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LLM agents show less efficient market behavior than humans in study

By PulseAugur Editorial · [2 sources] ·

A new study published on arXiv explores the behavior of large language models (LLMs) when placed in a competitive market environment. Researchers found that LLM agents, when used in a double auction mechanism, did not converge to market equilibrium as efficiently as human participants. Analysis of their trading decisions and Chain-of-Thought traces revealed a shift from strategic considerations to urgency when executing trades, indicating a difference in decision-making processes compared to humans. The study also released a testing framework for future evaluations of LLM agents in economic settings. AI

IMPACT Suggests current LLMs may not be directly substitutable for humans in all economic market mechanisms.

RANK_REASON Academic paper on LLM behavior in economic simulations. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.MA (Multiagent) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LLM agents show less efficient market behavior than humans in study

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Academic paper on LLM behavior in economic simulations. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [2]

  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…

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