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

TL;DR

This paper compares methods for modeling human behavior in a strategic network game, finding that a community-aware, distribution-based model best captures group dynamics and produces plausible individual behaviors.

Contribution

It introduces and evaluates the hCAB model, which uses community-aware, distribution-based assumptions to better replicate human behavior in network games.

Findings

hCAB closely mirrors population dynamics in small societies

Participants struggled to distinguish hCAB agents from humans

hCAB outperforms other models in capturing behavior distribution

Abstract

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) [39]. These modeling methods differ with respect to the assumptions they use to parameterize human behavior (behavior matching 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, the hCAB model closely mirrors the population dynamics of human groups (with notable differences). Additionally, in a user study, human participants had difficulty distinguishing hCAB agents from other humans, thus illustrating that the hCAB model also produces plausible (individual) behavior in this strategic network game.