Artificial Intelligence and Collusion: A Literature Overview
The use of algorithms in pricing strategies has received special attention among competition law scholars. There is an increasing number of scholars who argue that the pricing algorithms, facilitated by increased access to Big Data, could move in the direction of collusive price setting. Though this claim is being made, there are various responses. On the one hand, scholars point out that current artificial intelligence is not yet well-developed to trigger that result. On the other hand, scholars argue that algorithms may have other pricing results rather than collusion. Despite the uncertainty that collusive price could be the result of the use of pricing algorithms, a plethora of scholars are developing views on how to deal with collusive price setting caused by algorithms. The most obvious choice is to work with the legal instruments currently available. Beyond this choice, scholars also suggest constructing a new rule of reason. This rule would allow us to judge whether an algorithm could be used or not. Other scholars focus on developing a test environment. Still other scholars seek solutions outside competition law and elaborate on how privacy regulation or transparency reducing regulation could counteract a collusive outcome. Besides looking at law, there are also scholars arguing that technology will allow us to respond to the excesses of pricing algorithms. It is the purpose of this chapter to give a detailed overview of this debate on algorithms, price setting and competition law.
KeywordsPrice fixing Tacit collusion Conscious parallelism Rule of reason Per se illegal Algorithmic collusion Monitoring algorithm Parallel algorithm Signaling algorithm Self-learning algorithms Reinforcement learning Q-learning Sandbox texting White-box testing Black-box testing
- Axelrod, R. (1988). The evolution of cooperation. In A. A. Gromyko & M. E. Hellman (Eds.), Breakthrough: emerging new thinking (pp. 185–192) (adapted from Axelrod, The Evolution of Cooperation. New York: Basic Books, 1984).Google Scholar
- Ballard, D. I., & Naik, A. S. (2017). Algorithms, artificial intelligence, and joint conduct. Competition Policy - International Antitrust Chronicle, 1(2), 29.Google Scholar
- Blockx, J. (2017). Antitrust in digital markets in the EU: Policing price bots. In Radbound Economic Law Conference. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2987705. Accessed July 1, 2018.
- Blockx, J. (2018). Policing price bots: algorithms and collusion. In ASCOLA Conference. http://www.law.nyu.edu/sites/default/files/upload_documents/Blockx.pdf. Accessed July 1, 2018.
- Capobianco, A., & Gonzaga, P. (2017). Algorithms and competition: Friends or foes. Competition Policy International - Antitrust Chronicle, 1(2), 1–6. https://www.competitionpolicyinternational.com/wp-content/uploads/2017/08/CPI-Capobianco-Gonzaga.pdf. Accessed July 1, 2018.
- Dabbah, M. M., & Hawk, B. E. (2009). Anti-Cartel enforcement worldwide (volumes I–III). Cambridge: Cambridge University Press.Google Scholar
- Deng, A. (2018a). An antitrust lawyer’s guide to machine learning. Antitrust, 32(2), 82–87.Google Scholar
- Deng, A. (2018b). What do we know about Algorithmic Tacit Collusion. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3171315. Accessed July 1, 2018.
- Dolmans, M. (2017). Artificial intelligence and the future of competition law—further thoughts (reaction to Prof. Ariel Ezrachi), GCLC Lunch Talk: Algorithms and Markets: Virtual or Virtuous Competition? https://www.coleurope.eu/sites/default/files/uploads/event/dolmans.pdf. Accessed July 1, 2018.
- Ezrachi, A., & Stucke, M. E. (2017a). Artificial intelligence & collusion: When computers inhibit competition. University of Illinois Law Review, 2017(5), 1776–1808.Google Scholar
- Ezrachi, A., & Stucke, M. E. (2017b) Algorithmic collusion: Problems and counter-measures. OECD Roundtable on Algorithms and Collusion (21–23 June 2017), DAF/COMP/WD (2017) https://www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=DAF/COMP/WD%282017%2925&docLanguage=En. Accessed July 1, 2018.
- Gal, M. S., & Elkin-Koren, N. (2017). Algorithmic consumers. Harvard Journal of Law & Technology, 30(2), 1–44.Google Scholar
- Gal, M. S. (2017). Algorithmic-facilitated coordination: Market and legal solutions. Competition Policy International—Antitrust Chronicle, 1(2), 22–28.Google Scholar
- Gal, M. S. (2018). Algorithms as illegal agreements. Berkeley Technology Law Journal (Forthcoming). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3171977. Accessed July 1, 2018.
- Geradin, D. (2017). Algorithmic tacit collusion and individualized pricing: Are antitrust concerns justified? In Copenhagen Economics Conference. https://www.copenhageneconomics.com/dyn/resources/Filelibrary/file/6/66/1498204706/geradin.pdf. Accessed July 1, 2018.
- Hovenkamp, H. (2005). Federal Antitrust Policy: The law of competition and its practices (3rd ed.). Saint-Paul: Thomson/West.Google Scholar
- Ioannidou, M. (2018). Digital agoraphobia: An enforcement perspective. In 2018 ASCOLA Conference. http://www.law.nyu.edu/sites/default/files/upload_documents/Ioannidou_0.pdf. Accessed July 1, 2018.
