The Harmony Index: Evaluating, Predicting, and Visualizing Effectiveness in Multi-Agent Team Dynamics

Abstract

Team-based games are a keystone pillar of the gaming industry. Sadly, the understanding of team dynamics—and the recommendations for both human and AI-based teammates—are based on a rudimentary understanding of human-AI teaming. We propose a superior metric, which provides information about team effectiveness in an efficient and easily-replicable manner. Without an accurate and effective metric for team evaluation, it is nigh-impossible to provide feedback to players and game designers to improve team balance. We provide such a metric. The Harmony Index, a novel algorithm using real-world data, provides simpler and more accurate actionable directives to improve game design across MOBAs and other game genres. We prove its predictive power in a separate analysis and make recommendations for its use in assessing team effectiveness as well as its future use in additional domains.