Policy-Based Reinforcement Learning for Assortative Matching in Human Behavior Modeling

TL;DR

This paper introduces a multi-agent reinforcement learning model with attention mechanisms to simulate and analyze human behavior and assortative matching in virtual communities, aiding understanding of social dynamics.

Contribution

It proposes a novel MARL approach with multi-head attention to better model human behavior in socio-economic scenarios, enhancing simulation accuracy.

Findings

Enhanced learning effectiveness with multi-head attention

Simulation of human behavior in diverse environmental settings

Insights into the evolution of social and organizational dynamics

Abstract

This paper explores human behavior in virtual networked communities, specifically individuals or groups' potential and expressive capacity to respond to internal and external stimuli, with assortative matching as a typical example. A modeling approach based on Multi-Agent Reinforcement Learning (MARL) is proposed, adding a multi-head attention function to the A3C algorithm to enhance learning effectiveness. This approach simulates human behavior in certain scenarios through various environmental parameter settings and agent action strategies. In our experiment, reinforcement learning is employed to serve specific agents that learn from environment status and competitor behaviors, optimizing strategies to achieve better results. The simulation includes individual and group levels, displaying possible paths to forming competitive advantages. This modeling approach provides a means for further analysis of the evolutionary dynamics of human behavior, communities, and organizations in various socioeconomic issues.