A burgeoning literature shows that self-learning algorithms may, under some conditions, reach seemingly-collusive outcomes: after repeated interaction, competing algorithms earn supra-competitive profits, at the expense of efficiency and consumer welfare. However, these simulations results,...

Strategic decisions are increasingly delegated to algorithms. We extend the results of Waltman and Kaymak [2008] and Calvano et al. [2020b] to the context of dynamic optimization with imperfect monitoring by analyzing a setting where a limited number of agents use simple and independent...

Digital platforms frequently refer their users to competitors. We show that these references induce a business-sharing effect that may relax competition for users, resulting in lower quality of content. More surprisingly, user surplus may also decrease as the quality effect may overwhelm the...

The best-performing and most popular algorithms are often the least explainable. In parallel, there is growing concern and evidence that sophisticated algorithms may engage, autonomously, in profit-maximizing but welfare-damaging strategies. Drawing on the literature on self-regulation and...