How can I quickly calculate agent influence in large multi-agent systems?
InfluenceNet: AI Models for Banzhaf and Shapley Value Prediction
March 12, 2025
https://arxiv.org/pdf/2503.08381This paper introduces InfluenceNet, a neural network-based approach to efficiently estimate Banzhaf and Shapley-Shubik power indices in multi-agent voting systems. These indices quantify the influence of individual agents within a coalition, but traditional calculation methods are computationally expensive. InfluenceNet offers a faster alternative, especially for large-scale systems, making power index analysis more accessible.
Key points for LLM-based multi-agent systems:
- Scalability: InfluenceNet efficiently handles large numbers of agents, surpassing traditional methods which struggle with exponential complexity. This scalability is crucial for complex multi-agent applications that LLMs enable.
- Power Distribution Analysis: Understanding power dynamics within LLM-driven multi-agent systems is critical for optimizing interactions and decision-making. InfluenceNet provides a way to analyze agent influence and potential bottlenecks.
- Coalition Formation and Dynamics: While the current research focuses on static systems, future work aims to extend InfluenceNet to dynamic scenarios, aligning with the dynamic nature of many LLM-based multi-agent applications.
- Generalizability: While the model's robustness to varying coalition densities needs improvement, the ability of a neural network to approximate power indices opens up avenues for analyzing more complex and realistic multi-agent scenarios powered by LLMs.