Pricing algorithms and tacit collusion: what is the evidence?

Article Sidebar

HTML (Português (Brasil)) PDF (Português (Brasil))
Published: Dec 14, 2022
DOI: https://doi.org/10.52896/rdc.v10i2.1014
Keywords:
algorithm; pricing; cartel; competition; concentration algoritmo; fijación de precios; cartel; competencia; concentración algoritmo; precificação; cartel; concorrência; concentração

Main Article Content

Rutelly
Escola Nacional de Administração Pública
https://orcid.org/0000-0002-2807-8024
Fernando
Goias Federal University
https://orcid.org/0000-0001-9570-8113

Abstract

Introduction: This study aims to identify, from a quick review, if there is evidence that pricing algorithms lead to implicit collusion. This study aims to fill a gap in the Brazilian antitrust literature, which does not have a consolidated view of the state of the art on power of algorithms.


Materials and methods: This research adopts a rapid literature review on pricing algorithms. Eighteen articles dealing with theory, simulation, empirical, and qualitative studies were selected.


Results: From the review, it is concluded that (i) evidence of implicit collusion arising from pricing algorithms is still found at the theoretical level and in simulation models, and (ii) there is a lack of empirical evidence associated with cases of antitrust judged by competition authorities.


Discussion: Considering that it is an incipient topic with rich potential still uncertain, the studies included in the quick review point to the importance of antitrust authorities to prepare for the new competitive scenario formatted by disseminating pricing algorithms. In this article, we explore practical consequences for the Brazilian regulatory authority. The article contributes to policy formulation for the defense of competition based on evidence.

Downloads

Download data is not yet available.

Article Details

Issue
Vol. 10 No. 2 (2022): JULHO/DEZEMBRO 2022
Section
Revista de Defesa da Concorrência
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Os direitos autorais dos artigos publicados são do(s) autor(es), com direitos da Revista de Defesa da Concorrência (RDC) sobre a primeira publicação, impressa e/ou digital.

O(s) autor(es) somente poderá(ão) utilizar os mesmos resultados em outras publicações indicando claramente a Revista de Defesa da Concorrência como o meio da publicação original. Se não houver tal indicação, considerar-se-á situação de autoplágio.

A reprodução, total ou parcial, dos artigos aqui publicados fica sujeita à expressa menção da procedência de sua publicação neste periódico, citando-se o volume e o número dessa publicação, além do link DOI para referência cruzada. Para efeitos legais, deve ser consignada a fonte de publicação original.

Por se tratar de periódico de acesso aberto, permite-se o uso gratuito dos artigos em aplicações educacionais e científicas desde que citada a fonte, conforme a licença Creative Commons Attribution 4.0 International (CC BY 4.0).

 

Author Biographies

Rutelly, Escola Nacional de Administração Pública

Consultor Legislativo. Doutorando em Políticas Públicas pela Escola Nacional de Administração Pública (ENAP), Mestre em Economia pelo Centro de Desenvolvimento e Planejamento Regional (Cedeplar) da Universidade Federal de Minas Gerais (UFMG) e Bacharel em Ciências Econômicas pela UFMG. É professor colaborador dos cursos de MBA na área de regulação ofertados pela Fundação Getúlio Vargas de Brasília-DF e do MBA em Direito e Regulação do Setor Elétrico do Instituto Brasileiro de Ensino, Desenvolvimento e Pesquisa (IDP). Brasília - DF.

Fernando, Goias Federal University

Professor associado da Faculdade de Ciências Sociais da Universidade Federal de Goiás (UFG). Professor do Programa de Pós-Graduação em Ciência Política e Relações Internacionais da UFG. Professor do Programa Profissional de Doutorado em Políticas Públicas da Escola Nacional de Administração Pública (ENAP). Professor afiliado no Ostrom Workshop on Political Theory and Policy Analysis, Indiana University. Pesquisador do Instituto Nacional de Ciência e Tecnologia (INCT) – Democracia Digital, Universidade Federal da Bahia (UFBA). Bolsista de Produtividade do Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). Filgueiras é doutor em Ciência Política pelo Instituto Universitário de Pesquisas do Rio de Janeiro (Iuperj). Entre suas obras, Governance for the Digital World - Nem More State Nem More Market (Palgrave, 2021), com Virgilio Almeida. Goiânia-GO.

References

ALMEIDA, V.; FILGUEIRAS, F.; & MENDONÇA, R.F. Algorithms and institutions: How social sciences can contribute to governance of algorithms? IEEE Internet Computing, v. 26, n. 1, p. 42-46, 2022.

