SidConArena: An Environment Evaluating Agents in Open-Ended,Positive-Sum Bargaining Game
On the map: LLM agents and tool use → Social simulation and multi-agent debate
Abstract (arXiv)
Evaluating LLM agents requires dynamic environments that go beyond static reasoning and zero-sum games. Real-world economic interaction is often open-ended and mixed-motive: agents must negotiate, create positive-sum surplus, compete for scarce assets, and plan under delayed returns. We introduce SidConArena, a new benchmark framework for evaluating LLM agents in open-ended, positive-sum bargaining. SidConArena formalizes a multi-player economy as a finite-horizon partially observable stochastic game with three coupled phases: natural-language negotiation with binding trades, deterministic converter-based production, and sealed-bid auctions for long-term assets. The framework combines structured observations, phase-aware agent dispatching, a neural-symbolic action interface, and asynchronous execution, enabling free-form interaction while preserving rule-grounded evaluation. Across homogeneous and heterogeneous tournaments, stronger frontier models achieve higher economic outcomes, yet agents still misvalue resources, bargain passively, and remain limited in long-horizon investment planning.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 19 of its 29 references that are in the map. First posted on arXiv 24 Jun 2026.
Builds on
19 of 29 references.
Closest ancestors · 5
Same direction, newest first
- 2025A Visualized Framework for Event Cooperation with Generative Agents16 Sep
- 2025Can A Society of Generative Agents Simulate Human Behavior and Inform Public Health Policy? A Case Study on Vaccine Hesitancy12 Mar
- 2024How Well Can LLMs Negotiate? NegotiationArena Platform and Analysis8 Feb
- 2023Generative agent-based modeling with actions grounded in physical, social, or digital space using Concordia6 Dec
- 2023Generative Agents: Interactive Simulacra of Human Behavior7 Apr
Other references · 14
- 2025GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Modelscited by 446
- 2023SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agentscited by 350
- 2023NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmarkcited by 339
- 2024AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agentscited by 319
- 2023Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectivescited by 253
- 2023Language Agents with Reinforcement Learning for Strategic Play in the Werewolf Gamecited by 148
- 2023SmartPlay: A Benchmark for LLMs as Intelligent Agentscited by 123
- 2024GameBench: Evaluating Strategic Reasoning Abilities of LLM Agentscited by 89
- 2023Large Language Models Play StarCraft II: Benchmarks and A Chain of Summarization Approachcited by 79
- 2019No Press Diplomacy: Modeling Multi-Agent Gameplaycited by 37
- 2024Simulating Financial Market via Large Language Model based Agentscited by 25
- 2024Do These LLM Benchmarks Agree? Fixing Benchmark Evaluation with BenchBenchcited by 25
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2026TERMS-Bench: Diagnosing LLM Negotiation Agents Beyond Deal Rate
- 2025Strategic Tradeoffs Between Humans and AI in Multi-Agent Bargaining
- 2026Cattle Trade: A Multi-Agent Benchmark for LLM Bluffing, Bidding, and Bargaining
- 2026CoffeeBench: Benchmarking Long-Horizon LLM Agents in Heterogeneous Multi-Agent Economies
- 2025The Illusion of Rationality: Tacit Bias and Strategic Dominance in Frontier LLM Negotiation Games
- 2026Training Language Models for Bilateral Trade with Private Information
- 2025LLM Agents for Bargaining with Utility-based Feedback
- 2024Game-theoretic LLM: Agent Workflow for Negotiation Games