Agrawal, Ajay
[Verfasser:in]
;
Oettl, Alex
[Sonstige Person, Familie und Körperschaft];
McHale, John
[Sonstige Person, Familie und Körperschaft]National Bureau of Economic Research
Erschienen:
Cambridge, Mass: National Bureau of Economic Research, April 2018
Erschienen in:NBER working paper series ; no. w24541
Umfang:
1 Online-Ressource
Sprache:
Englisch
DOI:
10.3386/w24541
Identifikator:
Reproduktionsnotiz:
Hardcopy version available to institutional subscribers
Entstehung:
Anmerkungen:
Mode of access: World Wide Web
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Beschreibung:
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