Multiagent coordination in cooperative multiagent systems (MASs) has been widely studied in both fixed-agent repeated interaction setting and the static social learning framework. However, two aspects of dynamics in real-world multiagent scenarios are currently missing in existing works. First, the network topologies can be dynamic where agents may change their connections through rewiring during the course of interactions. Second, the game matrix between each pair of agents may not be static and usually not known as a prior. Both the network dynamic and game uncertainty increase the coordination difficulty among agents. In this paper, we consider a multiagent dynamic social learning environment in which each agent can choose to rewire potential partners and interact with randomly chosen neighbors in each round. We propose an optimal rewiring strategy for agents to select most beneficial peers to interact with for the purpose of maximizing the accumulated payoff in repeated interactions. We empirically demonstrate the effectiveness and robustness of our approach through comparing with benchmark strategies. The performance of three representative learning strategies under our social learning framework with our optimal rewiring is investigated as well.
 In systems of autonomous self-interested agents, in which agents' neighbourhoods are defined by their connections to others, cooperation can arise through observation of the behaviour of neighbours to determine values of trust and reputation. While there are many techniques for encouraging cooperative behaviour within such systems, they often require a centralised authority or rely on reciprocity that is not always available. In response, this paper presents a decentralised mechanism to supporting cooperation without requiring reciprocity. The mechanism is based on tag-based cooperation, supplemented by assessing neighbourhood context and using simple rewiring to cope with cheaters. In particular, the paper makes two key contributions. First, it provides a technique for increasing resilience in the face of malicious behaviour by enabling individuals to rewire their connections to others and so modify their neighbourhoods. Second, it provides an empirical analysis of several strategies for rewiring, evaluating them through simulations. In this paper, we focus on the problem of maintaining significant levels of cooperation in peer-to-peer (P2P) networks of selfish adaptive peers. We propose a simple algorithm that maintains high levels of cooperation in such a network while performing the collective task of file sharing. The algorithm is adapted from novel "tag" models of cooperation that do not rely on explicit reciprocity, reputation or trust mechanisms. A sequence of three simulation models is presented-starting with an abstract model of tag-based cooperation (TagWorld) and finishing with a P2P file-sharing model (FileWorld) that puts the technique to work. From analysis of extensive computer simulations, we demonstrate the technique to be scalable, robust, and decentralized; it requires no central servers or authorities. The algorithm is relatively simple: peers do not need to store additional trust information about other nodes or to perform significant additional processing. It is generally known that cooperation can be achieved in complex real-world interactions that are not limited to direct interactions only. In particular, cooperation can consider prior interactions with other players, i.e., indirect reciprocity. Moreover, coalition based mechanisms have shown to facilitate cooperation among self-interested agents. Also, research on games over dynamic topologies has found empirical evidence showing that partner switching leads to cooperative behavior. In this paper we present a new mechanism to improve cooperation among self-interested agents placed in a complex network. Our mechanism is based on three main pillars: indirect reciprocity, coalitions and rewiring. Thus agents play against each other an indirect reciprocity game where they can create coalitions to share information about agents' reputation or change their personal network (social contacts). Altogether, we explore the conditions to enhance cooperation in complex networks. Finally, in our experiments we determine how, by using our mechanism, cooperation is improved in our reputation-based game, and how topology highly influences cooperation in our scenario.