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AI and the transformation of social science research
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Careful bias management and data fidelity are key
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In the citing paper
“Researchers have also recently noted GPT’s ability to help with computational social science tasks (49–54), detect misinformation (55), infer politicians’ ideologies (56), write persuasive political arguments (57), respond to patient questions (58), simulate human research participants (59–61), and model collective behavior (56).”
From GPT is an effective tool for multilingual psychological text analysis (2024) · cites this paper as [53]
In the citing paper
“Researchers who want to use LLMs to impersonate biased groups, for example, may discover these tools are unwilling to perform such roles because they have been fine-tuned according to the normative preferences of highly educated liberals who may have more concern about the protection of marginalized groups than others (12, 64).”
From Can Generative AI improve social science? (2024) · cites this paper as [12]
In the citing paper
“Though such guard-rails probably improve the safety of Generative AI tools for public use, they may impede the ability of social scientists to leverage bias for research purposes (12).”
From Can Generative AI improve social science? (2024) · cites this paper as [12]
In the citing paper
“Though Generative AI may help researchers increase the external validity of their research designs, this may come at the cost of internal validity—or the capacity of different groups of researchers to reproduce or replicate each other’s results (2, 12).”
From Can Generative AI improve social science? (2024) · cites this paper as [12]
Citations
Cites
Generative Agents: Interactive Simulacra of Human Behavior
Park, J. S., O'Brien, J. C., Cai, C. J., Morris, M. R., Liang, P., & Bernstein, M. S. (2023) · arXiv · doi:10.48550/arxiv.2304.03442 · Cited by 2,173
Pennycook, G., McPhetres, J., Zhang, Y., Lu, J. G., & Rand, D. G. (2020) · Psychological Science · doi:10.1177/0956797620939054 · Cited by 1,715
Taxonomy of Risks posed by Language Models
Weidinger, L., Uesato, J., Rauh, M., Griffin, C., Huang, P.-S., Mellor, J., Glaese, A., Cheng, M., Balle, B., Kasirzadeh, A., et al. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533088 · Cited by 764
Algorithmic bias: Senses, sources, solutions
Fazelpour, S., & Danks, D. J. (2021) · Philosophy Compass · doi:10.1111/phc3.12760 · Cited by 338
The Science of Wisdom in a Polarized World: Knowns and Unknowns
Grossmann, I., Weststrate, N. M., Ardelt, M., Brienza, J. P., Dong, M., Ferrari, M., Fournier, M. A., Hu, C. S., Nusbaum, H. C., & Vervaeke, J. (2020) · Psychological Inquiry · doi:10.1080/1047840x.2020.1750917 · Cited by 330
Self-citationLocally noisy autonomous agents improve global human coordination in network experiments
Shirado, H., & Christakis, N. A. (2017) · Nature · doi:10.1038/nature22332 · Cited by 288
Self-citationUsing large-scale experiments and machine learning to discover theories of human decision-making
Peterson, J. C., Bourgin, D., Agrawal, M., Reichman, D., & Griffiths, T. L. (2021) · Science · doi:10.1126/science.abe2629 · Cited by 273
Large language models associate Muslims with violence
Abid, A., Farooqi, M., & Zou, J. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00359-2 · Cited by 176
Integrating social and cognitive aspects of belief dynamics: towards a unifying framework
Galešić, M., Olsson, H., Dalege, J., van der Does, T., & Stein, D. L. (2021) · Journal of The Royal Society Interface · doi:10.1098/rsif.2020.0857 · Cited by 102
Psychological Perspectives on Nuclear Deterrence
Tetlock, P. E., McGuire, C., & Mitchell, G. (1991) · Annual Review of Psychology · doi:10.1146/annurev.ps.42.020191.001323 · Cited by 34
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Research works printed but not linked: 3
- S. Bubeck arXiv:2303.12712 [cs.CL] (2023). · arXiv
- L. P. Argyle Polit. Anal. 10.1017/pan.2023.2 (2023). · DOI · Paper page
- Y. Bai arXiv:2212.08073 [cs.CL] (2022). · arXiv
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Sorted by citations
Items 101–200 of 381
Leça, M. D. M., Valença, L., Santos, R., & Santos, R. D. S. (2025) · IEEE/ACM International Workshop on Methodological Issues with Empirical Studies in Software Engineering (WSESE) · doi:10.1109/wsese66602.2025.00013 · Cited by 14
“LLMs are now being used widely across research fields due to their potential to streamline text-heavy tasks [24], [27].”
