A distributed cooperative approach for optimizing a family of network games

The present study considers a distributed cooperative approach for network problems where agents have personal preferences over outcomes. Such problems can be described by Asymmetric Constraints where the joint action of agents yields different gains to each participant Grubshtein et al. (2010). The proposed method constructs and solves an Asymmetric Distributed Constraints Optimization Problem whose solutions guarantee a minimal gain for each agent, which is at least as high as the agents' Bayesian equilibrium gain. The paper focuses on a special class of Network Games and proves that the proposed method produces optimal results in terms of the number of agents whose gain improves over their equilibrium gain and that the resulting solutions are Pareto Efficient. Extensive empirical evaluation of the studied network problem shows that the number of improving agents is not negligible and that under some configurations up to 70% of the agents improve their gain while none of the agents receive a payoff lower than their equilibrium gain.