Social Structure Emergence: A Multi-agent Reinforcement Learning Framework for Relationship Building

Abstract

Social structures naturally arise from social networks, yet no model well interprets the emergence of structural properties in a unified dimension. Here, we unify explanations for the emergence of network structures by revealing the pivotal role of social capital, i.e., benefits that a society grants to individuals, in network formation. We propose a game-based framework social capital games that mathematically conceptualizes social capital. Through this framework, individuals are regarded as independent learning agents that aim to gain social capital via building interpersonal ties. We adopt multi-agent reinforcement learning (MARL) to train agents. By varying configurations of the game, we observe the emergence of classical structures of community, small-world, and core-periphery.

Publication
19th International Conference on Autonomous Agents and MultiAgent Systems – AAMAS 2020