IDEAS home Printed from https://ideas.repec.org/a/spr/joevec/v33y2023i5d10.1007_s00191-023-00845-3.html
   My bibliography  Save this article

Superhuman science: How artificial intelligence may impact innovation

Author

Listed:
  • Ajay Agrawal

    (University of Toronto
    National Bureau of Economic Research (NBER))

  • John McHale

    (J.E. Caines School of Business and Economics, University of Galway)

  • Alexander Oettl

    (National Bureau of Economic Research (NBER)
    Scheller College of Business, Georgia Institute of Technology)

Abstract

New product innovation in fields like drug discovery and material science can be characterized as combinatorial search over a vast range of possibilities. Modeling innovation as a costly multi-stage search process, we explore how improvements in artificial intelligence (AI) could affect the productivity of the discovery pipeline in allowing improved prioritization of innovations that flow through that pipeline. We show how AI-aided prediction can increase the expected value of innovation and can increase or decrease the demand for downstream testing, depending on the type of innovation, and examine how AI can reduce costs associated with well-defined bottlenecks in the discovery pipeline.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joevec:v:33:y:2023:i:5:d:10.1007_s00191-023-00845-3
    DOI: 10.1007/s00191-023-00845-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00191-023-00845-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00191-023-00845-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Roberts, Kevin & Weitzman, Martin L, 1981. "Funding Criteria for Research, Development, and Exploration Projects," Econometrica, Econometric Society, vol. 49(5), pages 1261-1288, September.
    2. Charles I. Jones, 2023. "Recipes and Economic Growth: A Combinatorial March Down an Exponential Tail," Journal of Political Economy, University of Chicago Press, vol. 131(8), pages 1994-2031.
    3. Aghion, Philippe & Howitt, Peter, 1992. "A Model of Growth through Creative Destruction," Econometrica, Econometric Society, vol. 60(2), pages 323-351, March.
    4. Gene M. Grossman & Elhanan Helpman, 1991. "Quality Ladders and Product Cycles," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 106(2), pages 557-586.
    5. Daron Acemoglu & Pascual Restrepo, 2018. "The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment," American Economic Review, American Economic Association, vol. 108(6), pages 1488-1542, June.
    6. Avinash K. Dixit & Robert S. Pindyck, 1994. "Investment under Uncertainty," Economics Books, Princeton University Press, edition 1, number 5474.
    7. Erik Brynjolfsson & Daniel Rock & Chad Syverson, 2018. "Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 23-57, National Bureau of Economic Research, Inc.
    8. Dosi, Giovanni, 1993. "Technological paradigms and technological trajectories : A suggested interpretation of the determinants and directions of technical change," Research Policy, Elsevier, vol. 22(2), pages 102-103, April.
    9. K. J. Arrow, 1971. "The Economic Implications of Learning by Doing," Palgrave Macmillan Books, in: F. H. Hahn (ed.), Readings in the Theory of Growth, chapter 11, pages 131-149, Palgrave Macmillan.
    10. Daniel A. Levinthal, 1997. "Adaptation on Rugged Landscapes," Management Science, INFORMS, vol. 43(7), pages 934-950, July.
    11. Samuel S. Kortum, 1997. "Research, Patenting, and Technological Change," Econometrica, Econometric Society, vol. 65(6), pages 1389-1420, November.
    12. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    13. Kauffman, Stuart & Lobo, Jose & Macready, William G., 2000. "Optimal search on a technology landscape," Journal of Economic Behavior & Organization, Elsevier, vol. 43(2), pages 141-166, October.
    14. Weitzman, Martin L, 1979. "Optimal Search for the Best Alternative," Econometrica, Econometric Society, vol. 47(3), pages 641-654, May.
    15. Lee Fleming, 2001. "Recombinant Uncertainty in Technological Search," Management Science, INFORMS, vol. 47(1), pages 117-132, January.
    16. 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.
    17. David H. Autor, 2015. "Why Are There Still So Many Jobs? The History and Future of Workplace Automation," Journal of Economic Perspectives, American Economic Association, vol. 29(3), pages 3-30, Summer.
    18. 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.
    19. Lee Fleming & Olav Sorenson, 2004. "Science as a map in technological search," Strategic Management Journal, Wiley Blackwell, vol. 25(8‐9), pages 909-928, August.
    20. Jan W. Rivkin, 2000. "Imitation of Complex Strategies," Management Science, INFORMS, vol. 46(6), pages 824-844, June.
