Inducing State Anxiety in LLM Agents Reproduces Human-Like Biases in Consumer Decision-Making
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911196910714880 |
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| author | Ben-Zion, Ziv Elyoseph, Zohar Spiller, Tobias Lazebnik, Teddy |
| author_facet | Ben-Zion, Ziv Elyoseph, Zohar Spiller, Tobias Lazebnik, Teddy |
| contents | 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<0.001; Cohens d=-1.07 to -2.05), robust across models and budgets. These results show that psychological context can systematically alter not only what LLMs generate but also the actions they perform. By reproducing human-like emotional biases in consumer behavior, LLM agents reveal a new class of vulnerabilities with implications for digital health, consumer safety, and ethical AI deployment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_06222 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Inducing State Anxiety in LLM Agents Reproduces Human-Like Biases in Consumer Decision-Making Ben-Zion, Ziv Elyoseph, Zohar Spiller, Tobias Lazebnik, Teddy Human-Computer Interaction General Economics Economics 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<0.001; Cohens d=-1.07 to -2.05), robust across models and budgets. These results show that psychological context can systematically alter not only what LLMs generate but also the actions they perform. By reproducing human-like emotional biases in consumer behavior, LLM agents reveal a new class of vulnerabilities with implications for digital health, consumer safety, and ethical AI deployment. |
| title | Inducing State Anxiety in LLM Agents Reproduces Human-Like Biases in Consumer Decision-Making |
| topic | Human-Computer Interaction General Economics Economics |
| url | https://arxiv.org/abs/2510.06222 |