The deployment of Large Language Models (LLMs) as autonomous economic agents introduces systemic risks that extend beyond individual capability failures. As agents transition to directly interacting with marketplaces, their collective behavior can amplify volatility and mask deception at scale. We introduce the Agent Bazaar, a multi-agent simulation framework for evaluating Economic Alignment, the capacity of agentic systems to preserve market stability and integrity. We identify two failure modes: (1) Algorithmic Instability in a B2C market ("The Crash"), where firms amplify price volatility until the market collapses, and (2) Sybil Deception in a C2C market ("The Lemon Market"), where a single deceptive agent controlling multiple coordinated seller identities floods the market with fraudulent listings, eroding trust and consumer welfare. We evaluate frontier and open-weight models across both scenarios and find that models largely fail to self-regulate, with failure severity varying by model rather than by size. We propose economically aligned harnesses, Stabilizing Firms and Skeptical Guardians, that improve outcomes but remain fragile under harder market conditions. To close this gap, we train agents with REINFORCE++ using an adaptive curriculum, producing a 9B model that outperforms all evaluated frontier and open-weight models. We propose the Economic Alignment Score (EAS), a 4-component scalar metric aggregating stability, integrity, welfare, and profitability, enabling direct cross-model comparison. Our results show that economic alignment is orthogonal to general capability and can be directly trained with targeted RL.

翻译：大规模语言模型作为自主经济智能体的部署引入了超越个体能力失效的系统性风险。当智能体转向直接与市场交互时，其集体行为可能放大波动性并大规模掩盖欺诈行为。我们提出Agent Bazaar这一多智能体仿真框架，用于评估"经济对齐"能力——即智能体系统维护市场稳定性与完整性的能力。我们识别出两种失效模式：（1）B2C市场中的算法性不稳定（"崩盘"），即企业行为放大价格波动直至市场崩溃；以及（2）C2C市场中的女巫欺骗（"柠檬市场"），即单个欺骗性智能体通过控制多个协调的卖方身份，用虚假商品清单淹没市场，侵蚀信任与消费者福利。我们在两种场景下评估前沿模型与开源权重模型，发现模型普遍无法实现自我监管，且失效严重程度因模型而异而非规模决定。我们提出经济对齐的约束机制——稳定企业与怀疑守护者——虽能改善结果，但在更严峻的市场条件下仍显脆弱。为缩小这一差距，我们采用自适应课程学习结合REINFORCE++算法训练智能体，产生了在性能上超越所有被评估前沿与开源权重模型的9B参数模型。我们提出经济对齐分数（EAS），一种包含稳定性、完整性、福利与盈利能力四个分量的标量指标，可直接实现跨模型比较。结果表明，经济对齐与通用能力正交，可通过定向强化学习直接训练获得。