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 to establish an antitrust infringement. However, exactly how this may happen is an open question. I show how in simulated sequential competition, competing reinforcement learning algorithms can indeed learn to converge to collusive equilibria when the set of discrete prices is limited. When this set increases, the algorithm considered increasingly converges to supra-competitive asymmetric cycles. I show that results are robust to various extensions and discuss practical limitations and policy implications. "It's true that the idea of automated systems getting together and reaching a meeting of minds is still science fiction. (…) But we do need to keep a close eye on how algorithms are developing. (…) So that when science fiction becomes reality, we're ready to deal with it." -EU Competition Commissioner Margrethe Vestager (2017)
There is a growing concern that U.S. merger control may have been too lenient, but empirical evidence remains limited. After reviewing event studies as a method to acquire empirical insights into the competitive effects of mergers, I propose a novel application using Hoberg-Phillips TNIC data. This application allows for the ready approximation of abnormal stock market returns of likely competitors to 1,751 of the largest U.S. mergers since 1997. I document that likely competitors experience on average an abnormal return of close to one percent around a merger announcement. Abnormal returns are also strongly associated with concerns of market power, which suggests that competitors benefit at least in part because of an anticipation of anti-competitive effectsand hence insufficient merger control.
In the modern economy, algorithms influence many aspects of our lives, from how much we pay for groceries and what adverts we see, to the decisions taken by health professionals. As is true with all new technologies, algorithms bring new economic opportunities and make our lives easier, but they also bring new challenges. Indeed, many competition authorities have voiced their concerns that under certain circumstances algorithms may harm consumers, lead to exclusion of some competitors and may even enable firms (knowingly or otherwise) to avoid competitive pressure and collude. In this article, we explain how algorithms work and what potential benefits and harms they bring to competition.
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