Allyn Young’s concept of increasing returns (not to be confused with static, equilibrium constructs of economies of scale and increasing returns to scale) is applied to analyse how and why increasing returns arise in the production (generation) and use (application) of knowledge and big data, thereby driving economic growth and progress. Knowledge is chosen as our focus because it is said to be our most powerful engine of production, and big data are included to make the analysis more complete and recent. We analyse four mechanisms or sources of increasing returns in the production of knowledge, and four in the use of knowledge. Turning to big data, we analyse increasing returns in the functioning of digital platforms and increasing returns in machine learning from gigantic amounts of training data. Concluding remarks concern some key differences between big data and knowledge, some policy implications, and some of the social negative impacts from the ways in which big data are being used.

PAGES
10 – 29
DOI
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Issues
Also in this issue:
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Agnes Horvath, Magic and the Will to Science: A Political Anthropology of Liminal Technicality
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Gibson Burrell, Ronald Hartz, David Harvie, Geoff Lightfoot, Simon Lilley and Friends, Shaping for Mediocrity: The Cancellation of Critical Thinking at our Universities
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Bas de Boer, How Scientific Instruments Speak: Postphenomenology and Technological Mediations in Neuroscientific Practice
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Bjørn Lomborg, False Alarm
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How does innovation arise in the bicycle sector? The users’ role and their betrayal in the case of the ‘gravel bike’
The impact of increasing returns on knowledge and big data: from Adam Smith and Allyn Young to the age of machine learning and digital platforms
Paper