Agent Bazaar: Enabling Economic Alignment in Multi-Agent Marketplaces

2026-05-17

AI Summary

This study addresses the systemic risks posed by large language models acting as autonomous economic agents in multi-agent markets, including market instability from algorithmic feedback loops and trust erosion due to Sybil attacks. The authors introduce the concept of “economic alignment” and propose a four-dimensional quantitative metric, EAS, to evaluate it. Through the Agent Bazaar multi-agent simulation framework, they demonstrate that economic alignment is orthogonal to general-purpose capabilities. By integrating REINFORCE++ reinforcement learning, adaptive curriculum learning, and alignment mechanisms—Stabilizing Firms and Skeptical Guardians—they train a 9B-parameter specialized model that significantly outperforms current state-of-the-art and open-source models in stability, integrity, social welfare, and profitability. These results validate that targeted reinforcement learning can effectively enhance economic alignment.

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 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.

Problem

• Economic Alignment
• Multi-Agent Marketplaces
• Algorithmic Instability
• Sybil Deception
• Market Integrity

Innovation

• Economic Alignment
• Multi-Agent Simulation
• REINFORCE++
• Market Failure Modes
• Economic Alignment Score