Abstract Machine learning algorithms are increasingly able to predict what goods and
services particular people will buy, and at what price. It is possible to imagine a situation in …

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 …

The behavior of artificial intelligence (AI) algorithms is shaped by how they learn about their
environment. We compare the prices generated by AIs that use different learning protocols …

This special issue of the Oxford Review of Economic Policy, based on papers presented at a
Bank of England conference, explores the impact of artificial intelligence (AI) on financial …

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

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 …

We analyze how higher demand-forecasting precision affects firms' chances of sustaining
supracompetitive profits, depending on whether actions are observable or hidden. We …

The widespread use of market‐making algorithms in electronic over‐the‐counter markets
may give rise to unexpected effects resulting from the autonomous learning dynamics of …

There is a common assumption that the adoption of AI and ML in financial markets will make
markets more competitive and reduce consumer prices. This paper argues, however, that …

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 …