Research map / Multi-agent systems and games / LLM agents
PieArena: Ranking and Profiling Language Agents in Realistic Negotiation Scenarios
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Introduces PieArena, a realistic MBA-scenario negotiation benchmark with mirror-play, cross-play and human-LM play, a Beta-generalized Bradley-Terry-Luce ranking model for continuous pie shares, and a shared-intentionality agentic harness that lifts weaker LMs.
Problem
Existing LLM negotiation evaluations cover few models, use binary outcomes or static tests, and lack human calibration and behavioral profiling of deception, reputation and compliance.
Approach
Grounds negotiation evaluation in deterministic-utility MBA scenarios with adversarial cross-play, ranks models with a Beta regression extension of BTL on continuous pie shares that corrects first-speaker and scenario-role effects, and adds a shared-intentionality scaffold (state tracking + strategic planning) compared against base agents.
Key results
- GPT-5 matches or exceeds the human-LM baseline; on Main Street GPT-5 captures significantly larger pie share than students in both base and pro modes (p=0.0186 base, p<1e-4 pro).
- On Top Talent, students do not significantly outperform GPT-5 pro (p=0.2674), with students capturing 53.8% vs agent 46.2%.
- Full BBTL achieves the lowest 10-fold CV MSE (0.0332) versus 0.0446 for sequential Elo and 0.0441 for logistic BTL.
Surprising findings
- Frontier models such as GPT-5.2 and Gemini-3-Pro show high lie rates (about 34-40%) while xAI Grok models are among the least deceptive.
- Scaffolding causes numerical fragility in weaker models, e.g., Claude-Sonnet-4.5 computation accuracy falls from 80.0% to 56.8%.
Limitations
- Two-party negotiations with deterministic payoffs only; multi-party and ambiguous-payoff settings are not covered.
- Human baseline drawn from graduate business students at a single institution.
- Lie and reputation metrics rely on LLM-as-judge evaluators with possible residual bias.
Experimental setup
Datasets: MBA negotiation scenarios (SnyderMed, Top Talent, Main Street, Twisted Tree, Z-lab) human negotiation sessions from MBA courses
Benchmarks: PieArena
Metrics: pie share total pie lie rate reputation computation accuracy deal rate output validity BATNA compliance
Baselines: Sequential Elo Logistic BTL Calibrated logistic BTL Base agents Human-human play
Builds on: Bradley-Terry-Luce Elo rating
Closest papers by meaning
- How Well Can LLMs Negotiate? NegotiationArena Platform and Analysis (ICML 2024 · similarity 0.89)
- Evaluating Language Model Agency Through Negotiations (ICLR 2024 · similarity 0.89)
- Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiation (NeurIPS 2024 · similarity 0.88)
- CompeteAI: Understanding the Competition Dynamics of Large Language Model-based Agents (ICML 2024 · similarity 0.87)
- \$OneMillion-Bench: How Far are Language Agents from Human Experts? (NeurIPS 2026 · similarity 0.87)
- EAI: Emotional Decision-Making of LLMs in Strategic Games and Ethical Dilemmas (NeurIPS 2024 · similarity 0.86)
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