    21. Tjur, Tue, 2009. "Coefficients of Determination in Logistic Regression Models—A New Proposal: The Coefficient of Discrimination," The American Statistician, American Statistical Association, vol. 63(4), pages 366-372.
    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. Qu, Guannan & Chen, Jin & Zhang, Ruhao & Wang, Luyao & Yang, Yayu, 2023. "Technological search strategy and breakthrough innovation: An integrated approach based on main-path analysis," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    2. Agrawal, Ajay & McHale, John & Oettl, Alexander, 2024. "Artificial intelligence and scientific discovery: a model of prioritized search," Research Policy, Elsevier, vol. 53(5).
    3. Koen Frenken & Alessandro Nuvolari, 2004. "Entropy statistics as a framework to analyse technological evolution," Chapters, in: John Foster & Werner Hölzl (ed.), Applied Evolutionary Economics and Complex Systems, chapter 5, Edward Elgar Publishing.
    4. Stephan Billinger & Nils Stieglitz & Terry R. Schumacher, 2014. "Search on Rugged Landscapes: An Experimental Study," Organization Science, INFORMS, vol. 25(1), pages 93-108, February.
    5. Karén Hovhannissian & Marco Valente, 2004. "Modeling Directed Local Search Strategies on Technology Landscapes: Depth and Breadth," ROCK Working Papers 028, Department of Computer and Management Sciences, University of Trento, Italy, revised 17 Jun 2008.
    6. Valente Houhannisian, 2004. "Modeling Directod Local Search Strategies on Technology Landscapes and Breadth," Quaderni DISA 091, Department of Computer and Management Sciences, University of Trento, Italy, revised 17 Jun 2008.
    7. Karén Hovhannisian & Marco Valente, 2005. "Modeling Directed Local Search Strategies on Technology," Computational Economics 0507001, University Library of Munich, Germany.
    8. Madeline K. Kneeland & Melissa A. Schilling & Barak S. Aharonson, 2020. "Exploring Uncharted Territory: Knowledge Search Processes in the Origination of Outlier Innovation," Organization Science, INFORMS, vol. 31(3), pages 535-557, May.
    9. Erin L. Scott & Pian Shu & Roman M. Lubynsky, 2015. "Are “Better” Ideas More Likely to Succeed? An Empirical Analysis of Startup Evaluation," Harvard Business School Working Papers 16-013, Harvard Business School.
    10. Taalbi, Josef, 2017. "What drives innovation? Evidence from economic history," Research Policy, Elsevier, vol. 46(8), pages 1437-1453.
    11. Frenken, Koen, 2006. "A fitness landscape approach to technological complexity, modularity, and vertical disintegration," Structural Change and Economic Dynamics, Elsevier, vol. 17(3), pages 288-305, September.
    12. Arthur Charpentier & Romuald Élie & Carl Remlinger, 2023. "Reinforcement Learning in Economics and Finance," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 425-462, June.
    13. Gatti, Corrado & Volpe, Loredana & Vagnani, Gianluca, 2015. "Interdependence among productive activities: Implications for exploration and exploitation," Journal of Business Research, Elsevier, vol. 68(3), pages 711-722.
    14. Dibiaggio, Ludovic & Nasiriyar, Maryam & Nesta, Lionel, 2014. "Substitutability and complementarity of technological knowledge and the inventive performance of semiconductor companies," Research Policy, Elsevier, vol. 43(9), pages 1582-1593.
    15. Oliver Baumann & Nicolaj Siggelkow, 2013. "Dealing with Complexity: Integrated vs. Chunky Search Processes," Organization Science, INFORMS, vol. 24(1), pages 116-132, February.
    16. Stanislao Gualdi & Antoine Mandel, 2019. "Endogenous growth in production networks," Journal of Evolutionary Economics, Springer, vol. 29(1), pages 91-117, March.
    17. Luca Grilli & Sergio Mariotti & Riccardo Marzano, 2024. "Artificial intelligence and shapeshifting capitalism," Journal of Evolutionary Economics, Springer, vol. 34(2), pages 303-318, April.
    18. Natalicchio, A. & Messeni Petruzzelli, A. & Garavelli, A.C., 2017. "Innovation problems and search for solutions in crowdsourcing platforms – A simulation approach," Technovation, Elsevier, vol. 64, pages 28-42.
    19. Svenja C. Sommer & Elliot Bendoly & Stylianos Kavadias, 2020. "How Do You Search for the Best Alternative? Experimental Evidence on Search Strategies to Solve Complex Problems," Management Science, INFORMS, vol. 66(3), pages 1395-1420, March.
    20. Arthur Charpentier & Romuald Elie & Carl Remlinger, 2020. "Reinforcement Learning in Economics and Finance," Papers 2003.10014, arXiv.org.

    More about this item

    Keywords

    Artificial intelligence; Innovation; R&D prioritization;
    All these keywords.

    JEL classification:

    • D20 - Microeconomics - - Production and Organizations - - - General

    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:spr:joevec:v:33:y:2023:i:5:d:10.1007_s00191-023-00845-3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.