We examine recent claims that a particular Q-learning algorithm used by
competitorsautonomously'and systematically learns to collude, resulting in supracompetitive …

A booming literature describes how artificial intelligence algorithms may autonomously learn
to generate supra-competitive profits. The widespread interpretation of this phenomenon as …

Abstract We let" Algorithmic Market-Makers"(AMMs), using Q-learning algorithms, choose
prices for a risky asset when their clients are privately informed about the asset payoff. We …

There has been substantial recent concern that pricing algorithms might learn
to``collude.''Supra-competitive prices can emerge as a Nash equilibrium of repeated pricing …

Research on the possibility of algorithmic collusion has rapidly expanded in recent years
and has come to the attention of competition authorities worldwide. Claims regarding the …

We develop tools for quantifying the likelihood of observing collusive strategies among
provably convergent decentralized multiagent reinforcement learning (MARL) algorithms in …

We analyze the probability of observing collusion by Decentralized Q-learning in a pricing
duopoly with three possible prices and a memory of one period. Using the logit demand …

Artificial intelligence has helped fuel a growing market in the supply of pricing algorithms by
software developers. While there is an efficiency rationale for outsourcing pricing …

This article challenges the idea of algorithmic collusion as proposed in Calvano et al.(2020)
and subsequent literature. Identifying a critical mistake, we dispute the notion that …

Concern about algorithmic collusion has initiated a stream of literature that claims to
construct algorithms that collude, but also a stream of literature that criticizes these claims …