This paper provides an overview of recent developments in algorithmic antitrust, and the economics and legal issues raised in the areas of abuse of dominance, algorithmic pricing and collusion, and mergers and acquisition. The general theme is that while much has been made of the possible...

Pricing algorithms are increasingly replacing human decision making in real marketplaces. To inform the competition policy debate on possible consequences, we run experiments with pricing algorithms powered by Artificial Intelligence in controlled environments (computer simulations).In...

We present a methodology for analyzing the impact of algorithmic recommendations on product market competition, addressing concerns that have been raised in both academic and policy circles regarding their potential anti-competitive effects. Our analysis demonstrates that recommender systems...

We analyze the effects of better algorithmic demand forecasting on collusive profits. We show that the comparative statics crucially depend on the whether actions are observable. Thus, the optimal antitrust policy needs to take into account the institutional settings of the industry in question....

We analyze the effects of better algorithmic demand forecasting on collusive profits. We show that the comparative statics crucially depend on the whether actions are observable. Thus, the optimal antitrust policy needs to take into account the institutional settings of the industry in question....

Platforms play an essential role in the modern economy. At the same time, algorithms are becoming more widely used for pricing and other business functions. Previous literature examined algorithmic pricing, but not in the context of network effects and platforms. Moreover, platform competition...

Increasingly, retailers have access to better pricing technology, especially in online markets. Firms employ automated pricing algorithms that allow for high-frequency price changes. What are the implications for price competition? We develop a model of price competition where firms can differ...

As self-learning pricing algorithms become popular, there are growing concerns among academics and regulators that algorithms could learn to collude tacitly on non-competitive prices and thereby harm competition. I study popular reinforcement learning algorithms and show that they develop...

We analyze pricing patterns and price level effects of algorithms in the market segments for OTC-antiallergics and -painkillers in Germany. Based on a novel hourly dataset which spans over four months and contains over 10 million single observations, we produce the following results. First,...

Recent experimental simulations have shown that autonomous pricing algorithms are able to learn collusive behavior and thus charge supra-competitive prices without being explicitly programmed to do so. These simulations assume, however, that both firms employ the identical price-setting...