Algorithms Collusion And Competition Law

What is algorithmic collusion? This evaluative book provides an insight into tackling this important question for competition law, with contrasting critical perspectives, including theoretical, empirical, and doctrinal – the latter frequently from a comparative perspective. Bringing together scholarly discussion on algorithmic collusion, the book questions whether competition law is adeptly equipped to deal with its various facets.

Ariel Ezrachi and Maurice Stucke take a hard look at today’s app-assisted paradise of digital shopping. The algorithms and data-crunching that make online purchasing so convenient are also changing the nature of the market by shifting power into the hands of the few, with risks to competition, our democratic ideals, and our overall well-being.

The digital economy, broadly defined as the economy operating on the basis of interconnectivity between people and businesses, has gradually spread over the world. Although a global phenomenon, the digital economy plays out in local economic, political, and regulatory contexts. The problems thus created by the digital economy may be approached differently depending on the context. This edited collection brings together leading scholars based in Asia to detail how their respective jurisdictions respond to the competition law problems evolving out of the deployment of the digital economy. This book is timely, because it will show to what extent new competition law regimes or those with a history of lax enforcement can respond to these new developments in the economy. Academics in law and business strategies with an interest in competition law, both in Asia and more broadly, will find the insights in this edited collection invaluable. Further, this volume will be a key resource for scholars, practitioners and students.

A review of the theoretical research on unlawful collusion, focusing on the impact and optimal design of competition law and enforcement. Collusion occurs when firms in a market coordinate their behavior for the purpose of producing a supracompetitive outcome. The literature on the theory of collusion is deep and broad but most of that work does not take account of the possible illegality of collusion. Recently, there has been a growing body of research that explicitly focuses on collusion that runs afoul of competition law and thereby makes firms potentially liable for penalties. This book, by an expert on the subject, reviews the theoretical research on unlawful collusion, with a focus on two issues: the impact of competition law and enforcement on whether, how long, and how much firms collude; and the optimal design of competition law and enforcement. The book begins by discussing general issues that arise when models of collusion take into account competition law and enforcement. It goes on to consider game-theoretic models that encompass the probability of detection and penalties incurred when convicted, and examines how these policy instruments affect the frequency of cartels, cartel duration, cartel participation, and collusive prices. The book then considers the design of competition law and enforcement, examining such topics as the formula for penalties and leniency programs. The book concludes with suggested future lines of inquiry into illegal collusion.

Algorithms are ubiquitous in our daily lives. They affect the way we shop, interact, and make exchanges on the marketplace. In this regard, algorithms can also shape competition on the marketplace. Companies employ algorithms as technologically innovative tools in an effort to edge out competitors. Antitrust agencies have increasingly recognized the competitive benefits, but also competitive risks that algorithms entail. Over the last few years, many algorithm-driven companies in the digital economy have been investigated, prosecuted and fined, mostly for allegedly unfair algorithm design. Legislative proposals aim at regulating the way algorithms shape competition. Consequently, a so-called “algorithmic antitrust” theory and practice have also emerged. This book provides a more innovation-driven perspective on the way antitrust agencies should approach algorithmic antitrust. To date, the analysis of algorithmic antitrust has predominantly been shaped by pessimistic approaches to the risks of algorithms on the competitive environment. With the benefit of the lessons learned over the last few years, this book assesses whether these risks have actually materialized and whether antitrust laws need to be adapted accordingly. Effective algorithmic antitrust requires to adequately assess the pro- and anti-competitive effects of algorithms on the basis of concrete evidence and innovation-related concerns. With a particular emphasis on the European perspective, this book brings together experts and scrutinizes on the implications of algorithmic antitrust for regulation and innovation.

"This thesis demonstrates algorithmic collusion in a price oligopoly with multinomial logit demand. Of main interest is the scenario where competing firms independently adopt copies of the same price algorithm. One of the key takeaways is that in this scenario, major challenges to achieving and sustaining supra-competitive outcomes can be overcome by the firms. We show this by constructing competitive price algorithms that have collusive capabilities when at least one of the opponents uses a copy of the same algorithm. Importantly, the firms do not have to activate the algorithm at the same time, do not need to use the same input parameters, and do not need to know that they are using the same algorithm. Moreover, the proposed algorithms do not engage in illicit forms of communication or signaling, and therefore exemplify tacit (lawful) algorithmic collusion . This thesis thus contributes to the ongoing debate on the extent to which collusion by algorithms is possible within the confines of existing antitrust law and jurisprudence, and further contributes to the fields of axiomatic bargaining, dynamic pricing, and demand learning in the presence of competitors."--

Big Data and Big Analytics are a big deal today. Big Data is playing a pivotal role in many companies' strategic decision-making. Companies are striving to acquire a 'data advantage' over rivals. Data-driven mergers are increasing. These data-driven business strategies and mergers raise significant implications for privacy, consumer protection and competition law. At the same time, European and United States' competition authorities are beginning to consider the implications of a data-driven economy on competition policy. In 2015, the European Commission launched a competition inquiry into the e-commerce sector and issued a statement of objections in its Google investigation. The implications of Big Data on competition policy will likely be a part of the mix. Big Data and Competition Policy is the first work to offer a detailed description of the important new issue of Big Data and explains how it relates to competition laws and policy, both in the EU and US. The book helps bring the reader quickly up to speed on what is Big Data, its competitive implications, the competition authorities' approach to data-driven mergers and business strategies, and their current approach's strengths and weaknesses. Written by two recognized leading experts in competition law, this accessible work offers practical guidance and theoretical discussion of the potential benefits (including data-driven efficiencies) and concerns for the practitioner, policy maker, and academic alike.

This book is devoted to the encounter and interaction of agents such as robots with other agents and describes how they cooperate with their previously unknown teammates, forming an Ad Hoc team. It presents a new algorithm, PLASTIC, that allows agents to quickly adapt to new teammates by reusing knowledge learned from previous teammates. PLASTIC is instantiated in both a model-based approach, PLASTIC-Model and a policy-based approach, PLASTIC-Policy. In addition to reusing knowledge learned from previous teammates, PLASTIC also allows users to provide expert-knowledge and can use transfer learning (such as the new Two Stage Transfer algorithm) to quickly create models of new teammates when it has some information about its new teammates. The effectiveness of the algorithm is demonstrated on three domains, ranging from multi-armed bandits to simulated robot soccer games.