IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2508.02966.html
   My bibliography  Save this paper

Measuring Human Leadership Skills with Artificially Intelligent Agents

Author

Listed:
  • Ben Weidmann
  • Yixian Xu
  • David J. Deming

Abstract

We show that the ability to lead groups of humans is predicted by leadership skill with Artificially Intelligent agents. In a large pre-registered lab experiment, human leaders worked with AI agents to solve problems. Their performance on this 'AI leadership test' was strongly correlated with their causal impact on human teams, which we estimate by repeatedly randomly assigning leaders to groups of human followers and measuring team performance. Successful leaders of both humans and AI agents ask more questions and engage in more conversational turn-taking; they score higher on measures of social intelligence, fluid intelligence, and decision-making skill, but do not differ in gender, age, ethnicity or education. Our findings indicate that AI agents can be effective proxies for human participants in social experiments, which greatly simplifies the measurement of leadership and teamwork skills.

Suggested Citation

  • Ben Weidmann & Yixian Xu & David J. Deming, 2025. "Measuring Human Leadership Skills with Artificially Intelligent Agents," Papers 2508.02966, arXiv.org.
    • Handle: RePEc:arx:papers:2508.02966
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2508.02966
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jason W. Burton & Ezequiel Lopez-Lopez & Shahar Hechtlinger & Zoe Rahwan & Samuel Aeschbach & Michiel A. Bakker & Joshua A. Becker & Aleks Berditchevskaia & Julian Berger & Levin Brinkmann & Lucie Fle, 2024. "How large language models can reshape collective intelligence," Nature Human Behaviour, Nature, vol. 8(9), pages 1643-1655, September.
    2. David J. Deming, 2017. "The Growing Importance of Social Skills in the Labor Market," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 132(4), pages 1593-1640.
    3. Argyle, Lisa P. & Busby, Ethan C. & Fulda, Nancy & Gubler, Joshua R. & Rytting, Christopher & Wingate, David, 2023. "Out of One, Many: Using Language Models to Simulate Human Samples," Political Analysis, Cambridge University Press, vol. 31(3), pages 337-351, July.
    4. Milena Tsvetkova & Taha Yasseri & Niccolo Pescetelli & Tobias Werner, 2024. "A new sociology of humans and machines," Nature Human Behaviour, Nature, vol. 8(10), pages 1864-1876, October.
    5. John J. Horton, 2023. "Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?," NBER Working Papers 31122, National Bureau of Economic Research, Inc.
    6. John J. Horton, 2023. "Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?," Papers 2301.07543, arXiv.org.
    7. Ben Weidmann & David J. Deming, 2021. "Team Players: How Social Skills Improve Team Performance," Econometrica, Econometric Society, vol. 89(6), pages 2637-2657, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hui Chen & Antoine Didisheim & Luciano Somoza & Hanqing Tian, 2025. "A Financial Brain Scan of the LLM," Papers 2508.21285, arXiv.org.
    2. repec:osf:osfxxx:r3qng_v1 is not listed on IDEAS
    3. Aliya Amirova & Theodora Fteropoulli & Nafiso Ahmed & Martin R Cowie & Joel Z Leibo, 2024. "Framework-based qualitative analysis of free responses of Large Language Models: Algorithmic fidelity," PLOS ONE, Public Library of Science, vol. 19(3), pages 1-33, March.
    4. Sugat Chaturvedi & Rochana Chaturvedi, 2025. "Who Gets the Callback? Generative AI and Gender Bias," Papers 2504.21400, arXiv.org.
    5. Navid Ghaffarzadegan & Aritra Majumdar & Ross Williams & Niyousha Hosseinichimeh, 2024. "Generative agent‐based modeling: an introduction and tutorial," System Dynamics Review, System Dynamics Society, vol. 40(1), January.
