  Cooperative behaviors are common in humans and are fundamental to our
society. Theoretical and experimental studies have modeled environments in
which the behaviors of humans, or agents, have been restricted to analyze their
social behavior. However, it is important that such studies are generalized to
less restrictive environments to understand human society. Social network games
(SNGs) provide a particularly powerful tool for the quantitative study of human
behavior. In SNGs, numerous players can behave more freely than in the
environments used in previous studies; moreover, their relationships include
apparent conflicts of interest and every action can be recorded. We focused on
reciprocal altruism, one of the mechanisms that generate cooperative behavior.
This study aims to investigate cooperative behavior based on reciprocal
altruism in a less restrictive environment. For this purpose, we analyzed the
social behavior underlying such cooperative behavior in an SNG. We focused on a
game scenario in which the relationship between the players was similar to that
in the Leader game. We defined cooperative behaviors by constructing a payoff
matrix in the scenario. The results showed that players maintained cooperative
behavior based on reciprocal altruism, and cooperators received more advantages
than noncooperators. We found that players constructed reciprocal relationships
based on two types of interactions, cooperative behavior and unproductive
communication.

Social and Information Networks;Physics and Society;Populations and Evolution