How does learning speed impact coordination in multi-agent systems?
Inertial Coordination Games
September 13, 2024
https://arxiv.org/pdf/2409.08145This paper examines "inertial coordination games," where agents repeatedly decide to take a risky action or not, with payoffs depending on a hidden fundamental state and past actions of others.
For LLM-based multi-agent systems, the key point is the impact of learning speed on system behavior:
- Slow learning (sub-quadratic growth of knowledge precision) leads to the "risk-dominant" outcome prevailing, regardless of the initial conditions. This means the system will settle on the safer, more predictable outcome.
- Fast learning (super-quadratic growth of precision) makes the system sensitive to initial conditions and potentially leads to "non-risk dominant" outcomes. Initial decisions by agents can have a lasting impact, even if those decisions were based on limited information.
This implies that controlling the rate at which LLMs learn and share information in a multi-agent system can be crucial in shaping the system's long-term behavior and achieving desired outcomes.