ASSAD, S.; CALVANO, E.; CALZOLARI, G.; CLARK, R.; DENICOLÒ, V.; ERSHOV, D.; JOHNSON, J.; PASTORELLO, S.; RHODES, A.; XU, L.; & WILDENBEEST, M. Autonomous algorithmic collusion: Economic research and policy implications. Oxford Review of Economic Policy, v. 37, n. 3, p. 459–478, 2021.

ASSAD, S.; CLARK, R.; ERSHOV, D.; & XU, L. Algorithmic Pricing and Competition: Empirical Evidence from the German Retail Gasoline Market. CESifo Working Paper nº 8521, 2020, disponível em: https://ssrn.com/abstract=3682021.

BERNHARDT, L.; & DEWENTER, R. Collusion by code or algorithmic collusion? When pricing algorithms take over. European Competition Journal, v. 16, n. 2–3, p. 312–342, 2020.

BROWN, Z.; & MACKAY, A. Competition in pricing algorithms, SSRN 3485024, mimeo, 2021.

CADE. Guia para análise de atos de concentração horizontal. Brasília: Conselho Administrativo de Defesa Econômica, 2016.

CALVANO, E.; CALZOLARI, G.; DENICOLÒ, V.; & PASTORELLO, S. Algorithmic collusion with imperfect monitoring. International Journal of Industrial Organization, v. 79, p. 1-15, 2021.

CALVANO, E.; CALZOLARI, G.; DENICOLÒ, V.; & PASTORELLO, S. Algorithmic Pricing What Implications for Competition Policy? Review of Industrial Organization, v. 55, n. 1, p. 155–171, 2019.

CALVANO, E.; CALZOLARI, G.; DENICOLÒ, V.; & PASTORELLO, S. Artificial intelligence, algorithmic pricing, and collusion. American Economic Review, v. 110, n. 10, p. 3267–3297, 2020.

CHEN, L.; MISLOVE, A.; & WILSON, C. An Empirical Analysis of Algorithmic Pricing on Amazon Marketplace. In: Proceedings of the 25th International World Wide Web Conference (WWW 2016), Montreal, Canada, 2016.

CMA. Pricing algorithms Economic working paper on the use of algorithms to facilitate collusion and personalised pricing. London: Competition & Markets Authority, 2018, disponível em Pricing algorithms (publishing.service.gov.uk)

CULPEPPER, P.; THELEN, K. Are we Amazon Primed? Consumers and politics of platform power. Comparative Political Studies, 53, n. 2, p. 288-318, 2021.

EZRACHI, A.; & STUCKE, M.E. Virtual Competition: The Promise and Perils of the Algorithm-Driven Economy. Cambridge: Harvard University Press, 2016.

FILGUEIRAS, F. Big data, artificial intelligence and the future of regulatory tools. In: M.P. Howlett (ed.). Routledge Handbook on Policy Tools. New York: Routledge, 2022.

FILGUEIRAS, F. New Pythias of public administration: Ambiguity and choice in AI systems as challenges for governance. AI & Society, early view, 2021.

GAUTIER, A.; ASHWIN; & VAN CLEYNENBREUGEL, P. AI algorithms, price discrimination and collusion: a technological, economic and legal perspective. European Journal of Law and Economics, v. 50, n. 3, p. 405–435, 2020.

GAVINE, A.; MACGILLIVRAY, S.; ROSS-DAVIE, M.; CAMPBELL, K.; WHITE, L.; & RENFREW, M. Maximising the availability and use of high-quality evidence for policymaking: collaborative, targeted and efficient evidence reviews. Palgrave Commun, v. 4, n. 5), 2018.

GRUPO DE TRABALHO EM ECONOMIA DIGITAL DAS AUTORIDADES DE CONCORRÊNCIA. BRICS in the digital economy: competition policy in practice, 2019, disponível em http://cdn.cade.gov.br/Portal/Not%C3%ADcias/2019/Cade%20lan%C3%A7a%20relat%C3%B3rio%20sobre%20economia%20digital%20em%20reuni%C3%A3o%20do%20BRICS__brics_report.pdf.

HANSEN, K. T.; MISRA, K.; PAI, M. M. Collusive Outcomes via Pricing Algorithms. Journal of European Competition Law and Practice, v. 12, n. 4, p. 334–337, 2021a.

HANSEN, K. T.; MISRA, K.; PAI, M. M. Algorithmic collusion: Supra-competitive prices via independent algorithms. Marketing Science, v. 40, n. 1, p. 1–12, 2021b.