Bank Run, Interrupted: Modeling Deposit Withdrawals with Generative AI
Kazinnik, S. (2026) · Management Science · doi:10.1287/mnsc.2024.06807 · Cited by 12
Shen, C., Song, Z., Ши, Л., Tanimoto, J., & Wang, Z. (2024) · Science China Information Sciences · doi:10.1007/s11432-023-4170-3 · Cited by 9
“Understanding these dynamics is essential for developing AI that integrates seamlessly into human social structures [1–3].”
Zhong, Z., Zhang, H., Ozaki, J., Zhou, Y., Zhao, X., Dan, D., & Wang, C. (2025) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2025.130791 · Cited by 9
Taskin, B., Xie, W., & Lazebnik, T. (2026) · Scientific Reports · doi:10.1038/s41598-026-35327-6 · Cited by 14
“Lastly, genetic-algorithm-based SR methods treat mathematical expressions as individuals (or genes) in a population 48 .”
Saccenti, D., Buattini, M., Grazioli, S., & Torres, D. (2024) · Possibility Studies & Society · doi:10.1177/27538699241231862 · Cited by 10
“Yet, this position has been refuted by recent works, suggesting that AI models can efficiently replace human subjects, by making human-like judgments across a number of domains (Dillion et al., 2023; Grossmann et al., 2023).”
Perils and Opportunities in Using Large Language Models in Psychological ResearchPreprint
Abdurahman, S., Atari, M., Karimi-Malekabadi, F., Xue, M. J., Trager, J. P., Park, P. S., Golazizian, P., Omrani, A., & Dehghani, M. (2023) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/tg79n · Cited by 10
Comments on “ AI and the advent of the cyborg behavioral scientist”
Blythe, P. A., Kulis, C., McGraw, A. P., Haenlein, M., Hewett, K., Yoo, K., Wood, S. L., Morwitz, V. G., & Huber, J. C. (2025) · Journal of Consumer Psychology · doi:10.1002/jcpy.1453 · Cited by 7
Automated vs. manual linguistic annotation for assessing pragmatic competence in English classes
Mahmoudi-Dehaki, M., & Nasr-Esfahani, N. (2025) · Research Methods in Applied Linguistics · doi:10.1016/j.rmal.2025.100253 · Cited by 8
Can Large Language Models Simulate Spoken Human Conversations?
Mayor, E., Bietti, L. M., & Bangerter, A. (2025) · Cognitive Science · doi:10.1111/cogs.70106 · Cited by 12
“The potential use of large language models (LLMs) to simulate human cognition and behavior has been heralded as an upcoming paradigm shift in psychological and social science research (Grossmann et al., 2023; Ke et al., 2024; Pickering & Garrod, 2021; Salah et al., 2023).”
Using large language models for survey research in communication: opportunities and challenges
Valenzuela, S., Winter, S., & Rivera, S. (2025) · Communication and Change · doi:10.1007/s44382-025-00014-z · Cited by 8
“Grossmann et al. (2023) highlight that the origin of such biases can be twofold: “Do they correctly reflect the populations, or are they artifacts of model construction?””
Perils and Opportunities in Using Large Language Models in Psychological ResearchPreprint
Abdurahman, S., Atari, M., Karimi-Malekabadi, F., Xue, M. J., Trager, J. P., Park, P. S., Golazizian, P., Omrani, A., & Dehghani, M. (2023) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/d695y · Cited by 8
Luke, H. (2025) · Land Use Policy · doi:10.1016/j.landusepol.2025.107526 · Cited by 7
Generalizability of choice architecture interventions
Szászi, B., Goldstein, D. G., Soman, D., & Michie, S. (2025) · Nature Reviews Psychology · doi:10.1038/s44159-025-00471-9 · Cited by 15
“For instance, large language models (LLMs) can in some cases simulate human-like responses and behaviour that potentially mimicks the response of different groups of people 151,152 and might be used to explore the generalizability of intervention effectiveness across different populations.”
