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Autonomous algorithmic collusion: Q‐learning under sequential pricing
In: The Rand journal of economics, Band 52, Heft 3, S. 538-558
ISSN: 1756-2171
AbstractPrices are increasingly set by algorithms. One concern is that intelligent algorithms may learn to collude on higher prices even in the absence of the kind of coordination necessary to establish an antitrust infringement. However, exactly how this may happen is an open question. I show how in simulated sequential competition, competing reinforcement learning algorithms can indeed learn to converge to collusive equilibria when the set of discrete prices is limited. When this set increases, the algorithm considered increasingly converges to supra‐competitive asymmetric cycles. I show that results are robust to various extensions and discuss practical limitations and policy implications.
Event Studies in Merger Analysis: Review and an Application Using U.S. TNIC Data
In: Amsterdam Law School Research Paper No. 2020-02
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Working paper
Autonomous Algorithmic Collusion: Q-Learning Under Sequential Pricing
In: RAND Journal of Economics, Forthcoming
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Working paper
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The Cost Coordination Theory of Harm and the EU Trucks Case
In: European Competition Journal
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Mergers: Coordinated Effects
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Collusive Benchmark Rates Fixing
In: Amsterdam Law School Research Paper No. 2017-34
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Working paper
Collusive Benchmark Rates Fixing
In: DIW Berlin Discussion Paper No. 1715
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Working paper
When to give the green light to green agreements
In: Oxera Agenda, September 2021
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