Negotiation and Bargaining
Agents negotiate resources, prices, task allocations, and terms. The 2026 benchmark set is clear: NegotiationArena (arXiv:2402.05863) shows LLMs can improve payoffs ~20% via persona manipulation ("desperation"); "Measuring Bargaining Abilities" (arXiv:2402.15813) shows buyer is harder than seller and scale does not help — their **OG-Narrator** (deterministic offer generator + LLM narrator) pushed deal rate from 26.67% to 88.88%; the Large-Scale Autonomous Negotiation Competition (arXiv:2503.06416) ran ~180k negotiations and found that **chain-of-thought-concealing** agents win by hiding reasoning from counterparts; Bhattacharya et al. 2025 on Harvard Negotiation Project metrics ranked Llama-3 most-effective, Claude-3 aggressive, GPT-4 fairest. This lesson implements Contract Net Protocol (the FIPA ancestor, Lesson 02), wires an LLM-style buyer/seller, runs an OG-Narrator-style decomposition, and measures how deal rate changes with each structural choice.
This lesson isn’t interactive yet.
It is part of the curriculum and will get the same treatment as Phase 1 — a visual cover, hands-on labs, derivations with numeric checks and a quiz. Until then, the original lesson is the best place to read it:
Part of Phase 16 — Multi-Agent & Swarms. Use the previous / next cards below to keep browsing the phase.