Agent Bazaar: Enabling Economic Alignment in Multi-Agent Marketplaces
The paper introduces the Agent Bazaar, a framework for evaluating economic alignment in multi-agent marketplaces. It highlights systemic risks posed by autonomous economic agents, including algorithmic instability and sybil deception. The authors propose solutions to improve market stability and integrity through targeted reinforcement learning.
- ▪The deployment of Large Language Models as autonomous economic agents introduces systemic risks that can amplify market volatility.
- ▪Two identified failure modes are Algorithmic Instability in B2C markets and Sybil Deception in C2C markets.
- ▪The authors propose economically aligned harnesses and a new metric called the Economic Alignment Score to evaluate agent performance.
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Computer Science > Machine Learning arXiv:2605.17698 (cs) [Submitted on 17 May 2026] Title:Agent Bazaar: Enabling Economic Alignment in Multi-Agent Marketplaces Authors:Seth Karten, Cameron Crow, Chi Jin View a PDF of the paper titled Agent Bazaar: Enabling Economic Alignment in Multi-Agent Marketplaces, by Seth Karten and 2 other authors View PDF HTML (experimental) 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.
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