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Competition and Network Effects

The paper linked to above discusses how network effects and data harvesting don’t make companies as impervious to competition as some have claimed. The three “critical points” it makes towards proving this end are: network effects frequently indirectly benefit different groups by connecting them together, certain potential users are more valuable than others based on their relation to existing users, and network effects can work in reverse. The first point was made based on an analysis of Youtube, where the power of the platform stemmed from having both posters and viewers and connecting them, rather from simply trying to attain as many users as possible. The second point was made by analyzing platforms such as OpenTable, which initially strove for rapid growth that ended up backfiring since the platform was not particularly useful to any of its users, then transitioned to targeting denser markets and excelling there. The third point, essentially just an explanation of equilibria like we’ve seen in class, was made while commenting on how users may use multiple platforms at once and try new ones at little cost. The authors then use those three points, along with examples of large platforms that have either failed or had financial difficulties, to make the case that simply being a large platform that benefits from network effects doesn’t make something immune to competition and that they aren’t ‘winner-take-all.’ The paper also discusses the value of data collected by large platforms, citing numerous examples where data didn’t save companies from competitors entering the market. It concludes by making the case that a company dependent on network effects doesn’t inherently require anti-trust regulation (though anti-trust should still apply as normal for anti-competitive behaviors). I personally agree, swayed by the case that the ability for users to try multiple platforms simultaneously allows competition in spite of network effects. A new platform won’t reach the critical market share to dominate the existing leader just after a few people start trying it in addition, but that partial support does allow for the platform to gradually gain market share and compete (instead of trending back down towards zero) in a way that wouldn’t happen with a more simplified model of network effects.


The points made by the paper clearly relate to our study of network effects. The first two points add a bit of nuance to network effects, focusing on the relative value of a specific potential user in relation to the existing users, whereas our study only really focused on the quantity of users. The third point, however, is nothing new to us; it is merely the same understanding of equilibria in network effects that we have. We already know, and have seen in a problem set, that a usage drop past a certain critical point will cause a downward transition to a stable equilibrium with much lower usage of the platform.


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November 2018