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This paper studies multi-agent systems that involve networks ofself-interested agents. We propose a Markov Decision Process-derived framework,called RepNet-MDP, tailored to domains in which agent reputation is a keydriver of the interactions between agents. The fundamentals are based on theprinciples of RepNet-POMDP, a framework developed by Rens et al. in 2018, butaddresses its mathematical inconsistencies and alleviates its intractability byonly considering fully observable environments. We furthermore use an onlinelearning algorithm for finding approximate solutions to RepNet-MDPs. In aseries of experiments, RepNet agents are shown to be able to adapt their ownbehavior to the past behavior and reliability of the remaining agents of thenetwork. Finally, our work identifies a limitation of the framework in itscurrent formulation that prevents its agents from learning in circumstances inwhich they are not a primary actor.

Reputation-driven Decision-making in Networks of Stochastic Agents