Power indices are essential in assessing the contribution and influence of individual agents in multi-agent systems, providing crucial insights into collaborative dynamics and decision-making processes. While invaluable, traditional computational methods for exact or estimated power indices values require significant time and computational constraints, especially for large coalitions. These constraints have historically limited researchers' ability to analyse complex multi-agent interactions comprehensively. To address this limitation, we introduce a novel Neural Networks-based approach that efficiently estimates power indices for voting games, demonstrating comparable and often superiour performance to existing tools in terms of both speed and accuracy. This method not only addresses existing computational bottlenecks, but also enables rapid analysis of large coalitions, opening new avenues for multi-agent system research by overcoming previous computational limitations and providing researchers with a more accessible, scalable analyticalthis http URLincreased efficiency will allow for the analysis of more complex and realistic multi-agent scenarios.
View on arXiv@article{kempinski2025_2503.08381,
title={ InfluenceNet: AI Models for Banzhaf and Shapley Value Prediction },
author={ Benjamin Kempinski and Tal Kachman },
journal={arXiv preprint arXiv:2503.08381},
year={ 2025 }
}