# InfluenceNet: AI Models for Banzhaf and Shapley Value Prediction

arXiv: https://arxiv.org/abs/2503.08381 · HTML: https://mlsanity.com/paper/2503.08381/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 77 references that are in the map.
- First posted on arXiv 11 Mar 2025.

## Builds on (most cited first, 12 of 12)

- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Data Shapley: Equitable Valuation of Data for Machine Learning](https://mlsanity.com/paper/1904.02868/) (2019)
- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [Why do tree-based models still outperform deep learning on tabular data?](https://mlsanity.com/paper/2207.08815/) (2022)
- [Data Banzhaf: A Robust Data Valuation Framework for Machine Learning](https://mlsanity.com/paper/2205.15466/) (2022)
- [The Shapley Value in Machine Learning](https://mlsanity.com/paper/2202.05594/) (2022)
- [The Shapley Value of Tuples in Query Answering](https://mlsanity.com/paper/1904.08679/) (2019)
- [Efficient Computation of the Shapley Value for Game-Theoretic Network Centrality](https://mlsanity.com/paper/1402.0567/) (2014)
- [Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members](https://mlsanity.com/paper/2208.08798/) (2022)
- [Expected Shapley-Like Scores of Boolean Functions: Complexity and Applications to Probabilistic Databases](https://mlsanity.com/paper/2401.06493/) (2024)
- [Robotic Lever Manipulation using Hindsight Experience Replay and Shapley Additive Explanations](https://mlsanity.com/paper/2110.03292/) (2021)
- [Sharing Rewards in Cooperative Connectivity Games](https://mlsanity.com/paper/1402.0572/) (2014)

## Abstract (arXiv)

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 (n≥10) 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 analytical tool.This increased efficiency will allow for the analysis of more complex and realistic multi-agent scenarios.