Bhatia, S., van Baal, S. T., Wang, F., & Walasek, L. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2406489122 · Cited by 10
Hwang, A. H., Bernstein, M. S., Sundar, S. S., Zhang, R., Ribeiro, M. H., Lu, Y., Chang, S. Y., Wu, T., Yang, A., Williams, D. C., et al. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3716299 · Cited by 16
Exploring the dual impact of AI in post-entry language assessment: Potentials and pitfalls
Zhang, T., Erlam, R., & de Magalhães, M. B. (2025) · Annual Review of Applied Linguistics · doi:10.1017/s0267190525000030 · Cited by 8
“GenAI has impacted a range of industries, reshaping both the professional and personal domains of everyday life (Grossmann et al., 2023).”
Vogelsmeier, L. V. D. E., de Oliveira, E. A., Misiejuk, K., López‐Pernas, S., & Saqr, M. (2025) · Computers in Human Behavior · doi:10.1016/j.chb.2025.108769 · Cited by 9
“Early research has shown that LLMs are “increasingly capable of simulating human-like responses and behaviors, offering opportunities to test theories and hypotheses about human behavior at great scale and speed” (Grossmann et al. 2023, p. 1108).”
Large Language Models as Psychological Simulators: A Methodological Guide
Lin, Z. (2026) · Advances in Methods and Practices in Psychological Science · doi:10.1177/25152459251410153 · Cited by 17
“Commercial services now offer AI participants for market research, and academic proposals suggest LLMs could substitute human participants (Grossmann et al., 2023; Sarstedt et al., 2024).”
Abramson, C. M., Prendergast, T., Li, Z., & Dohan, D. P. (2026) · Annual Review of Sociology · doi:10.1146/annurev-soc-011824-104836 · Cited by 6
Simple autonomous agents can enhance creative semantic discovery by human groups
Ueshima, A., Jones, M. I., & Christakis, N. A. (2024) · Nature Communications · doi:10.1038/s41467-024-49528-y · Cited by 10
“Despite the ongoing transformation of social and computational science research by large language models 28 , here we focus on simple autonomousagentsthat workwithclassicnaturallanguageprocessing techniques and that are thus relatively transparent in nature 4,29 .”
MethodSelf-citationGuo, T., He, Z., Chen, S., Ши, Л., & Tanimoto, J. (2024) · Computational Economics · doi:10.1007/s10614-024-10559-8 · Cited by 6
“…sophisticated reinforcement learning techniques, such as Q-learning (Hu et al., 2022; Hu & Leung, 2018; Hu et al., 2019; Hu & Leung, 2017), and large language models (Grossmann et al., 2023) could be employed to manage this complexity and simulate the behavior of AAs in intricate scenarios.”
Addressing diversity in hiring procedures: a generative adversarial network approach
Marra, T., & Kubiak, E. (2024) · AI and Ethics · doi:10.1007/s43681-024-00445-2 · Cited by 6
“Machine learning holds significant potential in reshaping the landscape of personnel selection techniques [19] and social science research [57].”
Tong, J. S., Zhang, Y., & Li, Y. (2025) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2025.100123 · Cited by 6
El‐Bassel, N., David, J., Mukherjee, T. I., Aggarwal, M., Wu, E., Gilbert, L., Walters, S. T., Chandler, R. K., Hunt, T., Frye, V. A., et al. (2025) · Implementation Science · doi:10.1186/s13012-025-01447-2 · Cited by 9
“In response, social science researchers have adopted participatory approaches in AI that prioritize community co-design, transparency, and ethical over-sight [1, 8, 9].”
Arti-‘fickle’ intelligence: using LLMs as a tool for inference in the political and social sciences
Argyle, L. P., Busby, E. C., Gubler, J. R., Hepner, B., Lyman, A., & Wingate, D. (2025) · Nature Computational Science · doi:10.1038/s43588-025-00843-4 · Cited by 14
Artificial intelligence use and scientific innovation
Liu, Y., Xie, Y., Shen, X., & Wu, D. (2025) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.70043 · Cited by 7
“More remarkably, AI can now generate novel hypotheses that are difficult for humans to conceive intuitively, potentially leading to fundamental breakthroughs (Bail, 2024; Grossmann et al., 2023; Krenn et al., 2023).”
Medical Forecasting—A Skill Set Worthy of Attention
Creutzfeldt, C. J., & Holloway, R. G. (2024) · JAMA Neurology · doi:10.1001/jamaneurol.2024.1711 · Cited by 5
Rask, M., & Shimizu, K. (2024) · International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) · doi:10.1109/acdsa59508.2024.10467341 · Cited by 8
“Among the few is the work by Grossman et al., [6], which sheds light on both the potential and challenges of LLMs in this field.”
