NATIONAL BUREAU OF ECONOMIC RESEARCH
NATIONAL BUREAU OF ECONOMIC RESEARCH
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Finding Needles in Haystacks: Artificial Intelligence and Recombinant Growth

Ajay Agrawal, John McHale, Alex Oettl

NBER Working Paper No. 24541
Issued in April 2018
NBER Program(s):Economic Fluctuations and Growth Program, Productivity, Innovation, and Entrepreneurship Program

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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Document Object Identifier (DOI): 10.3386/w24541

Published: Finding Needles in Haystacks: Artificial Intelligence and Recombinant Growth, Ajay Agrawal, John McHale, Alexander Oettl. in The Economics of Artificial Intelligence: An Agenda, Agrawal, Gans, and Goldfarb. 2019

 
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