Internet markets tend to concentrate in the hands of a very few large platforms. These platforms have been accused of abusing their monopoly power vis-a-vis their users (consumers, sellers, advertisers) and maintaining the latter through hostile behavior towards potential competitors. They are said to harm users by ‘self-preferencing’, data harvesting, creating 'monopoly positions' and extracting resulting rents with high fees, all the while avoiding competition by acquiring, copying and otherwise disadvantaging potential competitors. This proposal addresses these concerns in four parts. Part 1 focuses on the dual role of online marketplaces, whereby the platform both runs the marketplace and acts as a seller on it. I aim to understand how such hybrid marketplaces conduct themselves toward consumers and third party sellers. The model will be used to evaluate recent policy proposals and suggest ways to avoid significant unintended consequences. Part 2 studies how platforms steer consumers to sellers. As most platforms let sellers set prices and collect fees on revenues, a platform’s own algorithm and her choice to augment/replace it with a position auction may be consequently driven by revenue maximization. I plan to show that steering systems may drastically alter pricing, leading to ‘mediated’ competition. Part 3 explores the nature of recommendation algorithms, particularly the interplay between consumer search and algorithm effectiveness. I demonstrate that algorithms may be self-fulfilling and self-defeating, which determines their effectiveness and significantly alters the resulting allocations and their efficiency. Part 4 explains the circumstances in which an incumbent platform may acquire an upstart platform. These depend on, the overlap of existing user bases, increasing returns to data and monopoly power over advertisers. Acquisitions may be used in situations of both no and substantial overlap in user bases, with mixed welfare consequences.
- HORIZON.1.1 - European Research Council (ERC) Main Programme