Modeling Human Behavior in a Strategic Network Game with Complex Group Dynamics
This work addresses understanding human networks to improve societal outcomes, but it is incremental as it focuses on a specific game and small data sets.
The paper tackled modeling human behavior in the Junior High Game, a strategic network game, by comparing methods that differ in assumptions and moments modeled; the best method, hCAB, which models distribution and uses community-aware behavior, closely mirrored group dynamics in small societies and produced agents indistinguishable from humans in a user study.
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, from a small data set, 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 moments they model (mean vs. distribution). Results show that the highest-performing method, called hCAB, models the distribution of human behavior rather than the mean and assumes humans use community-aware behavior rather than behavior matching. When applied to small societies (6-11 individuals), the hCAB model closely mirrors the population dynamics of human groups (with notable differences). Additionally, in a user study, human participants were unable to distinguish individual hCAB agents from other humans, thus illustrating that the hCAB model also produces plausible (individual) human behavior in this strategic network game.