How can I build robust multi-agent game equilibria?
Characterising Simulation-Based Program Equilibria
This paper explores how AI agents can interact strategically when they can see each other's code ("program equilibrium"). It proposes a new type of AI agent, a generalized "epsilon-Grounded Bot," which is more robust and versatile than previous approaches. These bots simulate interactions in a simplified repeated game-like manner to choose actions.
For LLM-based multi-agent systems, this work offers insights into designing robust agent interactions even when LLMs have access to each other's prompts (analogous to code). The "epsilon-Grounded Bot" approach provides a more practical method for achieving cooperation in such scenarios compared to brittle methods relying on exact prompt comparison. It also shows that without shared randomness, full cooperation may be difficult to attain, which is a relevant constraint for many decentralized web applications. The work introduces the concept of "screening," analogous to parts of an LLM prompt being hidden from other LLMs, and explores how this impacts attainable equilibria, which is directly relevant to prompt engineering practices in multi-agent LLM scenarios.