Artificial Intelligence and Collusion: A Literature Overview

Steven Uytsel ()
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Steven Uytsel: Kyushu University

A chapter in Robotics, AI and the Future of Law, 2018, pp 155-182 from Springer

Abstract: Abstract The use of algorithms in pricing strategies has received special attention among competition lawLaw scholars. There is an increasing number of scholars who argue that the pricing algorithms, facilitated by increased access to Big DataBig Data , could move in the direction of collusive price setting. Though this claim is being made, there are various responses. On the one hand, scholars point out that current artificial intelligence is not yet well-developed to trigger that result. On the other hand, scholars argue that algorithms may have other pricing results rather than collusion. Despite the uncertainty that collusive price could be the result of the use of pricing algorithms, a plethora of scholars are developing views on how to deal with collusive price setting caused by algorithms. The most obvious choice is to work with the legal instruments currently available. Beyond this choice, scholars also suggest constructing a new rule of reasonRule of reason . This rule would allow us to judge whether an algorithmAlgorithm could be used or not. Other scholars focus on developing a test environment. Still other scholars seek solutions outside competition lawLaw and elaborate on how privacyPrivacy regulation or transparencyTransparency reducing regulation could counteract a collusive outcome. Besides looking at lawLaw , there are also scholars arguing that technology will allow us to respond to the excesses of pricing algorithms. It is the purpose of this chapter to give a detailed overview of this debate on algorithms, price setting and competition lawLaw .

Keywords: Price fixing; Tacit collusion; Conscious parallelism; Rule of reason; Per se illegal; Algorithmic collusion; Monitoring algorithm; Parallel algorithm; Signaling algorithm; Self-learning algorithms; Reinforcement learning; Q-learning; Sandbox texting; White-box testing; Black-box testing (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/978-981-13-2874-9_7

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