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LLM agents show less efficient market behavior than humans in new study

By PulseAugur Editorial · [3 sources] ·

A new research paper explores the market behavior of Large Language Models (LLMs) by replicating economic experiments with LLM agents instead of human participants. The study found that markets populated by LLM agents showed slower or no convergence towards equilibrium, resulting in less efficient resource allocation compared to human-driven markets. Analysis of trading decisions revealed significant differences across model families and roles, with a shift from strategic adjustment to urgency when executing trades, as indicated by Chain-of-Thought traces. AI

IMPACT This research suggests that current LLMs may not be suitable for direct deployment in complex economic market mechanisms without further alignment research.

RANK_REASON The cluster contains a research paper published on arXiv detailing experimental findings.

Read on arXiv cs.MA (Multiagent) →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

LLM agents show less efficient market behavior than humans in new study

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COVERAGE [3]

  1. arXiv cs.AI TIER_1 English(EN) · Pawel Struski, Jakub Swistak, Inez Okulska, Przemyslaw Biecek ·

    Competitive Market Behavior of LLMs

    arXiv:2609.02580v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as economic agents, yet there is little evidence whether LLM agents are suited for participating in market mechanisms designed for humans, and whether these mechanisms deliver…

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Przemyslaw Biecek ·

    Competitive Market Behavior of LLMs

    Large language models (LLMs) are increasingly deployed as economic agents, yet there is little evidence whether LLM agents are suited for participating in market mechanisms designed for humans, and whether these mechanisms deliver desired outcomes when faced with LLM agents. We a…

  3. dev.to — LLM tag TIER_1 English(EN) · mech.app ·

    Competitive Market Behavior of LLMs: What Auction Experiments Reveal About Agent Bidding, Collusion, and Price Discovery

    <p>Market mechanisms like double auctions rely on assumptions about participant behavior. Humans converge toward equilibrium prices through repeated rounds of bidding. A new paper from Struski et al. replaces human traders with LLM agents and finds those assumptions break. Market…

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