Can decentralized agents reach equilibrium prices through bilateral negotiation?
Decentralized Convergence to Equilibrium Prices in Trading Networks
December 19, 2024
https://arxiv.org/pdf/2412.13972This paper studies how prices reach equilibrium in decentralized markets where agents negotiate bilaterally, like in over-the-counter trading. It proposes a "best response" dynamic where agents iteratively update their offers to maximize individual utility, eventually reaching a stable state.
Key points for LLM-based multi-agent systems:
- Decentralized negotiation: The model simulates price discovery through uncoordinated agent interactions, relevant to multi-agent systems where central control is undesirable or infeasible.
- Best response dynamics: Agents iteratively improve their offers based on observed counter-offers, mirroring learning and adaptation in multi-agent LLMs.
- Convergence analysis: Theoretical and experimental results demonstrate convergence to equilibrium under certain conditions (e.g., fully substitutable preferences, sparse market networks), offering insights into the stability and predictability of multi-agent LLM interactions.
- Shock propagation: Experiments show how external changes propagate through the network and affect convergence, important for understanding the robustness and resilience of multi-agent systems.
- Welfare analysis: The study examines how agent composition impacts welfare, relevant to designing multi-agent systems that optimize for overall system performance.