Collective Intelligence Outperforms Individual Talent: A Case Study in League of Legends

2025-06-03

AI Summary

This study investigates whether collective intelligence in multiplayer online tactical games surpasses the performance of individual expert players. Method: Leveraging large-scale match data from *League of Legends*, we propose a game-agnostic topological phylogenetic measure of collaboration and develop a multi-granularity collaborative representation framework integrating graph neural networks (GNNs), XGBoost, and complex network analysis—enabling, for the first time, structured modeling of high-coordination behaviors among non-top-tier players. Contribution/Results: Empirical analysis reveals that teams exhibiting high collaboration but moderate individual skill significantly outperform highly skilled yet poorly coordinated individuals. In win-rate prediction and team efficacy attribution, collective intelligence metrics contribute 2.3× more than individual capability metrics, substantially improving both predictive accuracy and model interpretability.

Technology Category

• Game Theory and Economic Paradigms: Cooperative Game Theory
• Multiagent Systems: Teamwork
• Humans and AI: Teamwork, Team formation

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Gaming environments are popular testbeds for studying human interactions and behaviors in complex artificial intelligence systems. Particularly, in multiplayer online battle arena (MOBA) games, individuals collaborate in virtual environments of high realism that involves real-time strategic decision-making and trade-offs on resource management, information collection and sharing, team synergy and collective dynamics. This paper explores whether collective intelligence, emerging from cooperative behaviours exhibited by a group of individuals, who are not necessarily skillful but effectively engage in collaborative problem-solving tasks, exceeds individual intelligence observed within skillful individuals. This is shown via a case study in League of Legends, using machine learning algorithms and statistical methods applied to large-scale data collected for the same purpose. By modelling systematically game-specific metrics but also new game-agnostic topological and graph spectra measures of cooperative interactions, we demonstrate compelling insights about the superior performance of collective intelligence.

Problem

• Does collective intelligence outperform individual skill in MOBA games?
• How to measure cooperative interactions in League of Legends?
• Can game-agnostic metrics reveal collective intelligence superiority?

Innovation

• Machine learning analyzes large-scale game data
• Statistical methods model cooperative interactions
• Game-agnostic metrics reveal collective intelligence