We examine recent claims that a particular Q-learning algorithm used by competitors 'autonomously' and systematically learns to collude, resulting in supracompetitive prices and extra profits for the firms sustained by collusive equilibria. A detailed analysis of the inner workings of this...

In the context of repeated first-price auctions, we explore how a bid-rigging cartel can simultaneously overcome the difficulty of soliciting truthful private information about valuations and the difficulty of enforcing its internal mechanism. Focusing on the class of trigger-strategy collusive...

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,...

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....

To make sense of mixed empirical evidence on the pricing rigidity of cartels, this paper studies how colluding firms price their goods in a model where firms have private marginal costs and sell to consumers with search costs. In this model, firms repeatedly interact in selling a homogeneous...