Thursday, July 9, 2026
No menu items!
HomeNatureLarge language models can predict the results of social science experiments

Large language models can predict the results of social science experiments

  • Bail, C. A. Can generative AI improve social science? Proc. Natl Acad. Sci. USA 121, e2314021121 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Grossmann, I. et al. AI and the transformation of social science research. Science 380, 1108–1109 (2023).

    Article 
    CAS 
    PubMed 
    ADS 

    Google Scholar
     

  • Luo, X. et al. Large language models surpass human experts in predicting neuroscience results. Nat. Hum. Behav. 9, 305–315 (2025).

    Article 
    PubMed 

    Google Scholar
     

  • Crockett, M. & Messeri, L. Should large language models replace human participants? Preprint at PsyArXiv https://doi.org/10.31234/osf.io/4zdx9 (2024).

  • Abdurahman, S. et al. Perils and opportunities in using large language models in psychological research. PNAS Nexus 3, pgae245 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Argyle, L. P. et al. Out of one, many: using language models to simulate human samples. Polit. Anal. 31, 337–351 (2023).

    Article 

    Google Scholar
     

  • Bisbee, J., Clinton, J. D., Dorff, C., Kenkel, B. & Larson, J. M. Synthetic replacements for human survey data? The perils of large language models. Polit. Anal. 32, 401–416 (2024).

    Article 

    Google Scholar
     

  • Atari, M., Xue, M. J., Park, P. S., Blasi, D. & Henrich, J. Which humans? Preprint at PsyArXiv https://doi.org/10.31234/osf.io/5b26t (2023).

  • Dillion, D., Tandon, N., Gu, Y. & Gray, K. Can AI language models replace human participants? Trends Cogn. Sci. 27, 597–600 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Filippas, A., Horton, J. J. & Manning, B. S. Large language models as simulated economic agents: what can we learn from homo silicus? In Proc. 25th ACM Conference on Economics and Computation 614–615 (ACM, 2024).

  • Binz, M. & Schulz, E. Using cognitive psychology to understand GPT-3. Proc. Natl Acad. Sci. USA 120, e2218523120 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bai, H., Voelkel, J. G., Muldowney, S., Eichstaedt, J. C. & Willer, R. LLM-generated messages can persuade humans on policy issues. Nat. Commun. 16, 6037 (2025).

    Article 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar
     

  • Hackenburg, K. et al. The levers of political persuasion with conversational artificial intelligence. Science 390, eaea3884 (2025).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Meta Fundamental AI Research Diplomacy Team (FAIR) et al. Human-level play in the game of diplomacy by combining language models with strategic reasoning. Science 378, 1067–1074 (2022).

  • Park, J. S. et al. Generative agents: interactive simulacra of human behavior. In Proc. 36th Annual ACM Symposium on User Interface Software and Technology (eds Follmer, S. et al.) 1–22 (ACM, 2023).

  • Schramowski, P., Turan, C., Andersen, N., Rothkopf, C. A. & Kersting, K. Large pre-trained language models contain human-like biases of what is right and wrong to do. Nat. Mach. Intell. 4, 258–268 (2022).

    Article 

    Google Scholar
     

  • Yin, Y., Jia, N. & Wakslak, C. J. AI can help people feel heard, but an AI label diminishes this impact. Proc. Natl Acad. Sci. USA 121, e2319112121 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lehr, S. A., Caliskan, A., Liyanage, S. & Banaji, M. R. ChatGPT as research scientist: probing GPT’s capabilities as a research librarian, research ethicist, data generator, and data predictor. Proc. Natl Acad. Sci. USA 121, e2404328121 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Time-sharing Experiments for the Social Sciences. TESS www.tessexperiments.org (2026).

  • Coppock, A., Leeper, T. J. & Mullinix, K. J. Generalizability of heterogeneous treatment effect estimates across samples. Proc. Natl Acad. Sci. USA 115, 12441–12446 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar
     

  • Chang, S., Kennedy, A., Leonard, A. & List, J. A. 12 Best Practices for Leveraging Generative AI in Experimental Research (National Bureau of Economic Research, 2024).

  • Broska, D., Howes, M. & van Loon, A. The mixed subjects design: treating large language models as potentially informative observations. Sociol. Methods Res. 54, 1074–1109 (2025).

