@inproceedings{liu-etal-2026-mechanism,
title = "Mechanism Alignment for Socially Embedded Decision Making in {LLM}s",
author = "Liu, Jiaxin and
Wu, Yuhe and
Xie, Yixuan and
Xiao, Hanzhi and
ma, Zeyu and
GU, Wenqing and
Xian, Zeheng and
Zhang, Guang",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.468/",
pages = "10759--10779",
ISBN = "979-8-89176-459-0",
abstract = "LLM agents are widely used in social simulation, yet fluent social reasoning does not guarantee valid decision mechanisms. We test whether models implement socially embedded utility, where actions depend on peer-weighted references rather than isolated private preference. We introduce a theory-guided benchmark from network utility theory covering utility recovery, social-comparison gating, and structural transfer across two out-of-distribution decision forms. Across open-source and proprietary models, we find a gap between cue recognition and peer aggregation, spurious social activation when comparison should be inactive, and declining accuracy as social dependence rises. We propose theory-supervised mechanism alignment trained on latent utility states. Fine-tuning yields +8.3 and +13.9 utility-recovery points on Llama-3.1-8B and Qwen3-8B, raises gating above 95{\%}, and improves transfer to unseen structures. Multi-agent simulations show that repairing local utility shifts macro outcomes, including stronger financial amplification and more stable labor stratification, which indicates that realistic macro-level simulations require adjustments at the individual level. Data and code are available at https://github.com/saaranLiu/SEDM-alignment."
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<abstract>LLM agents are widely used in social simulation, yet fluent social reasoning does not guarantee valid decision mechanisms. We test whether models implement socially embedded utility, where actions depend on peer-weighted references rather than isolated private preference. We introduce a theory-guided benchmark from network utility theory covering utility recovery, social-comparison gating, and structural transfer across two out-of-distribution decision forms. Across open-source and proprietary models, we find a gap between cue recognition and peer aggregation, spurious social activation when comparison should be inactive, and declining accuracy as social dependence rises. We propose theory-supervised mechanism alignment trained on latent utility states. Fine-tuning yields +8.3 and +13.9 utility-recovery points on Llama-3.1-8B and Qwen3-8B, raises gating above 95%, and improves transfer to unseen structures. Multi-agent simulations show that repairing local utility shifts macro outcomes, including stronger financial amplification and more stable labor stratification, which indicates that realistic macro-level simulations require adjustments at the individual level. Data and code are available at https://github.com/saaranLiu/SEDM-alignment.</abstract>
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%0 Conference Proceedings
%T Mechanism Alignment for Socially Embedded Decision Making in LLMs
%A Liu, Jiaxin
%A Wu, Yuhe
%A Xie, Yixuan
%A Xiao, Hanzhi
%A ma, Zeyu
%A GU, Wenqing
%A Xian, Zeheng
%A Zhang, Guang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F liu-etal-2026-mechanism
%X LLM agents are widely used in social simulation, yet fluent social reasoning does not guarantee valid decision mechanisms. We test whether models implement socially embedded utility, where actions depend on peer-weighted references rather than isolated private preference. We introduce a theory-guided benchmark from network utility theory covering utility recovery, social-comparison gating, and structural transfer across two out-of-distribution decision forms. Across open-source and proprietary models, we find a gap between cue recognition and peer aggregation, spurious social activation when comparison should be inactive, and declining accuracy as social dependence rises. We propose theory-supervised mechanism alignment trained on latent utility states. Fine-tuning yields +8.3 and +13.9 utility-recovery points on Llama-3.1-8B and Qwen3-8B, raises gating above 95%, and improves transfer to unseen structures. Multi-agent simulations show that repairing local utility shifts macro outcomes, including stronger financial amplification and more stable labor stratification, which indicates that realistic macro-level simulations require adjustments at the individual level. Data and code are available at https://github.com/saaranLiu/SEDM-alignment.
%U https://aclanthology.org/2026.emnlp-main.468/
%P 10759-10779
Markdown (Informal)
[Mechanism Alignment for Socially Embedded Decision Making in LLMs](https://aclanthology.org/2026.emnlp-main.468/) (Liu et al., EMNLP 2026)
ACL
- Jiaxin Liu, Yuhe Wu, Yixuan Xie, Hanzhi Xiao, Zeyu ma, Wenqing GU, Zeheng Xian, and Guang Zhang. 2026. Mechanism Alignment for Socially Embedded Decision Making in LLMs. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 10759–10779, Budapest, Hungary. Association for Computational Linguistics.