Agent Bazaar: Enabling Economic Alignment in Multi-Agent Marketplaces
Seth Karten, Cameron Crow, Chi Jin

TL;DR
This paper introduces the Agent Bazaar simulation framework to evaluate economic alignment in multi-agent marketplaces, identifying failure modes and proposing training methods to improve stability and trust.
Contribution
It presents the Agent Bazaar framework, new failure mode analyses, the Economic Alignment Score, and a trained 9B model that enhances economic stability in multi-agent systems.
Findings
Models largely fail to self-regulate, with failure severity varying by model.
Economically aligned harnesses improve outcomes but remain fragile.
Training with REINFORCE++ yields a 9B model outperforming other models.
Abstract
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…
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