BackgroundYu, F., Zhao, Y., Xu, L., & Peng, K. (2025) · Frontiers of digital education. · doi:10.1007/s44366-025-0043-1 · Cited by 8
“This revolution has also significantly impacted psychology (Grossmann et al., 2023; Xu et al., 2024), compelling research-oriented education to undergo transformation in order to cultivate research talent capable of adapting to the changing times.”
Homophily in An Artificial Social Network of Agents Powered By Large Language ModelsPreprint
He, J. K., Wallis, F. P. S., & Rathje, S. (2023) · Research Square · doi:10.21203/rs.3.rs-3096289/v1 · Cited by 5
Artificial Intelligence Chatbots Mimic Human Collective Behaviour
He, J., Wallis, F., Gvirtz, A., & Rathje, S. (2024) · doi:10.21203/rs.3.rs-3096289/v2 · Cited by 4
Lin, Z. (2026) · Annual Review of Psychology · doi:10.1146/annurev-psych-100925-034807 · Cited by 4
Using Artificial Intelligence to Generate Visual Vignettes in Factorial Survey Experiments
Schwitter, N. (2025) · Social science computer review · doi:10.1177/08944393251392916 · Cited by 5
“While closed-source models are currently a more viable solution for most social scientists, as argued by Davidson (2024), in the long run, academic researchers will be best served by generative AI developed and maintained speci fi cally for research purposes (Bail, 2024; Grossmann et al., 2023).”
From tool to partner: generative AI usage patterns and research performance among doctoral students
Xu, H., & Shen, W. (2026) · Studies in Higher Education · doi:10.1080/03075079.2026.2625386 · Cited by 8
Charting the rise of imaginary worlds in history
Dubourg, E., Safa, R., Thouzeau, V., & Baumard, N. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-04877-x · Cited by 5
“LLMs have already shown signi fi cant promise in annotation tasks (Abdurahman et al. 2024; Brown et al. 2020; Ding et al. 2023; Grossmann et al. 2023), with accuracy exceeding that of other annotation methods (Bongini et al. 2023; Pei et al. 2023; Rathje et al. 2024; Gilardi et al. 2023), even in…”
Beyond accidents and misuse: decoding the structural risk dynamics of artificial intelligence
Kilian, K. A. (2025) · AI & Society · doi:10.1007/s00146-025-02419-2 · Cited by 18
Lucio, R., Harris, A., Báez, J. C., Campbell, M., & Ricciardelli, L. A. (2025) · Journal of Evidence-Based Social Work · doi:10.1080/26408066.2025.2548853 · Cited by 6
The NIMH Task Force on Social Work Research Report: Past Accomplishments and Current Challenges
Feldman, R. A. (2024) · Research on Social Work Practice · doi:10.1177/10497315241232320 · Cited by 4
“In a recent issue of Science , Grossmann et al. (2023) 2023) offered recommendations for social work researchers and journal editors on using generative AI and large language models that included guidance on data collection, analysis, interpretation, and dissemination.”
Productivity Modern Management Science Practices in the Age of AI
Wazi, N. W. M., Karim, F. B., & Noor, N. A. A. M. (2024) · Advances in business strategy and competitive advantage book series · doi:10.4018/979-8-3693-6720-9.ch005 · Cited by 4
AI agents can coordinate beyond human scale
De Marzo, G., Castellano, C., & Garcia, D. (2024) · arXiv · doi:10.48550/arxiv.2409.02822 · Cited by 4
“The right graph displays the average opinion for the 25-agent subgroup, which quickly fully coordinates. less [25, 4, 31], mostly with a focus on social simulation of network structures [16, 33, 12], opinion and information spreading [34, 14, 11] and online interaction [41, 35, 38].”
AI-Empowered Human Research Integrating Brain Science and Social Sciences Insights
Xiong, F., Yu, X., & Leong, H. W. (2024) · International Conference on Intelligent Education and Intelligent Research (IEIR) · doi:10.1109/ieir62538.2024.10960181 · Cited by 6
“However, recent advancements in AI, particularly large language models (LLMs), have the potential to revolutionize the research paradigm of social sciences [11].”
