Prices are increasingly set by algorithms. One concern is that intelligent algorithms may
learn to collude on higher prices even in the absence of the kind of coordination necessary …

We show that if they are allowed enough time to complete the learning, Q-learning
algorithms can learn to collude in an environment with imperfect monitoring adapted from …

This Article offers a novel perspective on the implications of increasingly autonomous and"
black box" algorithms, within the ramification of algorithmic trading, for the integrity of capital …

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

Problem definition: This paper addresses the question whether or not self-learning
algorithms can learn to collude instead of compete against each other, without violating …

I study self-learning pricing algorithms and show that they are collusive in market
simulations. To derive a counterfactual that resembles traditional tacit collusion, I conduct …

Increasingly, firms use algorithms powered by artificial intelligence to set prices. Previous
research simulated interactions among Q-learning algorithms in an oligopoly model of price …

Deep reinforcement learning (DRL) is decisive in addressing uncertainties in intelligent grid-
building interactions. Using DRL algorithms, this research optimizes the operational strategy …

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

Algorithmic pricing on online e-commerce platforms raises the concern of tacit collusion,
where reinforcement learning algorithms learn to set collusive prices in a decentralized …