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Simple Rules for a Complex World with Arti?cial Intelligence

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  • Jesus Fernandez-Villaverde

    (University of Pennsylvania)

Abstract

Can articial intelligence, in particular, machine learning algorithms, replace the idea of simple rules, such as ?rst possession and voluntary exchange in free markets, as a foundation for public policy? This paper argues that the preponderance of the evidence sides with the interpretation that while arti?cial intelligence will help public policy along important aspects, simple rules will remain the fundamental guideline for the design of institutions and legal environments where markets operate. “Digital socialism” might be a hipster thing to talk about in Williamsburg or Shoreditch, but it is as much of a chimera as “analog socialism.”

Suggested Citation

  • Jesus Fernandez-Villaverde, 2020. "Simple Rules for a Complex World with Arti?cial Intelligence," PIER Working Paper Archive 20-010, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    • Handle: RePEc:pen:papers:20-010
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    References listed on IDEAS

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    1. Steven J. Davis & James A. Kahn, 2008. "Interpreting the Great Moderation: Changes in the Volatility of Economic Activity at the Macro and Micro Levels," Journal of Economic Perspectives, American Economic Association, vol. 22(4), pages 155-180, Fall.
    2. Athey, Susan & Imbens, Guido W. & Metzger, Jonas & Munro, Evan, 2024. "Using Wasserstein Generative Adversarial Networks for the design of Monte Carlo simulations," Journal of Econometrics, Elsevier, vol. 240(2).
    3. Jesús Fernández‐Villaverde & Samuel Hurtado & Galo Nuño, 2023. "Financial Frictions and the Wealth Distribution," Econometrica, Econometric Society, vol. 91(3), pages 869-901, May.
    4. Raj Chetty & Nathaniel Hendren & Patrick Kline & Emmanuel Saez, 2014. "Where is the land of Opportunity? The Geography of Intergenerational Mobility in the United States," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 129(4), pages 1553-1623.
    5. Daron Acemoglu & Simon Johnson & James A. Robinson, 2001. "The Colonial Origins of Comparative Development: An Empirical Investigation," American Economic Review, American Economic Association, vol. 91(5), pages 1369-1401, December.
    6. Susan Athey & Mohsen Bayati & Guido Imbens & Zhaonan Qu, 2019. "Ensemble Methods for Causal Effects in Panel Data Settings," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 65-70, May.
    7. Jesus Fernandez-Villaverde & Daniel Sanches & Linda Schilling & Harald Uhlig, 2021. "Central Bank Digital Currency: Central Banking For All?," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 41, pages 225-242, July.
    8. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    9. Deaton, Angus & Cartwright, Nancy, 2018. "Understanding and misunderstanding randomized controlled trials," Social Science & Medicine, Elsevier, vol. 210(C), pages 2-21.
    10. Atila Abdulkadiroğlu & Joshua D. Angrist & Susan M. Dynarski & Thomas J. Kane & Parag A. Pathak, 2011. "Accountability and Flexibility in Public Schools: Evidence from Boston's Charters And Pilots," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 126(2), pages 699-748.
    11. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    12. Fernández-Villaverde, Jesús & Zarruk Valencia , David, 2018. "A Practical Guide to Parallelization in Economics," CEPR Discussion Papers 12890, C.E.P.R. Discussion Papers.
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    Cited by:

    1. Naudé, Wim, 2023. "Artificial Intelligence and the Economics of Decision-Making," IZA Discussion Papers 16000, Institute of Labor Economics (IZA).

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    More about this item

    Keywords

    Arti?cial intelligence; machine learning; economics; law; rule of law;
    All these keywords.

    JEL classification:

    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • H10 - Public Economics - - Structure and Scope of Government - - - General
    • H30 - Public Economics - - Fiscal Policies and Behavior of Economic Agents - - - General

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