neuralFOMO: Can LLMs Handle Being Second Best? Measuring Envy-Like Preferences in Multi-Agent Settings
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866914279602520064 |
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| author | Ramamoorthy, Arnav Dhorajiya, Shrey Pungalia, Ojas Upadhyay, Rashi Mishra, Abhishek H, Abhiram Alladi, Tejasvi Yenuganti, Sujan Kumar, Dhruv |
| author_facet | Ramamoorthy, Arnav Dhorajiya, Shrey Pungalia, Ojas Upadhyay, Rashi Mishra, Abhishek H, Abhiram Alladi, Tejasvi Yenuganti, Sujan Kumar, Dhruv |
| contents | Envy shapes competitiveness and cooperation in human groups, yet its role in large language model interactions remains largely unexplored. As LLMs increasingly operate in multi-agent settings, it is important to examine whether they exhibit envy-like preferences under social comparison. We evaluate LLM behavior across two scenarios: (1) a point-allocation game testing sensitivity to relative versus absolute payoff, and (2) comparative evaluations across general and contextual settings. To ground our analysis in psychological theory, we adapt four established psychometric questionnaires spanning general, domain-specific, workplace, and sibling-based envy. Our results reveal heterogeneous envy-like patterns across models and contexts, with some models sacrificing personal gain to reduce a peer's advantage, while others prioritize individual maximization. These findings highlight competitive dispositions as a design and safety consideration for multi-agent LLM systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_13481 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | neuralFOMO: Can LLMs Handle Being Second Best? Measuring Envy-Like Preferences in Multi-Agent Settings Ramamoorthy, Arnav Dhorajiya, Shrey Pungalia, Ojas Upadhyay, Rashi Mishra, Abhishek H, Abhiram Alladi, Tejasvi Yenuganti, Sujan Kumar, Dhruv Artificial Intelligence Computation and Language Computers and Society Envy shapes competitiveness and cooperation in human groups, yet its role in large language model interactions remains largely unexplored. As LLMs increasingly operate in multi-agent settings, it is important to examine whether they exhibit envy-like preferences under social comparison. We evaluate LLM behavior across two scenarios: (1) a point-allocation game testing sensitivity to relative versus absolute payoff, and (2) comparative evaluations across general and contextual settings. To ground our analysis in psychological theory, we adapt four established psychometric questionnaires spanning general, domain-specific, workplace, and sibling-based envy. Our results reveal heterogeneous envy-like patterns across models and contexts, with some models sacrificing personal gain to reduce a peer's advantage, while others prioritize individual maximization. These findings highlight competitive dispositions as a design and safety consideration for multi-agent LLM systems. |
| title | neuralFOMO: Can LLMs Handle Being Second Best? Measuring Envy-Like Preferences in Multi-Agent Settings |
| topic | Artificial Intelligence Computation and Language Computers and Society |
| url | https://arxiv.org/abs/2512.13481 |