[2511.12876] Think, Speak, Decide: Language-Augmented Multi-Agent Reinforcement Learning for Economic Decision-Making

Computer Science > Artificial Intelligence arXiv:2511.12876 (cs) [Submitted on 17 Nov 2025 (v1), last revised 21 Mar 2026 (this version, v4)] Title:Think, Speak, Decide: Language-Augmented Multi-Agent Reinforcement Learning for Economic Decision-Making Authors:Heyang Ma, Qirui Mi, Qipeng Yang, Zijun Fan, Bo Li, Haifeng Zhang View a PDF of the paper titled Think, Speak, Decide: Language-Augmented Multi-Agent Reinforcement Learning for Economic Decision-Making, by Heyang Ma and 5 other authors View PDF HTML (experimental) Abstract:Economic decision-making depends not only on structured signals such as prices and taxes, but also on unstructured language, including peer dialogue and media narratives. While multi-agent reinforcement learning (MARL) has shown promise in optimizing economic decisions, it struggles with the semantic ambiguity and contextual richness of language. We propose LAMP (Language-Augmented Multi-Agent Policy), a framework that integrates language into economic decision-making and narrows the gap to real-world settings. LAMP follows a Think-Speak-Decide pipeline: (1) Think interprets numerical observations to extract short-term shocks and long-term trends, caching high-value reasoning trajectories; (2) Speak crafts and exchanges strategic messages based on reasoning, updating beliefs by parsing peer communications; and (3) Decide fuses numerical data, reasoning, and reflections into a MARL policy to optimize language-augmented decision-making. Experiments i...