Algorithmic Collusion: Insights from Deep Learning

Matthias Hettich

No 9421, CQE Working Papers from Center for Quantitative Economics (CQE), University of Muenster

Abstract: Increasingly, firms use algorithms powered by artificial intelligence to set prices. Previous research simulated interactions among Q-learning algorithms in an oligopoly model of price competition. The algorithms learn collusive strategies but require a long time that corresponds to several years to do so. We show that pricing algorithms using deep learning (DQN) can collude significantly faster. The availability of these more powerful pricing algorithms enables simulations in larger markets. Collusion disappears in wide oligopolies with up to 10 firms. However, incorporating knowledge of the learning behavior by reformulating the state representation increases the ability to collude effectively.

Keywords: Algorithmic Pricing; Collusion; Artificial Intelligence; Reinforcement Learning; DQN (search for similar items in EconPapers)
JEL-codes: D21 D43 D83 L12 L13 (search for similar items in EconPapers)
Pages: 20 pages
Date: 2021-11
New Economics Papers: this item is included in nep-big, nep-cmp, nep-com, nep-ind and nep-reg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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