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Digital
Year: 2018 Publisher: Cambridge, Mass. National Bureau of Economic Research
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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.
Digital
Year: 2018 Publisher: Cambridge, Mass. National Bureau of Economic Research
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The recruitment of foreign scientists enhances US science through an expanded workforce but could also cause harm by displacing better connected domestic scientists, thereby reducing localized knowledge spillovers. We develop a model in which a sufficient condition for the absence of overall harm is that immigrant scientists generate at least the same level of localized spillovers as the domestic scientists they displace. To test this condition, we conduct an experiment in which each immigrant hypothetically displaces an appropriately matched domestic scientist. Overall, we do not find evidence that immigrant scientists harm US science by crowding out better-connected domestic scientists.
Book
Year: 2018 Publisher: Cambridge, Mass. National Bureau of Economic Research
Abstract | Keywords | Export | Availability | Bookmark
Loading...Choose an application
- Reference Manager
- EndNote
- RefWorks (Direct export to RefWorks)
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.
Book
Year: 2018 Publisher: Cambridge, Mass. National Bureau of Economic Research
Abstract | Keywords | Export | Availability | Bookmark
Loading...Choose an application
- Reference Manager
- EndNote
- RefWorks (Direct export to RefWorks)
The recruitment of foreign scientists enhances US science through an expanded workforce but could also cause harm by displacing better connected domestic scientists, thereby reducing localized knowledge spillovers. We develop a model in which a sufficient condition for the absence of overall harm is that immigrant scientists generate at least the same level of localized spillovers as the domestic scientists they displace. To test this condition, we conduct an experiment in which each immigrant hypothetically displaces an appropriately matched domestic scientist. Overall, we do not find evidence that immigrant scientists harm US science by crowding out better-connected domestic scientists.
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