A novel debate within competition policy and regulation circles is whether autonomous machine learning algorithms may learn to collude on prices. We show that when fims face short-run price commitments, independent Q-learning (a simple but well-established self-learning algorithm) learns to...

This paper discusses the question whether self-learning price-setting algorithms are able to coordinate their pricing behaviour to achieve a collusive outcome that maximizes the joint profits of the firms using these algorithms. While the legal literature generally assumes that algorithmic...

The paper uses a machine learning technique to build up a screen for collusive behavior. Such tools can be applied by competition authorities but also by companies to screen the behavior of their suppliers. The method is applied to the German retail gasoline market to detect anomalous behavior...

This compares the magnitudes of two forms of economic interaction between the developed and developing world. The first is the amount of economic foreign aid provided by the developed world to the developing world during a single year. The second is an estimate of the yearly amount that illegal...

Regression methods are commonly used in competition lawsuits for, e.g., determining overcharges in pricefixing cases. Technical evaluations of these methods' pros and cons are not necessarily intuitive. Appraisals that are based on case studies are descriptive but need not be universally valid....

There have been a number of studies attempting to quantify the impact of cartels and mergers on prices. The state of the art of empirical analysis related to antitrust is best illustrated by the research of John Connor and John Kwoka. Connor summarizes the existing empirical research that...