    6. Paola Cillo & Gaia Rubera, 2025. "Generative AI in innovation and marketing processes: A roadmap of research opportunities," Journal of the Academy of Marketing Science, Springer, vol. 53(3), pages 684-701, May.
    7. Yingnan Yan & Tianming Liu & Yafeng Yin, 2025. "Valuing Time in Silicon: Can Large Language Model Replicate Human Value of Travel Time," Papers 2507.22244, arXiv.org.
    8. Niyousha Hosseinichimeh & Aritra Majumdar & Ross Williams & Navid Ghaffarzadegan, 2024. "From text to map: a system dynamics bot for constructing causal loop diagrams," System Dynamics Review, System Dynamics Society, vol. 40(3), July.
    9. Eric Hitz & Mingmin Feng & Radu Tanase & Ren'e Algesheimer & Manuel S. Mariani, 2025. "The amplifier effect of artificial agents in social contagion," Papers 2502.21037, arXiv.org, revised Mar 2025.
    10. Kevin Leyton-Brown & Paul Milgrom & Neil Newman & Ilya Segal, 2024. "Artificial Intelligence and Market Design: Lessons Learned from Radio Spectrum Reallocation," NBER Chapters, in: New Directions in Market Design, National Bureau of Economic Research, Inc.
    11. C. Monica Capra & Thomas J. Kniesner, 2025. "Daniel Kahneman’s underappreciated last published paper: Empirical implications for benefit-cost analysis and a chat session discussion with bots," Journal of Risk and Uncertainty, Springer, vol. 71(1), pages 29-51, August.
    12. Kirshner, Samuel N., 2024. "GPT and CLT: The impact of ChatGPT's level of abstraction on consumer recommendations," Journal of Retailing and Consumer Services, Elsevier, vol. 76(C).
    13. Shu Wang & Zijun Yao & Shuhuai Zhang & Jianuo Gai & Tracy Xiao Liu & Songfa Zhong, 2025. "When Experimental Economics Meets Large Language Models: Evidence-based Tactics," Papers 2505.21371, arXiv.org, revised Jul 2025.
    14. Cortes, Patricia & Feng, Ying & Guida-Johnson, Nicolás & Pan, Jessica, 2023. "Automation and Gender: Implications for Occupational Segregation and the Gender Skill Gap," IZA Discussion Papers 16695, Institute of Labor Economics (IZA).
    15. Zengqing Wu & Run Peng & Xu Han & Shuyuan Zheng & Yixin Zhang & Chuan Xiao, 2023. "Smart Agent-Based Modeling: On the Use of Large Language Models in Computer Simulations," Papers 2311.06330, arXiv.org, revised Dec 2023.
    16. repec:osf:osfxxx:udz28_v1 is not listed on IDEAS
    17. Joshua C. Yang & Damian Dailisan & Marcin Korecki & Carina I. Hausladen & Dirk Helbing, 2024. "LLM Voting: Human Choices and AI Collective Decision Making," Papers 2402.01766, arXiv.org, revised Aug 2024.
    18. Elif Akata & Lion Schulz & Julian Coda-Forno & Seong Joon Oh & Matthias Bethge & Eric Schulz, 2025. "Playing repeated games with large language models," Nature Human Behaviour, Nature, vol. 9(7), pages 1380-1390, July.
    19. Nir Chemaya & Daniel Martin, 2024. "Perceptions and detection of AI use in manuscript preparation for academic journals," PLOS ONE, Public Library of Science, vol. 19(7), pages 1-16, July.
    20. David J. Deming, 2022. "Four Facts about Human Capital," Journal of Economic Perspectives, American Economic Association, vol. 36(3), pages 75-102, Summer.
    21. David J. Deming & Mikko I. Silliman, 2024. "Skills and Human Capital in the Labor Market," NBER Working Papers 32908, National Bureau of Economic Research, Inc.
    22. Lijia Ma & Xingchen Xu & Yong Tan, 2024. "Crafting Knowledge: Exploring the Creative Mechanisms of Chat-Based Search Engines," Papers 2402.19421, arXiv.org.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2508.02966. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.