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Algorithmic Collusion, Genuine and Spurious
In: CEPR Discussion Paper No. DP16393
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Algorithmic Collusion with Imperfect Monitoring
In: CEPR Discussion Paper No. DP15738
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Working paper
Does an Intermediate Price Facilitate Algorithmic Collusion?
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Algorithmic Collusion: Insights from Deep Learning
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Algorithmic Collusion and Algorithmic Compliance: Risks and Opportunities
In: The Global Antitrust Institute Report on the Digital Economy 27
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Working paper
On Algorithmic Collusion and Reward-Punishment Schemes
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On algorithmic collusion and reward–punishment schemes
In: Economics letters, Band 237, S. 111661
ISSN: 0165-1765
Autonomous Algorithmic Collusion: Q-Learning Under Sequential Pricing
In: RAND Journal of Economics, Forthcoming
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Working paper
Algorithmic Collusion: Supra-Competitive Prices via Independent Algorithms
In: CEPR Discussion Paper No. DP14372
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Working paper
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.
The Fundamental Unimportance of Algorithmic Collusion for Antitrust Law
In: Harvard Journal of Law and Technology, 2020
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AI-Powered Trading, Algorithmic Collusion, and Price Efficiency
In: Jacobs Levy Equity Management Center for Quantitative Financial Research Paper
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Autonomous algorithmic collusion: economic research and policy implications
In: Oxford review of economic policy, Band 37, Heft 3, S. 459-478
ISSN: 1460-2121
Abstract
Markets are being populated with new generations of pricing algorithms, powered with artificial intelligence (AI), that have the ability to autonomously learn to operate. This ability can be both a source of efficiency and cause of concern for the risk that algorithms autonomously and tacitly learn to collude. In this paper we explore recent developments in the economic literature and discuss implications for policy.
Price Discrimination-Driven Algorithmic Collusion: Platforms for Durable Cartels
In: Stanford Journal of Law, Business, and Finance, Forthcoming
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