    Article 
    MathSciNet 

    Google Scholar
     

  • Chu, J. Y. et al. Academics are more specific, and practitioners more sensitive, in forecasting interventions to strengthen democratic attitudes. Proc. Natl Acad. Sci. USA 121, e2307008121 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • DellaVigna, S. & Linos, E. RCTs to scale: comprehensive evidence from two nudge units. Econometrica 90, 81–116 (2022).

    Article 

    Google Scholar
     

  • Dreber, A. et al. Using prediction markets to estimate the reproducibility of scientific research. Proc. Natl Acad. Sci. USA 112, 15343–15347 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar
     

  • Milkman, K. L. et al. Megastudies improve the impact of applied behavioural science. Nature 600, 478–483 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar
     

  • Harding, J., D’Alessandro, W., Laskowski, N. G. & Long, R. AI language models cannot replace human research participants. AI Soc. 39, 2603–2605 (2024).

  • Messeri, L. & Crockett, M. J. Artificial intelligence and illusions of understanding in scientific research. Nature 627, 49–58 (2024).

    Article 
    CAS 
    PubMed 
    ADS 

    Google Scholar
     

  • Binz, M. et al. A foundation model to predict and capture human cognition. Nature 644, 1002–1009 (2025).

  • Petrov, N. B., Serapio-García, G. & Rentfrow, J. Limited ability of LLMs to simulate human psychological behaviours: a psychometric analysis. Preprint at https://arxiv.org/abs/2405.07248 (2024).

  • Tjuatja, L., Chen, V., Wu, T., Talwalkwar, A. & Neubig, G. Do LLMs exhibit human-like response biases? A case study in survey design. Trans. Assoc. Comput. Linguist. 12, 1011–1026 (2024).

    Article 

    Google Scholar
     

  • Chen, Y., Hu, Y. & Lu, Y. Predicting field experiments with large language models. Preprint at https://arxiv.org/abs/2504.01167 (2025).

  • Wang, X. et al. Self-consistency improves chain of thought reasoning in language models. In Proc. 11th International Conference on Learning Representations (poster) (ICLR, 2023).

  • Arora, S. et al. Ask me anything: a simple strategy for prompting language models. In Proc. 11th International Conference on Learning Representations (ICLR, 2023).

  • Voronov, A., Wolf, L. & Ryabinin, M. Mind your format: towards consistent evaluation of in-context learning improvements. In Findings of the Association for Computational Linguistics: ACL 2024 (eds Ku, L.-W. et al.) 6287–6310 (ACL, 2024).

  • Hou, B., O’Connor, J., Andreas, J., Chang, S. & Zhang, Y. Promptboosting: black-box text classification with ten forward passes. In Proc. International Conference on Machine Learning (eds Krause, A. et al.) 13309–13324 (PMLR, 2023).

  • Open Science Collaboration. Estimating the reproducibility of psychological science. Science 349, aac4716 (2015).

    Article 

    Google Scholar
     

  • Milkman, K. L. et al. Megastudy shows that reminders boost vaccination but adding free rides does not. Nature 631, 179–188 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar
     

  • Milkman, K. L. et al. A 680,000-person megastudy of nudges to encourage vaccination in pharmacies. Proc. Natl Acad. Sci. USA 119, e2115126119 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zickfeld, J. H. et al. Effectiveness of ex ante honesty oaths in reducing dishonesty depends on content. Nat. Hum. Behav. 9, 169–187 (2025).

    Article 
    PubMed 

    Google Scholar
     

  • Broockman, D. E., Kalla, J. L., Caballero, C. & Easton, M. Political practitioners poorly predict which messages persuade the public. Proc. Natl Acad. Sci. USA 121, e2400076121 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • DellaVigna, S. & Vivalt, E. Forecasting social science: evidence from 100 projects. National Bureau of Economic Research https://doi.org/10.3386/w34493 (2025).

  • Allen, J., Watts, D. J. & Rand, D. G. Quantifying the impact of misinformation and vaccine-skeptical content on Facebook. Science 384, eadk3451 (2024).

    Article 
    CAS 
    PubMed 
    ADS 

    Google Scholar
     

  • DellaVigna, S. & Pope, D. What motivates effort? Evidence and expert forecasts. Rev. Econ. Stud. 85, 1029–1069 (2018).

