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...

A booming literature describes how artificial intelligence algorithms (AIAs) may autonomously learn to generate supra-competitive profits. Key to the widespread interpretation of the phenomena as ``collusion'', is the observation that the unilateral price cuts of an agent are followed by several...

This article explains the supra-competitive profits observed in Calvano et al.(2020) and subsequent literature. We refute the idea that the phenomenon arises from collusive mechanisms, where each agent exerts the threat of punishment. Instead, our analysis reveals that the high prices and...

The best-performing and most popular algorithms are often the least explainable. In parallel, there is growing concern and evidence that algorithms may engage, autonomously, in welfaredamaging strategies. Inspired by recent regulatory proposals, we model a firm’s compliance strategy under the...