Editorial for the Special Issue on Algorithms in Our Lives
Bhatia, S., Galešić, M., & Mitchell, M. (2024) · Perspectives on Psychological Science · doi:10.1177/17456916231214452 · Cited by 4
“It is worth noting that some psychologists have also recently proposed replacing human participants with AI algorithms such as GPT (Dillion et al., 2023; Grossmann et al., 2023).”
Artificial Agents in Operations Management ExperimentsPreprint
Kirshner, S. N. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4726933 · Cited by 4
Digital technologies illuminate unexplored areas in nursing education
Wu, J., Chen, H., Shang, S., & Sun, H. (2025) · Interdisciplinary Nursing Research · doi:10.1097/nr9.0000000000000093 · Cited by 3
Using Large Language Models in Behavioral Science Interventions: Promise & Risk
Hecht, C. A., Ong, D. C., Clapper, M., Jones, M., Demszky, D., Yang, D., Eichstaedt, J. C., Bryan, C. J., & Yeager, D. S. (2025) · Behavioral Science & Policy · doi:10.1177/23794607251344698 · Cited by 7
Quantifying and explaining the rise of fiction
Dubourg, E., Thouzeau, V., Borredon, Q., & Baumard, N. (2025) · Evolutionary Human Sciences · doi:10.1017/ehs.2025.10011 · Cited by 4
“Indeed, LLMs have already shown significant promise in annotating cultural data at scale (Abdurahman et al., 2023; Brown et al., 2020; Ding et al., 2023; Grossmann et al., 2023).”
Probabilistic Record Linkage Using Pretrained Text Embeddings
Ornstein, J. T. (2025) · Political Analysis · doi:10.1017/pan.2025.10016 · Cited by 3
“It is worth noting, in light of ongoing debates over algorithmic bias in language models (Abid, Farooqi, and Zou 2021; Grossmann et al. 2023), that a disproportionate share of false positive matches (26 out of 32) are Asian, Hispanic, or African American names.”
A bias towards neutrality? How LLM guardrail sensitivity affects classification
Rogers, R., & Zhang, X. (2025) · Communication and Change · doi:10.1007/s44382-025-00013-0 · Cited by 5
“They also address issues with model validation whereby the researcher asks the model to explain or justify its response in a variety of ways (Grossmann et al., 2023).”
Matthes, J., & Vranken, S. (2025) · Communication and Change · doi:10.1007/s44382-025-00019-8 · Cited by 4
“This trend is also reflected in the growing body of research on the use of AI in social sciences (e.g., Bail, 2024; Grossmann et al., 2023), and the development of guidelines on responsible AI use in research by leading journals (e.g., Huh, 2023).”
Wang, J., & Redelmeier, D. A. (2025) · BMJ Quality & Safety · doi:10.1136/bmjqs-2025-019299 · Cited by 4
Yang, M., Luo, Y., He, T., Diao, S., Li, H., Zou, K., Hazlewood, G. S., Vandvik, P. O., Kawano-Dourado, L. B., Dourado, D. D. A., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02156-2 · Cited by 6
“Fourth, AI enhances the reproducibility and objectivity of research outcomes, mitigating the biases often present in traditional analysis methods due to researcher subjectivity or inconsistent data quality 61 .”
LLM agents reveal how human bias shapes path-dependent market dynamics
Hashimoto, R., Takayanagi, T., Suzuki, M., & Izumi, K. (2026) · Journal of Computational Social Science · doi:10.1007/s42001-026-00465-4 · Cited by 4
“LLMs have recently emerged as promising tools for constructing sophisticated agent models in social simulations [44], owing to their capacity to represent distinct agent profiles, generate text-based behavioral data, and engage in complex multi-agent interactions [45–47].”
Accelerating spiking neural networks with photonic reconfigurable devices
Lu, C., Xu, K., Liu, J., Ding, Y., Yu, J., Song, J., Meng, J., Wang, T., Guo, S., Jin, X., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-72119-y · Cited by 3
“Intelligent chatbots driven by arti fi cial neural networks (ANNs) have gained signi fi cant global popularity, accelerating and re fi ning the world ’ s development from art to science 1,2 .”
