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Finding Needles in Haystacks: Artificial Intelligence and Recombinant Growth

Ajay Agrawal, John McHale, Alexander Oettl

Chapter in NBER book The Economics of Artificial Intelligence: An Agenda (2019), Ajay Agrawal, Joshua Gans, and Avi Goldfarb, editors (p. 149 - 174)
Conference held September 13-14, 2017
Published in May 2019 by University of Chicago Press
© 2019 by the National Bureau of Economic Research

Innovation is often predicated on discovering useful new combinations of existing knowledge in highly complex knowledge spaces. These needle-in-a-haystack type problems are pervasive in fields like genomics, drug discovery, materials science, and particle physics. We develop a combinatorial-based knowledge production function and embed it in the classic Jones growth model (1995) to explore how breakthroughs in artificial intelligence (AI) that dramatically improve prediction accuracy about which combinations have the highest potential could enhance discovery rates and consequently economic growth. This production function is a generalization (and reinterpretation) of the Romer/Jones knowledge production function. Separate parameters control the extent of individual-researcher knowledge access, the effects of fishing out/complexity, and the ease of forming research teams.

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This chapter first appeared as NBER working paper w24541, Finding Needles in Haystacks: Artificial Intelligence and Recombinant Growth, Ajay Agrawal, John McHale, Alex Oettl
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