论文标题
在不同的社交网络结构中的强化沟通学习
Reinforcement Communication Learning in Different Social Network Structures
论文作者
论文摘要
社交网络结构是人类语言进化的关键决定因素之一。先前的工作表明，社会互动网络塑造了人类群体的分散学习，从而导致了各种交流惯例的出现。我们研究了社交网络组织对分散的多方强化学习社区中出现的通信系统属性的影响。我们发现，社交网络的全球连通性驱动了共享和对称通信系统上人口的融合，从而阻止了代理人形成许多本地“方言”。此外，代理商学位与其使用交流惯例的一致性成反比。这些结果表明，社交网络结构基本特性对强化交流学习的重要性，并提出了对人类对单词约定的融合发现的新解释。
Social network structure is one of the key determinants of human language evolution. Previous work has shown that the network of social interactions shapes decentralized learning in human groups, leading to the emergence of different kinds of communicative conventions. We examined the effects of social network organization on the properties of communication systems emerging in decentralized, multi-agent reinforcement learning communities. We found that the global connectivity of a social network drives the convergence of populations on shared and symmetric communication systems, preventing the agents from forming many local "dialects". Moreover, the agent's degree is inversely related to the consistency of its use of communicative conventions. These results show the importance of the basic properties of social network structure on reinforcement communication learning and suggest a new interpretation of findings on human convergence on word conventions.
