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Part IV What are the Risks if Competition Authorities Ignore or Downplay Big Data?, 12 Scale of Data: Trial-and-Error, ‘Learning-by-Doing’ Network Effects

Maurice E. Stucke, Allen P. Grunes

From: Big Data and Competition Policy

Maurice Stucke, Allen Grunes

From: Oxford Competition Law (http://oxcat.ouplaw.com). (c) Oxford University Press, 2015. All Rights Reserved.date: 20 September 2020

Market power — Rights — Internet — Technology

This chapter explores a second network effect, which arises from the scale of data: the more people who actively or passively contribute data, the more the company can improve the quality of its product, the more attractive the product is to other users. The chapter considers this data-driven network effect in three contexts: Waze’s navigation app, search engines, and Facebook’s digital assistant ‘M’. It argues that one can benefit (and one’s utility can increase) when others use the same search engine, since the quality of the search results can increase. As more people use the search engine, the more trial-and-error experiments, the more likely the search engine’s algorithms can learn of consumer preferences, the more relevant the search results will likely be, which in turn will attract others to use the search engine, and the positive feedback continues.

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