The Influence of Collusion on Price Changes: New Evidence from Major Cartel Cases

In this paper, we compare the distribution of price changes between collusive and noncollusive periods for ten major cartels. The first moments focus on previous research. We extend the discussion to the third (skewness) and fourth (kurtosis) moments. However, none of the above descriptive statistics can be considered as a robust test allowing a differentiation between competition and cartel. Therefore, we implement the Kolmogorov-Smirnov test. According to our results, 8 out of 10 cartels were successful in controlling the market price for a number of years. The proposed methodology may be used for antitrust screening and regulatory purposes.Cartel detection, collusion, competition policy

The Reality of Bytes: Regulating Economic Activity in the Age of the Internet

By utilizing both a backward and forward looking perspective, this Article develops a model conducive to better understand the Internet\u27s legal implications on economic regulation. The model is also intended to help legislators and regulators adapt their legal and regulatory frameworks to the Internet. This Article canvasses and builds upon the burgeoning development of Internet law. It suggests that the Internet\u27s impact on economic regulation is best understood by classifying its effects into four categories, each of which requires a different regulatory response. It also considers potential solutions for adapting economic regulation to the Internet. This Article concludes that no single suitable solution or analogy remedies the regulatory challenges posed by the Internet. Rather, as in real space, a combination of approaches is necessary to create an effective regulatory framework

Learning Constrained Dynamics with Gauss Principle adhering Gaussian Processes

The identification of the constrained dynamics of mechanical systems is often challenging. Learning methods promise to ease an analytical analysis, but require considerable amounts of data for training. We propose to combine insights from analytical mechanics with Gaussian process regression to improve the model's data efficiency and constraint integrity. The result is a Gaussian process model that incorporates a priori constraint knowledge such that its predictions adhere to Gauss' principle of least constraint. In return, predictions of the system's acceleration naturally respect potentially non-ideal (non-)holonomic equality constraints. As corollary results, our model enables to infer the acceleration of the unconstrained system from data of the constrained system and enables knowledge transfer between differing constraint configurations.Comment: To be published in 2nd Annual Conference on Learning for Dynamics and Control (L4DC), Proceedings of Machine Learning Research 202