HARRINGTON, J. E. Developing Competition Law for Collusion by Autonomous Artificial Agents. Journal of Competition Law and Economics, v. 14, n. 3, p. 331–63, 2018.

HUTCHINSON, C. S.; RUCHKINA, G. F.; PAVLIKOV, S. G. Tacit collusion on steroids: The potential risks for competition resulting from the use of algorithm technology by companies. Sustainability (Switzerland), v. 13, n. 2, p. 1–14, 2021.

IZQUIERDO S. S.; IZQUIERDO L. R. The “Win-Continue, Lose-Reverse” Rule in Cournot Oligopolies: Robustness of collusive outcomes. In AMBLARD, F.; MIGUEL,

F. J.; BLANCHET, A.; GAUDOU, B. (Eds.), Advances in artificial economics, p. 33–44. Berlin: Springer, 2015

KASTIUS, A.; SCHLOSSER, R. Dynamic pricing under competition using reinforcement learning. Journal of Revenue and Pricing Management, v. 21, n. 1, p. 50–63, 2022.

KELLEHER, J. Deep Learning. Cambridge: The MIT Press, 2019.

KLEIN, T. Autonomous algorithmic collusion: Q-learning under sequential pricing. RAND Journal of Economics, v. 52, n. 3, p. 538–558, 2021.

KNUTH, D. The Art of Computer Programming: Volume 1. Fundamental Algorithms. Addison-Wesley Professional: Boston, 3rd ed., 1997.

MEHRA, S. Antitrust and the Robo-Seller: Competition in the Time of Algorithms. Minnesota Law Review, v, 100, p. 1323–1375, 2016.

MOTTA, M. Competition policy: theory and practice. Cambridge University Press: New York, 616.p, 2004.

O’CONNOR, J.; WILSON, N. E. Reduced demand uncertainty and the sustainability of collusion: How AI could affect competition. Information Economics and Policy, v. 54, 2021.

OCDE. Algorithms and Collusion – Note from the European Union,

DAF/COMP/WD(2017)12, 14 June 2017, Disponível em https://one.oecd.org/document/DAF/COMP/WD(2017)12/en/pdf.

ONG, B. The Applicability of Art. 101 TFEU to Horizontal Algorithmic Pricing Practices: Two Conceptual Frontiers. IIC International Review of Intellectual Property and Competition Law, v. 52, n. 2, p. 189–211, 2021.

PEDRAM, M. Reliability of Regulating Artificial Intelligence to Restrain Cartelization: A Libertarian Approach. Asian Journal of Law and Economics, v. 12, n. 2, p. 149–169, 2021.

PETTICREW, M.; ROBERTS, H. Systematic reviews in the social sciences: a practical guide. Oxford: Blackwell Publishing, 2006, 336p.

RUSSELL, S. Human compatible. New York: Viking, 2019.

SANCHEZ-CARTAS, J. M.; KATSAMAKAS, E. Artificial Intelligence, Algorithmic Competition and Market Structures. IEEE Access, v. 10, p. 10575–10584, 2022.

SILVA, R. M. Estabilidade de Cartéis Tácitos e Ciclos Econômicos. Revista de Direito da Concorrência, n. 5, p. 25-47, 2005.

ŠMEJKAL, V. Cartels by robots – current antitrust law in search of an answer. InterEULawEast - Journal for International and European Law, Economics and Market Integrations, v. 4, n. 2, p. 1–18, dez. 2017.

ŠMEJKAL, V. Three challenges of artificial intelligence for antitrust policy and law. InterEULawEast, v. 8, n. 2, p. 97–118, 2021.

THÉPOT, J. Pricing algorithms in oligopoly with decreasing returns. Theory and Decision, v. 91, n. 4, p. 493–515, 2021.

VELJANOVSKI, C. What Do We Now Know about “Machine Collusion”. Journal of European Competition Law and Practice, v. 13, n. 1, p. 47–50, 2022.

WALTMAN, L.; KAYMAK, U. Q-learning agents in a Cournot oligopoly model. Journal of Economic Dynamics and Control, v. 32, n. 10, p. 3275–3293, 2008.

WECHE, J.; WECK, T. Tacit collusion and the boundaries of competition law: The parallel case of common ownership and algorithmic pricing. European Competition and Regulatory Law Review, v. 5, n. 1, p. 4–10, 2021.

YEUNG, K. Algorithmic Regulation: A Critical Interrogation. Regulation & Governance, v. 12, n. 4, 505–523, 2018.

ZHENG, G.; WU, H. Collusive algorithms as mere tools, super-tools or legal persons. Journal of Competition Law and Economics, v. 15, n. 2–3, p. 123–158, 2019.