Recently, the study of social conventions has attracted much attention in the literature. We notice that a type of interesting phenomena, local convention phenomena, may also exist in certain multiagent systems. When agents are partitioned into compact communities, different local conventions emerge in different communities. In this paper, we provide a definition for local conventions, and propose two metrics measuring their strength and diversity. In our experimental study, we show that agents can achieve coordination via establishing diverse stable local conventions, which indicates a practical way to solve coordination problems other than the traditional global convention emergence. Moreover, we find that with smaller community sizes, denser connections and fewer available actions, diverse local conventions emerge in shorter time.
Understanding the learning dynamics in multiagent systems is an important and challenging task. Past research on multi-agent learning mostly focuses on two-agent settings. In this paper, we consider the scenario in which a population of infinitely many agents apply regret minimization in repeated symmetric games. We propose a new formal model based on the master equation approach in statistical physics to describe the evolutionary dynamics in the agent population. Our model takes the form of a partial differential equation, which describes how the probability distribution of regret evolves over time. Through experiments, we show that our theoretical results are consistent with the agent-based simulation results.
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