- Ittoo, A., & Petit, N. (2017). Algorithmic pricing agents and tacit collusion: A technological perspective. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3046405. Accessed July 1, 2018.
- Jacobs, M. J. (2016). Keynote address: Faith-based intellectual technology, faith-based antitrust? In Keynote Address at the 12th Annual Asian Competition Forum. https://asiancompetitionforum.com/2016-conference-materials. Accessed July 1, 2018.
- Janka, S. F., & Uhsler, S. B. (2018). Antitrust 4.0—The rise of artificial intelligence and emerging challenges to antitrust law. European Competition Law Review, 39(3), 112–123.Google Scholar
- Lemley, M. A. (2015). Faith-based intellectual property. UCLA Law Review, 62, 1328–1346.Google Scholar
- Marty, F. (2017). Algorithmes de Prix. Intelligence Artificielle et Equilibres Collusifs, Science Po OFCE Working Paper No. 14. https://www.ofce.sciences-po.fr/pdf/dtravail/WP2017-14.pdf. Accessed July 1, 2018.
- Mehra, S. K. (2014). De-humanizing antitrust: The rise of the machines and the regulation of competition. Temple University Legal Studies Research Paper No. 2014-43. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2490651. Accessed July 1, 2018.
- Mehra, S. K. (2015). Antitrust and the robo-seller: Competition in the time of algorithms. Temple University Legal Studies Research Paper Series No. 2015-15. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2576341. Accessed July 1, 2018.
- Mehra, S. K. (2016). Antitrust and the robo-seller: Competition in the time of algorithms. Minnesota Law Review, 100, 1323–1375.Google Scholar
- OECD. (2017). Algorithms and collusion—Background note by the secretariat. DAF/Comp(2017)4. https://one.oecd.org/document/DAF/COMP(2017)4/en/pdf. Accessed July 1, 2018.
- Orbach, B. (2016). Hub-and-spoke conspiracies. Arizona Legal Studies Discussion Paper No. 16-11. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2765476. Accessed July 1, 2018.
- Oxera Consulting LLP. (2017). When algorithms set prices: Winners and losers. Discussion Paper. https://www.regulation.org.uk/library/2017-Oxera-When_algorithms_set_prices-winners_and_losers.pdf. Accessed July 1, 2018.
- Petit, N. (2017a). Antitrust and artificial intelligence: State of play, gclc lunch talk: Algorithms and markets: Virtual or virtuous competition? https://www.coleurope.eu/sites/default/files/uploads/event/petit_0.pdf. Accessed July 1, 2018.
- Posner, R. (1976). Antitrust law: An economic perspective. Chicago: The University of Chicago Press.Google Scholar
- Posner, R. (2014). Review of Kaplow, competition policy and price fixing. Antitrust Law Journal, 79, 761–768.Google Scholar
- Salcedo, B. (2015). Pricing algorithms and tacit collusion. http://brunosalcedo.com/docs/collusion.pdf. Accessed July 1, 2018.
- Schwalbe, U. (2018). Algorithms, machine learning, and collusion. https://www.uni-hohenheim.de/qisserver/rds?state=medialoader&objectid=11011&application=lsf. Accessed July 1, 2018.
- Stucke, M. E., & Ezrachi, A. (2016). Is your digital assistant devious? University of Tennessee Legal Studies Research Paper No. 304. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2828117. Accessed July 1, 2018.
- Stucke, M. E., & Ezrachi, A. (2017a). Two artificial neural networks meet in an online hub and change the future (of competition, market dynamics and society). Legal Studies Research Paper Series No. 323. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2949434. Accessed July 1, 2018.
- Stucke, M. E., & Ezrachi, A. (2017b). Looking up in the data-driven economy. University of Tennessee Legal Studies Research Paper No. 333. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2975510. Accessed July 1, 2018.
- United Nations Conference on Trade and Development (UNCTAD). (2016). Q&A with Ariel Ezrachi, Professor of Competition Law, Oxford. http://unctad.org/en/pages/newsdetails.aspx?OriginalVersionID=1362. Accessed July 1, 2018.
- Vestager, M. (2017). Algorithms and competition. In Bundeskartellamt 18th Conference on Competition. https://ec.europa.eu/commission/commissioners/2014-2019/vestager/announcements/bundeskartellamt-18th-conference-competition-berlin-16-march-2017_en. Accessed July 1, 2018.
- Watkins, C. J. C. H, & Dayan, P. (1992). Q-learning. Machine Learning, 8, 279–292. http://www.gatsby.ucl.ac.uk/~dayan/papers/cjch.pdf. Accessed July 1, 2018.CrossRefGoogle Scholar
- Zingales, N. (2018). Antitrust in the age of algorithmic nudging. In 2018 ASCOLA Conference. http://www.law.nyu.edu/sites/default/files/upload_documents/Zingales.pdf. Accessed July 1, 2018.