Modeling Human Behavior in a Strategic Network Game with Complex Group Dynamics

Human networks greatly impact important societal outcomes, including wealth and health inequality, poverty, and bullying. As such, understanding human networks is critical to learning how to promote favorable societal outcomes. As a step toward better understanding human networks, we compare and contrast several methods for learning models of human behavior in a strategic network game called the Junior High Game (JHG). These modeling methods differ with respect to the assumptions they use to parameterize human behavior (behavior vs. community-aware behavior) and the statistical moments they model (mean vs. distribution). Results show that the highest-performing method models the population's distribution rather than the mean and assumes humans use community-aware behavior rather than behavior matching. When applied to small societies (6-11 individuals), this learned model, called hCAB, closely mirrors the population dynamics of human groups (with some differences). Additionally, a user study reveals that human participants were unable to distinguish hCAB agents from other humans, thus illustrating that individual hCAB behavior plausibly mirrors human behavior in this strategic network game.