randomly selected partners from their neighborhood within a static network. In contrast, in this paper, we consider a dynamic environment in which agents learn through pair-wise interactions through playing uniquely and randomly generated cooperative games.

There are some existing works on using rewiring mechanism to support cooperation in social networks [@peleteiro2014exploring; @griffiths2010changing; @hales2005applying; @dekker2007realistic]. Peleteiro et al. [@peleteiro2014exploring] propose a new mechanism based on three main pillars: indirect reciprocity, coalitions and rewiring, to improve cooperation among self-interested agents placed in a complex network. Rewiring, as a part of the proposed mechanism, alters the worst social links 