Third-Party Pricing Algorithms and Information Sharing

We analyze the effects of information sharing in oligopoly when firms outsource pricing to a common third-party pricing algorithm developer. In a model where algorithms tailor prices to high-frequency demand shocks, we compare regimes that allow or prohibit conditioning on rival-specific shocks. Information sharing makes algorithmic prices more sensitive to seller-specific demand shocks---own and rival---and more correlated across sellers. These effects are stronger under common third-party algorithm design than under independent design because the third party's objective generates greater strategic complementarity in pricing than independent profit maximization. Information sharing harms expected consumer surplus more under common third-party design than under independent design, and its welfare effects are reversed across the two cases: information sharing lowers expected welfare under common third-party design while raising it under independent design. Our findings provide theoretical support for recent antitrust scrutiny of common third-party pricing algorithms that incorporate competitor data.