@inproceedings{cheng-etal-2025-observing,
title = "Observing Micromotives and Macrobehavior of Large Language Models",
author = "Cheng, Yuyang and
Qu, Xingwei and
Goldsack, Tomas and
Lin, Chenghua and
Chen, Chung-Chi",
editor = "Inui, Kentaro and
Sakti, Sakriani and
Wang, Haofen and
Wong, Derek F. and
Bhattacharyya, Pushpak and
Banerjee, Biplab and
Ekbal, Asif and
Chakraborty, Tanmoy and
Singh, Dhirendra Pratap",
booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
url = "https://aclanthology.org/2025.ijcnlp-long.69/",
pages = "1258--1276",
ISBN = "979-8-89176-298-5",
abstract = "Thomas C. Schelling, awarded the 2005 Nobel Memorial Prize in Economic Sciences, pointed out that ``individuals decisions (micromotives), while often personal and localized, can lead to societal outcomes (macrobehavior) that are far more complex and different from what the individuals intended.'' The current research related to large language models' (LLMs') micromotives, such as preferences or biases, assumes that users will make more appropriate decisions once LLMs are devoid of preferences or biases. Consequently, a series of studies has focused on removing bias from LLMs. In the NLP community, while there are many discussions on LLMs' micromotives, previous studies have seldom conducted a systematic examination of how LLMs may influence society{'}s macrobehavior. In this paper, we follow the design of Schelling{'}s model of segregation to observe the relationship between the micromotives and macrobehavior of LLMs. Our results indicate that, regardless of the level of bias in LLMs, a highly segregated society will emerge as more people follow LLMs' suggestions. We hope our discussion will spark further consideration of the fundamental assumption regarding the mitigation of LLMs' micromotives and encourage a reevaluation of how LLMs may influence users and society."
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<abstract>Thomas C. Schelling, awarded the 2005 Nobel Memorial Prize in Economic Sciences, pointed out that “individuals decisions (micromotives), while often personal and localized, can lead to societal outcomes (macrobehavior) that are far more complex and different from what the individuals intended.” The current research related to large language models’ (LLMs’) micromotives, such as preferences or biases, assumes that users will make more appropriate decisions once LLMs are devoid of preferences or biases. Consequently, a series of studies has focused on removing bias from LLMs. In the NLP community, while there are many discussions on LLMs’ micromotives, previous studies have seldom conducted a systematic examination of how LLMs may influence society’s macrobehavior. In this paper, we follow the design of Schelling’s model of segregation to observe the relationship between the micromotives and macrobehavior of LLMs. Our results indicate that, regardless of the level of bias in LLMs, a highly segregated society will emerge as more people follow LLMs’ suggestions. We hope our discussion will spark further consideration of the fundamental assumption regarding the mitigation of LLMs’ micromotives and encourage a reevaluation of how LLMs may influence users and society.</abstract>
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%0 Conference Proceedings
%T Observing Micromotives and Macrobehavior of Large Language Models
%A Cheng, Yuyang
%A Qu, Xingwei
%A Goldsack, Tomas
%A Lin, Chenghua
%A Chen, Chung-Chi
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-298-5
%F cheng-etal-2025-observing
%X Thomas C. Schelling, awarded the 2005 Nobel Memorial Prize in Economic Sciences, pointed out that “individuals decisions (micromotives), while often personal and localized, can lead to societal outcomes (macrobehavior) that are far more complex and different from what the individuals intended.” The current research related to large language models’ (LLMs’) micromotives, such as preferences or biases, assumes that users will make more appropriate decisions once LLMs are devoid of preferences or biases. Consequently, a series of studies has focused on removing bias from LLMs. In the NLP community, while there are many discussions on LLMs’ micromotives, previous studies have seldom conducted a systematic examination of how LLMs may influence society’s macrobehavior. In this paper, we follow the design of Schelling’s model of segregation to observe the relationship between the micromotives and macrobehavior of LLMs. Our results indicate that, regardless of the level of bias in LLMs, a highly segregated society will emerge as more people follow LLMs’ suggestions. We hope our discussion will spark further consideration of the fundamental assumption regarding the mitigation of LLMs’ micromotives and encourage a reevaluation of how LLMs may influence users and society.
%U https://aclanthology.org/2025.ijcnlp-long.69/
%P 1258-1276
Markdown (Informal)
[Observing Micromotives and Macrobehavior of Large Language Models](https://aclanthology.org/2025.ijcnlp-long.69/) (Cheng et al., IJCNLP-AACL 2025)
ACL
- Yuyang Cheng, Xingwei Qu, Tomas Goldsack, Chenghua Lin, and Chung-Chi Chen. 2025. Observing Micromotives and Macrobehavior of Large Language Models. In Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, pages 1258–1276, Mumbai, India. The Asian Federation of Natural Language Processing and The Association for Computational Linguistics.