Dear Colleagues,

In the last two decades, multi-agent systems have been widely investigated in artificial intelligence and algorithmics, due to strongly emerging real-life scenarios characterized by decentralization, autonomy and general lack of coordination. A large number of agents in our digital world and society exhibit various degrees of intentional or unintentional non-cooperative behaviour, while competing for shared and often scarce resources. In such a highly elusive and mutable setting, where scalability and efficiency are an issue, a suitable compromise needs to be reached among several desirable properties, such as good social welfare, stability, fairness, etc. In this respect, the general mismatch between the global optimization goals and agents’ preferences and private interests, the lack of coordination, and the pressing need for scalability and good performance all call for new research directions and solutions, resorting to tools and insights coming from the integration of algorithmic ideas with techniques borrowed from game theory and computational social choice. 

Original contributions in the above frame of reference are welcome, with special concern to the following challenges:

• If a given multi-agents system reaches a stable/equilibrium configuration, how suboptimal can the resulting performance be?
• Is it possible to be fair toward all the agents (e.g., when assigning goods and resources), without overly affecting other desirable properties (e.g., social welfare)?
• Can a central authority rule systems or intervene in order to encourage a cooperative behaviour of the agents and lead them, at least partially, toward better outcomes/choices?

We would welcome contributions including, but not limited to, the following settings: fair allocation, markets, coalition and team formation, matching problems, network and congestion games, voting, social influence maximization.

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• Multi-agent systems 
• Equilibria 
• Mechanism Design 
• Social Welfare 
• Price of Anarchy 
• Price of Stability 
• Fairness