Evaluating the statistical realism of LLM-generated social science data
Xie, Y., Liang, L., Li, S., Lu, Y., Xiao, Z., Shi, M., Huang, J., Wang, M., & Xie, Y. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2538145123 · Cited by 16
Wood, N. G., Robbins, S., Berodt, E. Z., von Westerholt, A. G., Behrndt, M., Budig, H., & Schreiber, D. (2026) · Philosophy & Technology · doi:10.1007/s13347-026-01114-4 · Cited by 3
Chen, H., Wang, Z., & Ren, M. (2025) · Data and Information Management · doi:10.1016/j.dim.2025.100107 · Cited by 11
Rahimipouri, E., & Ghaitaranpour, A. (2025) · Food Quality and Preference · doi:10.1016/j.foodqual.2025.105724 · Cited by 3
Leveraging Artificial Intelligence (AI) in Competitive Intelligence (CI) Research
Hair, J. F., & Sabol, M. A. (2024) · Revista Inteligência Competitiva · doi:10.24883/eaglesustainable.v15i.469 · Cited by 5
Road Rage Against the Machine: Humans and LLMs Share a Blame Bias Against Driverless Cars
Chu, Y., Liu, P., Savulescu, J., & Earp, B. D. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2526593 · Cited by 3
“…may be argued that LLMs do not truly understand human thought or language (Chemero, 2023), some suggest they may function as computational models of humans or human cognition (Grossmann et al., 2023; Horton, 2023), enabling them to be used as “social simulacra” of humans or “virtual populations.””
Do We Need a Social Media Accelerator?Preprint
Bail, C. A., Hillygus, D. S., Volfovsky, A., Allamong, M. B., Alqabandi, F., Jordan, D., Tierney, G., LaChapelle, T., Trexler, A., & van Loon, A. (2023) · SocArXiv (OSF Preprints) · doi:10.31235/osf.io/ucfbk · Cited by 4
Computational Analysis of 100K Choice DilemmasPreprint
Bhatia, S., van Baal, S. T., Wang, F., & Walasek, L. (2024) · doi:10.31234/osf.io/hzv7c · Cited by 3
Yang, Y., Duan, H., Liu, J., & Tam, K. Y. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4961494 · Cited by 9
Collaborative Growth: When Large Language Models Meet Sociolinguistics
Nguyen, D. (2025) · Language and Linguistics Compass · doi:10.1111/lnc3.70010 · Cited by 8
Static network structure cannot stabilize cooperation among large language model agents
Han, J., Battu, B., Romić, I., Rahwan, T., & Holme, P. (2025) · PLoS ONE · doi:10.1371/journal.pone.0320094 · Cited by 7
“This optimism stems from two key properties: (i) LLMs are trained on vast amounts of information spanning a wide range of topics and cultures 28,29 , and (ii) they possess the capacity for reasoning and counterargument 30 .”
Zenk-Möltgen, W. (2025) · IASSIST Quarterly · doi:10.29173/iq1119 · Cited by 2
“Further on are developments of artificial intelligence in the social sciences that rely largely on machine-accessible data and are increasingly being used (Grossmann et al .”
Surfacing citizens’ policy perspectives at scale in the age of large language models
Lopez-Lopez, E., & Herzog, S. M. (2025) · Behavioral Science & Policy · doi:10.1177/23794607251346311 · Cited by 3
“…and other possible uses of LLMs in policy contexts, 66,67 including using LLMs to scope likely reactions to behavioral interventions (see References 32 and 68 through 70 for examples of how LLMs can be used to simulate and study human behavior), design conversational agents that can better engage…”
InfluentialWang, J., & Solís, P. (2025) · Energy and AI · doi:10.1016/j.egyai.2025.100580 · Cited by 4
“Grossmann et al. [51] provide a comprehensive overview of how LLMs can simulate human behavior, produce synthetic survey responses, and enhance agent-based models, while also raising concerns about validity, bias, and trans-parency.”
The Case for Using Generative AI to Run Deliberation Simulations
Rountree, J., & Gastil, J. (2025) · Journal of Deliberative Democracy · doi:10.16997/jdd.1625 · Cited by 2
The Reasonable, the Rational, and the Good: On Folk Theories of Deliberative Judgment
Grossmann, I., Kachhiyapatel, N., Meyers, E. A., Zhang, H.-X., & Eibach, R. P. (2025) · Open Mind · doi:10.1162/opmi.a.24 · Cited by 2
“…decision-making about climate change, overreliance on context-free in data-driven approaches to policymaking (Fazelpour & Danks, 2021; O ’ Neil, 2016), and as human judgment intersects with the evolving capacities of generative artificial intelligence (Grossmann et al., 2023; Johnson et al., 2024).”
