IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2406.00477.html
   My bibliography  Save this paper

Generative AI as Economic Agents

Author

Listed:
  • Nicole Immorlica
  • Brendan Lucier
  • Aleksandrs Slivkins

Abstract

Traditionally, AI has been modeled within economics as a technology that impacts payoffs by reducing costs or refining information for human agents. Our position is that, in light of recent advances in generative AI, it is increasingly useful to model AI itself as an economic agent. In our framework, each user is augmented with an AI agent and can consult the AI prior to taking actions in a game. The AI agent and the user have potentially different information and preferences over the communication, which can result in equilibria that are qualitatively different than in settings without AI.

Suggested Citation

  • Nicole Immorlica & Brendan Lucier & Aleksandrs Slivkins, 2024. "Generative AI as Economic Agents," Papers 2406.00477, arXiv.org.
    • Handle: RePEc:arx:papers:2406.00477
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2406.00477
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mark Armstrong & John Vickers, 2010. "A Model of Delegated Project Choice," Econometrica, Econometric Society, vol. 78(1), pages 213-244, January.
    2. Dirk Bergemann & Stephen Morris, 2019. "Information Design: A Unified Perspective," Journal of Economic Literature, American Economic Association, vol. 57(1), pages 44-95, March.
    3. Emir Kamenica, 2019. "Bayesian Persuasion and Information Design," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 249-272, August.
    4. Dirk Bergemann & Benjamin Brooks & Stephen Morris, 2015. "The Limits of Price Discrimination," American Economic Review, American Economic Association, vol. 105(3), pages 921-957, March.
    5. Dirk Bergemann & Stephen Morris, 2016. "Information Design, Bayesian Persuasion, and Bayes Correlated Equilibrium," American Economic Review, American Economic Association, vol. 106(5), pages 586-591, May.
    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. Farzaneh Farhadi & Demosthenis Teneketzis, 2022. "Dynamic Information Design: A Simple Problem on Optimal Sequential Information Disclosure," Dynamic Games and Applications, Springer, vol. 12(2), pages 443-484, June.
    2. Roesler, Anne-Katrin & Deb, Rahul, 2021. "Multi-Dimensional Screening: Buyer-Optimal Learning and Informational Robustness," CEPR Discussion Papers 16206, C.E.P.R. Discussion Papers.
    3. Mark Armstrong & Jidong Zhou, 2022. "Consumer Information and the Limits to Competition," American Economic Review, American Economic Association, vol. 112(2), pages 534-577, February.
    4. Serena Wang & Michael I. Jordan & Katrina Ligett & R. Preston McAfee, 2024. "Relying on the Metrics of Evaluated Agents," Papers 2402.14005, arXiv.org, revised Oct 2024.
    5. Masaki Miyashita & Takashi Ui, 2023. "LQG Information Design," Papers 2312.09479, arXiv.org, revised Jun 2024.
    6. Jibang Wu & Ashwinkumar Badanidiyuru & Haifeng Xu, 2021. "Auctioning with Strategically Reticent Bidders," Papers 2109.04888, arXiv.org, revised Jan 2023.
    7. T. Tony Ke & Jiwoong Shin & Jungju Yu, 2023. "A Model of Product Portfolio Design: Guiding Consumer Search Through Brand Positioning," Marketing Science, INFORMS, vol. 42(6), pages 1101-1124, November.
    8. Yingkai Li & Aleksandrs Slivkins, 2022. "Exploration and Incentivizing Participation in Clinical Trials," Papers 2202.06191, arXiv.org, revised Aug 2024.
    9. Eduardo Perez‐Richet & Vasiliki Skreta, 2022. "Test Design Under Falsification," Econometrica, Econometric Society, vol. 90(3), pages 1109-1142, May.
    10. Zhou, Jidong, 2021. "Mixed bundling in oligopoly markets," Journal of Economic Theory, Elsevier, vol. 194(C).
    11. Miltiadis Makris & Ludovic Renou, 2018. "Information design in multi-stage games," Working Papers 861, Queen Mary University of London, School of Economics and Finance.
    12. Garcia, Daniel & Tsur, Matan, 2021. "Information design in competitive insurance markets," Journal of Economic Theory, Elsevier, vol. 191(C).
    13. Shih-Tang Su & Vijay G. Subramanian & Grant Schoenebeck, 2021. "Bayesian Persuasion in Sequential Trials," Papers 2110.09594, arXiv.org, revised Nov 2021.
    14. Chan, Jimmy & Gupta, Seher & Li, Fei & Wang, Yun, 2019. "Pivotal persuasion," Journal of Economic Theory, Elsevier, vol. 180(C), pages 178-202.
      • Jimmy Chan & Seher Gupta & Fei Li & Yun Wang, 2018. "Pivotal Persuasion," Working Papers 2018-11-03, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    15. Dirk Bergemann & Tibor Heumann & Stephen Morris, 2022. "Screening with Persuasion," Cowles Foundation Discussion Papers 2338, Cowles Foundation for Research in Economics, Yale University.
    16. Casey Rothschild, 2024. "Welfare analysis in insurance markets," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 49(1), pages 36-58, March.
    17. Heski Bar‐Isaac & Clare Leaver, 2022. "Training, Recruitment, and Outplacement as Endogenous Adverse Selection," Economica, London School of Economics and Political Science, vol. 89(356), pages 849-861, October.
    18. Bonatti, Alessandro & Dahleh, Munther & Horel, Thibaut & Nouripour, Amir, 2024. "Selling information in competitive environments," Journal of Economic Theory, Elsevier, vol. 216(C).
    19. Raphaela Hennigs, 2021. "Conflict prevention by Bayesian persuasion," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 23(4), pages 710-731, August.
    20. Kun Heo & Antoine Zerbini, 2024. "Segment and rule: Modern censorship in authoritarian regimes," Discussion Papers 2024-04, Nottingham Interdisciplinary Centre for Economic and Political Research (NICEP).

    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:2406.00477. 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.