Adaptive Multi-Agent Negotiation Framework for Decentralized Markets

This paper presents a revised decentralized negotiation framework for local energy and resource markets. Building on our original proposal, we extend Mean-Field Game (MFG) techniques to account for finite, heterogeneous agent populations and incorporate mean-field-type games (MFTGs) to relax the assumption of anonymity. We integrate reinforcement learning to adapt strategies to real-time price feedback and market conditions and employ heteroscedastic probabilistic forecasting to explicitly model uncertainty stemming from intermittent renewable generation and stochastic demand. The trading layer is realized with improved Lightning Network protocols featuring splicing, watchtowers and secure signing. We provide convergence proofs under asynchronous communication, demonstrate cross-domain extensibility, and evaluate performance on simulated energy and logistics markets. Results show that the adaptive framework maintains millisecond-level latency, achieves 90–95% of the Pareto optimum, reduces peak demand, improves revenue, and is robust against malicious agents and liquidity constraints. We discuss regulatory, privacy and carbon-market implications.