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Machine learning could improve innovation policy

Nature Machine Intelligence volume 2, page 84 (2020)Cite this article

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To the Editor — Understanding factors that affect the rate and direction of innovation has been a central aim of research in the study of science and innovation for more than half a century. However, substantially more progress has been achieved in understanding the factors that drive the rate of innovation than its direction — the set of research topics scientists or research institutions tackle at a given point in time (diversity) and over time (trajectory).

Economists have long understood that markets provide insufficient incentives for innovation because of the difficulty of fully appropriating the returns to innovation investments, particularly when new innovations build on older ones1. Similar features inhibit investment in the diversity of innovations2,3, thus potentially impacting their trajectory. For example, investment in environmentally friendly technologies may have been inhibited by initial successes using fossil-fuel technologies and hence scarce investments in alternative technologies3. These insights show that more empirical research is needed to uncover what factors influence the direction of innovation.

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References

  1. Arrow, K. in The Rate and Direction of Inventive Activity: Economic and Social Factors 609–626 (Princeton Univ. Press, 1962).

  2. Aghion, P., Dewatripont, M. & Stein, J. C. Rand J. Econ. 39, 617–635 (2008).

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  3. Acemoglu, D. Diversity and Technological Progress: NBER Working Paper No. 16984 (NBER, 2011).

  4. Sinha, A. et al. in Proc. 24th Int. Conf. World Wide Web 243–246 (ACM, 2015).

  5. Furman, J. L. & Teodoridis, F. Organ. Sci. https://doi.org/10.1287/orsc.2019.1308 (2020).

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Authors and Affiliations

  1. Boston University, Boston, MA, USA

    Jeffrey L. Furman

  2. National Bureau of Economic Research, Cambridge, MA, USA

    Jeffrey L. Furman

  3. University of Southern California, Los Angeles, CA, USA

    Florenta Teodoridis

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  1. Jeffrey L. Furman
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  2. Florenta Teodoridis
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Correspondence to Florenta Teodoridis.

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The authors declare that there are no competing interests.

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Furman, J.L., Teodoridis, F. Machine learning could improve innovation policy. Nat Mach Intell 2, 84 (2020). https://doi.org/10.1038/s42256-020-0155-8

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