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ALGORITHMIC COLLUSION: A REAL PROBLEM FOR COMPETITION POLICY?

Authors: Emilio Calvano; Giacomo Calzolari; Vincenzo Denicolò; Sergio Pastorello;

ALGORITHMIC COLLUSION: A REAL PROBLEM FOR COMPETITION POLICY?

Abstract

Recently, antitrust authorities started to worry about the possible consequences of algorithmic pricing. Indeed, we document that pricing algorithms are already widely used and argue that they are likely to become even more prevalent in the future. In particular, authorities worry about data-driven price discrimination and algorithmic collusion. We focus on the latter. It is the contention of this article that algorithmic collusion is a real risk, the seriousness of which is still difficult to assess, but that should not be dismissed lightly by antitrust agencies. First, we discuss various ways in which algorithms may facilitate collusion without creating any genuinely new antitrust issue. Second, we argue that pricing algorithms may learn to collude “autonomously” and without explicitly communicating with one another. In light of this evidence, we discuss the specific new policy challenges that this kind of algorithmic collusion poses.

Country
Italy
Keywords

Algorithmic collusion, competition in digital markets, Settore SECS-P/01 - ECONOMIA POLITICA

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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