Self-citationPowell, R. A. (2025) · Oxford University Press eBooks · doi:10.1093/9780191949142.001.0001 · Cited by 2
Affective connotations according to LLMs: implications for meaning measurement and cultural bias
Combs, A., Dametto, D., Blaison, C., Leung, R., Malhotra, A., Schröder, T., Hoey, J., & Smith‐Lovin, L. (2025) · Cognition & Emotion · doi:10.1080/02699931.2025.2568551 · Cited by 2
Quinn, N. W. T., Sridharan, V. K., Babbar‐Sebens, M., Zellner, M., Lott, C., Guzmán, S. M., Kumar, S., Ahmadisharaf, E., Rabby, S. H., & HELGESON, J. F. (2025) · Journal of Environmental Engineering · doi:10.1061/joeedu.eeeng-8218 · Cited by 2
Aligning LLM agents with human learning and adjustment behavior: A dual agent approach
Liu, T., Yang, J., Yin, Y., Li, M., Wang, L., & Zhu, Z. (2026) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2026.105818 · Cited by 2
Peker, Ö. B., Toker, E., Öcal, D., Dalyan, T., Afacan, E., & Gökdel, Y. D. (2026) · IEEE Access · doi:10.1109/access.2026.3656316 · Cited by 2
“In social sciences, AI is facilitating research through machine learning and natural language processing, helping to analyze complex social data and inform policy decisions [5], [6].”
A large-scale evaluation of commonsense knowledge in humans and large language models
Nguyen, T. D., Watts, D. J., & Whiting, M. E. (2026) · PNAS Nexus · doi:10.1093/pnasnexus/pgag029 · Cited by 6
Relationship between peer review quality and scientific impact: Insights from LLMs-assessed reviews
Sun, Z. (2026) · Journal of Informetrics · doi:10.1016/j.joi.2026.101801 · Cited by 3
Wang, Z., Song, R., Shen, C., Yin, S., Song, Z., Battu, B., Shi, L., Jia, D., Rahwan, T., & Hu, S. (2026) · National Science Review · doi:10.1093/nsr/nwag223 · Cited by 16
“First, LLMs are well known for their ability to understand language and generate conversations, making them well suited for interactions with humans [25 ,26 ].”
On using large language models to support social research for climate action
Nabavi, E., Maier, H. R., Razavi, S., Hindes, A., Howden, M. S., Grant, W. J., & Raman, S. (2026) · npj Climate Action · doi:10.1038/s44168-026-00375-1 · Cited by 2
On the Conceptualization of Synthetic Data: Why Not All Synthetic Data are Useful and Some are
Pashevich, E., & Hansen, J. U. (2026) · Minds and Machines · doi:10.1007/s11023-026-09781-0 · Cited by 2
“Another unfortunate application of synthesized data, suggested by Grossmann et al. (2023 ), is using LLMs to synthesize survey data for social sciences research.”
Aligning LLM with Humans for Travel Choices: A Persona-Based Embedding Learning Approach
Liu, T., Li, M., & Yin, Y. (2026) · Transportation Science · doi:10.1287/trsc.2025.0330 · Cited by 17
AI Fairness–From Machine Learning to Federated Learning
Patnaik, L. M., & Wang, W. (2024) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2023.029451 · Cited by 2
Metric-Independent Mitigation of Unpredefined Bias in Machine Classification
Tang, Z., Lu, T., & Li, T. (2024) · Intelligent Computing · doi:10.34133/icomputing.0083 · Cited by 2
Towards Hybrid Modelling and Simulation Concepts for Complex Socio-technical Systems
Tolk, A., Richkus, J., & Shaikh, Y. (2024) · Simulation foundations, methods and applications · doi:10.1007/978-3-031-59999-6_4 · Cited by 2
Generative Künstliche Intelligenz
Buder, J., Lindner, M. A., Oestermeier, U., Huff, M., Gerjets, P. H., Utz, S., & Creß, U. (2024) · Psychologische Rundschau · doi:10.1026/0033-3042/a000699 · Cited by 3
Forum: Fostering Cooperation in Conflict Research Beyond Borders
Tokdemir, E., Kalin, I., Cunningham, K., Aksoy, D., Carter, D. B., Loyle, C., Akcinaroglu, S., & Gleditsch, K. (2024) · All Azimuth A journal of foreign policy and peace · doi:10.20991/allazimuth.1590826 · Cited by 2
GALEA – Leveraging Generative Agents in Artifact Evaluation
Prat, N., Lalor, J. P., & Abbasi, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-93976-1_6 · Cited by 2
The prediction of non-ergodic humanity by artificial intelligence
Stewart, P. (2025) · AI & Society · doi:10.1007/s00146-025-02393-9 · Cited by 2
Wang, Z., Xia, H., Bao, H., Jing, Y., & Gu, R. (2025) · Advanced Science · doi:10.1002/advs.202508623 · Cited by 1
Xiong, Y., Cai, X., Lai, X., Wang, Y., Xin, H., Song, W., Lv, F., Guo, X., Yang, G., & Wu, Y. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1566260 · Cited by 2
“Artificial intelligence (AI) is a broad term that encompasses the use of computers to develop intelligent models capable of performing specified functions, with a significant impact on research in the natural and social sciences (11, 12).”
