Enabling Imitation-Based Cooperation in Dynamic Social Networks

Jacques Bara, Paolo Turrini, Giulia Andrighetto

Abstract: The emergence of cooperation among self-interested agents has been a key concern of the multi-agent systems community for decades. With the increased importance of network-mediated interaction, researchers have shifted the attention to the impact of social networks and their dynamics on cooperation, drawing various context-dependent and at times conflicting conclusions. In this short paper, summarising the findings in [1], we provide an evolutionary game theory framework to understand coevolutionary processes from a bottom up perspective - in particular the emergence of a cooperator-core and defector-periphery - clarifying the impact of partner selection and imitation strategies in promoting cooperative behaviour, without assuming underlying communication or reputation mechanisms. In doing so we provide a unifying framework to study imitation-based cooperation in dynamic social networks and show that disputes in the literature can in fact coexist.