Can Reinforcement Learning Enhance Social Capital?

Social capital captures the positional advantage gained by an individual by being in a social network. A well-known dichotomy defines two types of social capital: bonding capital, which refers to welfare such as trust and norms, and bridging capital, which refers to benefits in terms of influence and power. We present a framework where these notions are mathematically conceptualized. Through the framework, we discuss the process when an individual gains social capital through building new edges. We explore two questions: (1) How would an individual optimally form new relations? (2) What are the impacts of the network structure on the individual’s social capital? For these questions, we adopt a paradigm where the individual is a utility-driven agent who acquires knowledge about the network through repeated trial-and-error. In this paradigm, we propose two reinforcement learning algorithms: one guarantees the convergence to optimal values in theory, while the other is efficient in practice. We conduct experiments over both synthetic and real-world networks. Experimental results indicate that a centralized structure can enhance the performance of learning.