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Comment on "Science in the Age of Algorithms"

In: The Economics of Transformative AI

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  • Ajay Agrawal
  • John McHale
  • Alexander Oettl

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  • Ajay Agrawal & John McHale & Alexander Oettl, 2025. "Comment on "Science in the Age of Algorithms"," NBER Chapters, in: The Economics of Transformative AI, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberch:15322
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    File URL: http://www.nber.org/chapters/c15322.pdf
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    References listed on IDEAS

    as
    1. Agrawal, Ajay & McHale, John & Oettl, Alexander, 2024. "Artificial intelligence and scientific discovery: a model of prioritized search," Research Policy, Elsevier, vol. 53(5).
    2. Drew Fudenberg & Jon Kleinberg & Annie Liang & Sendhil Mullainathan, 2022. "Measuring the Completeness of Economic Models," Journal of Political Economy, University of Chicago Press, vol. 130(4), pages 956-990.
    3. Jens Ludwig & Sendhil Mullainathan, 2024. "Machine Learning as a Tool for Hypothesis Generation," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 139(2), pages 751-827.
    4. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    5. Ajay Agrawal & John McHale & Alexander Oettl, 2023. "Superhuman science: How artificial intelligence may impact innovation," Journal of Evolutionary Economics, Springer, vol. 33(5), pages 1473-1517, November.
    6. Sendhil Mullainathan & Ashesh Rambachan, 2024. "From Predictive Algorithms to Automatic Generation of Anomalies," Papers 2404.10111, arXiv.org, revised Sep 2025.
    7. Sendhil Mullainathan & Ashesh Rambachan, 2025. "Science in the Age of Algorithms," NBER Chapters, in: The Economics of Transformative AI, National Bureau of Economic Research, Inc.
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