Competing with Big Data
Abstract: In this paper we study competition in markets as diverse as search engines, maps, and self-driving cars. We identify the common characteristic of these - and many other - markets: they are driven by big data. Specifically, the cost of quality production in these markets is decreasing in the amount of machine-generated data about user behavior, which is a natural, inseparable byproduct of using services offered in such markets, thereby giving rise to indirect network effects. We construct a dynamic model of R&D competition and show that such markets tip under very mild conditions, moving towards monopoly. We also show how a dominant firm in one market can leverage its position to another data-driven market, thereby initiating a domino effect. We apply the model to contemporary cases, offer a welfare analysis, and propose a regulatory measure how to mitigate the negative effects of indirect network effects on innovation, consumer surplus, and total welfare.