Harmful Signals - Article 101 TFEU and Oligopoly Theory in the Age of Machine Learning

HARMFUL SIGNALS: CARTEL PROHIBITION AND OLIGOPOLY THEORY IN THE AGE OF MACHINE LEARNING

Journal of Competition Law & Economics ◽

10.1093/joclec/nhz011 ◽

2019 ◽

Cited By ~ 2

Author(s):

Stefan Thomas

Keyword(s):

Machine Learning ◽

European Union ◽

Consumer Welfare ◽

Tacit Collusion ◽

The European Union ◽

Oligopoly Theory ◽

Communicative Acts ◽

Sherman Act ◽

Learning Capabilities ◽

The U.S

Abstract The traditional legal approach for distinguishing between illicit collusion and legitimate oligopoly conduct is to rely on criteria that relate to the means and form of how rivals interact, such as elements of “practical cooperation”, or on the finding of an anticompetitive intent. These criteria ultimately refer to the inner sphere of natural persons and its emanations in communicative acts. Some authors therefore conclude that the cartel prohibition of Article 101 Treaty on the Functioning of the European Union (TFEU) or Section 1 of the U.S. Sherman Act is unable to capture collusion if it is achieved by autonomously acting computers relying on machine learning capabilities. It is instead suggested here to define collusion as parallel informational signals, which achieve a supracompetitive equilibrium, and to use the consumer welfare standard as a proxy for distinguishing between illicit collusion and legitimate oligopoly conduct. This approach is not tantamount to the idea of prohibiting tacit collusion as such. Rather, it is to check singular elements of communication, that is, “informational signals”, within an existing oligopolistic setting for their propensity to create consumer harm. This approach can help to close potential regulatory gaps currently associated with the surge of algorithmic pricing.

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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