Wu, J., & Cheng, L. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-05690-2 · Cited by 4
“Although generative AI may surpass humans in data processing speed and computational ability, it is necessary to distinguish between original rational thinking ability and intelligence formed based on human creation (Grossmann et al. 2023; Wang et al. 2023a).”
Artificial Intelligence Chatbots Mimic Human Collective BehaviourPreprint
He, J. K., Wallis, F. P. S., Gvirtz, A., & Rathje, S. (2023) · doi:10.31234/osf.io/vf5ap · Cited by 2
AI-Assisted Identification of Policy-Salient Research Priorities and Emerging IssuesPreprint
Rudd, M. A. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4502450 · Cited by 2
The Mixed Subjects Design: Treating Large Language Models as Potentially Informative ObservationsPreprint
Broska, D., Howes, M., & van Loon, A. (2024) · SocArXiv (OSF Preprints) · doi:10.31235/osf.io/j3bnt · Cited by 2
Testing the limits of large language models in debating humans
Flamino, J., Modi, M. S., Szymanski, B. K., Cross, B., & Mikolajczyk, C. (2025) · Scientific Reports · doi:10.1038/s41598-025-98378-1 · Cited by 1
Woodward, D., Sujithra, L. R., Nisha, P., & Saikia, B. (2024) · Advances in Complex Systems · doi:10.1142/s1793962325500217 · Cited by 1
Media, media education, GAI and radical uncertainty
Honkanen, P., & Nylund, M. (2024) · Media Education · doi:10.36253/me-16303 · Cited by 1
Validation of social science theories using machine learning models: a methodological perspective
David, L. K., Wang, J., & Angel, V. (2025) · Quality & Quantity · doi:10.1007/s11135-025-02075-0 · Cited by 1
“• Happiness and Well-being Theory explores the relationship of economic, cultural, and social factors that contribute to life satisfaction and societal progress (Grossmann et al. 2023).”
Expanding the Frontiers: Revolutionizing Quantitative Data Analysis in Social Sciences with AI
Andreev, H., Pericleous, K., & Kosmas, P. (2025) · CABI eBooks · doi:10.1079/9781800626607.0004 · Cited by 1
Aslanoğlu, R. (2025) · European journal of behavioral sciences · doi:10.33422/ejbs.v8i1.1471 · Cited by 1
“The entry of AI into this interdisciplinary field enables new research paradigms and more comprehensive understanding of the way-built environments influence human behavior and propagation within a society (Grossmann et al., 2023).”
Metadata
- Authors
- Grossmann, I., Feinberg, M., Parker, D. C., Christakis, N. A., Tetlock, P. E., & Cunningham, W. A.
- Affiliations
- University of Waterloo (CA)
- University of Toronto (CA)
- Yale University (US)
- University of Pennsylvania (US)
- ORCID
- Venue
- Science
- Published
- 2023-06-16
- Volume
- 380
- Issue
- 6650
- Pages
- 1108-1109
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W4380763235
- Cited by
- 377
- References
- 10
- Citations by year
- 2026: 110 · 2025: 156 · 2024: 92 · 2023: 19
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence, social science research, research transformation, algorithmic bias, data fidelity, artificial intelligence in research
- MeSH terms
- Artificial Intelligence, Humans, Social Sciences, Bias
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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