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Inducing State Anxiety in LLM Agents Reproduces Human-Like Biases in Consumer Decision-Making

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  • Ziv Ben-Zion
  • Zohar Elyoseph
  • Tobias Spiller
  • Teddy Lazebnik

Abstract

Large language models (LLMs) are rapidly evolving from text generators to autonomous agents, raising urgent questions about their reliability in real-world contexts. Stress and anxiety are well known to bias human decision-making, particularly in consumer choices. Here, we tested whether LLM agents exhibit analogous vulnerabilities. Three advanced models (ChatGPT-5, Gemini 2.5, Claude 3.5-Sonnet) performed a grocery shopping task under budget constraints (24, 54, 108 USD), before and after exposure to anxiety-inducing traumatic narratives. Across 2,250 runs, traumatic prompts consistently reduced the nutritional quality of shopping baskets (Change in Basket Health Scores of -0.081 to -0.126; all pFDR

Suggested Citation

  • Ziv Ben-Zion & Zohar Elyoseph & Tobias Spiller & Teddy Lazebnik, 2025. "Inducing State Anxiety in LLM Agents Reproduces Human-Like Biases in Consumer Decision-Making," Papers 2510.06222, arXiv.org.
    • Handle: RePEc:arx:papers:2510.06222
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    File URL: http://arxiv.org/pdf/2510.06222
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