    Article 

    Google Scholar
     

  • Vlasceanu, M. et al. Addressing climate change with behavioral science: a global intervention tournament in 63 countries. Sci. Adv. 10, eadj5778 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tappin, B. M., Wittenberg, C., Hewitt, L. B., Berinsky, A. J. & Rand, D. G. Quantifying the potential persuasive returns to political microtargeting. Proc. Natl Acad. Sci. USA 120, e2216261120 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Voelkel, J. G. et al. Megastudy testing 25 treatments to reduce antidemocratic attitudes and partisan animosity. Science 386, eadh4764 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Voelkel, J. G. et al. A registered report megastudy on the persuasiveness of the most-cited climate messages. Nat. Clim. Chang. 16, 214–225 (2026).

  • Saccardo, S. et al. Field testing the transferability of behavioural science knowledge on promoting vaccinations. Nat. Hum. Behav. 8, 878–890 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mason, K. et al. A megastudy of behavioral interventions to increase voter registration ahead of the 2024 US presidential election. Preprint at PsyArXiv https://doi.org/10.31234/osf.io/p6b2e_v3 (2025).

  • Goldwert, D. et al. A megastudy of behavioral interventions to catalyze public, political, and financial climate advocacy. PNAS Nexus 5, pgaf400 (2026).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Druckman, J. N. Experimental Thinking (Cambridge Univ. Press, 2022).

  • Freese, J. & Peterson, D. Replication in social science. Annu. Rev. Sociol. 43, 147–165 (2017).

    Article 

    Google Scholar
     

  • Bai, X., Wang, A., Sucholutsky, I. & Griffiths, T. L. Explicitly unbiased large language models still form biased associations. Proc. Natl Acad. Sci. USA 122, e2416228122 (2025).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wang, A., Morgenstern, J. & Dickerson, J. P. Large language models that replace human participants can harmfully misportray and flatten identity groups. Nat. Mach. Intell. 7, 400–411 (2025).

  • Park, P. S., Schoenegger, P. & Zhu, C. Diminished diversity-of-thought in a standard large language model. Behav. Res. Methods 56, 5754–5770 (2024).

  • Frank, M. C. Openly accessible LLMs can help us to understand human cognition. Nat. Hum. Behav. 7, 1825–1827 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Wack, M., Ehrett, C., Linvill, D. & Warren, P. Generative propaganda: evidence of AI’s impact from a state-backed disinformation campaign. PNAS Nexus 4, pgaf083 (2025).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Response retrieved from ChatGPT-4 on 22 May. ChatGPT https://chatgpt.com/share/2a2b66d0-e9fd-421f-bedd-bb6f9354742a (2024).

  • Almaatouq, A. et al. Beyond playing 20 questions with nature: integrative experiment design in the social and behavioral sciences. Behav. Brain Sci. 47, e33 (2024).

    Article 

    Google Scholar
     

  • Zhu, J.-Q., Xie, H., Arumugam, D., Wilson, R. C. & Griffiths, T. L. Using reinforcement learning to train large language models to explain human decisions. Preprint at https://arxiv.org/abs/2505.11614 (2025).

  • Tyner, A. H. et al. Investigating the replicability of the social and behavioural sciences. Nature 652, 143–150 (2026).

  • Cremer, D. D. & Kasparov, G. AI should augment human intelligence, not replace it. Harvard Business Review https://hbr.org/2021/03/ai-should-augment-human-intelligence-not-replace-it (2021).

  • Cummins, J. The threat of analytic flexibility in using large language models to simulate human data: a call to attention. Preprint at https://arxiv.org/abs/2509.13397 (2025).

  • Obermeyer, Z., Powers, B., Vogeli, C. & Mullainathan, S. Dissecting racial bias in an algorithm used to manage the health of populations. Science 366, 447–453 (2019).

    Article 
    CAS 
    PubMed 
    ADS 

    Google Scholar
     

  • Coppock, A. Generalizing from survey experiments conducted on mechanical turk: a replication approach. Political Sci. Res. Methods 7, 613–628 (2019).

    Article 

    Google Scholar
     

  • Mullinix, K. J., Leeper, T. J., Druckman, J. N. & Freese, J. The generalizability of survey experiments. J. Exp. Political Sci. 2, 109–138 (2015).

    Article 

    Google Scholar
     

  • Achiam, J. et al. GPT-4 technical report. Preprint at https://arxiv.org/abs/2303.08774 (2023).

  • Viechtbauer, W. metafor: Meta-Analysis Package for R v.4.8-0 (2015).

  • Rios, K., Roth, Z. C. & Coleman, T. J. III. The importance of scientists’ intellectual humility for communicating effectively across ideological and identity-based divides. Proc. Natl Acad. Sci. USA 122, e2400930121 (2025).

  • RELATED ARTICLES

    Most Popular

    Recent Comments