  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.
