Social capital brings individuals benefits and advantages in societies. In this paper, we formalize two types of social capital: bonding capital refers to links to neighbours, while bridging capital refers to brokerages between others. We ask the questions: How would a marginal individual gain social capital with imperfect information of the society? We formalize this issue as the partially observable network building problem and propose two reinforcement learning algorithms: one guarantees the convergence to optimal values in theory, while the other is efficient in practice. We conduct simulations over a real-world dataset, and experimental results coincide with our theoretical analysis.
CITATION STYLE
Zhao, H., Su, H., Chen, Y., Liu, J., Zheng, H., & Yan, B. (2019). A reinforcement learning approach to gaining social capital with partial observation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11670 LNAI, pp. 113–117). Springer Verlag. https://doi.org/10.1007/978-3-030-29908-8_9
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