Autonomous Algorithmic Collusion: Q-Learning Under Sequantial Pricing

Timo Klein ()
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Timo Klein: University of Amsterdam

No 18-056/VII, Tinbergen Institute Discussion Papers from Tinbergen Institute

Abstract: Prices are increasingly set by algorithms. One concern is that intelligent algorithms may learn to collude on higher prices even in absence of the kind of communication or agreement necessary to establish an antitrust infringement. However, exactly how this may happen is an open question. I show in a simulated environment of sequential competition that competing reinforcement learning algorithms can indeed learn to converge to collusive equilibria. When the set of discrete prices increases, the algorithm considered increasingly con- verges to supra-competitive asymmetric cycles. I show that results are robust to various extensions and discuss practical limitations and policy implications.

Keywords: Algorithmic Collusion; Pricing Algorithms; Machine Learning; Reinforcement Learning; Q-Learning (search for similar items in EconPapers)
JEL-codes: D43 D83 L13 L41 (search for similar items in EconPapers)
Date: 2018-06-21, Revised 2020-11-01
New Economics Papers: this item is included in nep-big, nep-cmp, nep-com and nep-law
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:tin:wpaper:20180056

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