Skip to main content
SpringerLink
Log in

AI algorithms, price discrimination and collusion: a technological, economic and legal perspective

  • Published:
European Journal of Law and Economics Aims and scope Submit manuscript

Abstract

In recent years, important concerns have been raised about the increasing capabilities of pricing algorithms to make use of artificial intelligence (AI) technologies. Two issues have gained particular attention: algorithmic price discrimination (PD) and algorithmic tacit collusion (TC). Although the risks and opportunities of both practices have been explored extensively in the literature, neither has yet been observed in the actual practice. As a result, there remains much confusion as to the ability of algorithms to engage in potentially harmful behavior with respect to price discrimination and collusion. In this article, we embed the economic and legal literature on these topics in a technological grounding to provide a more objective account of the capabilities of current AI technologies to engage in price discrimination and collusion. We argue that attention to these current technological capabilities should more directly inform on-going discussions on the urgency to reform legal rules or enforcement practices governing algorithmic PD and TC.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Notes

  1. Our usage of the term “AI” focuses on machine learning algorithms, which is by far the predominant form of AI deployed in real-life applications. It also includes econometrics and optimization methods, which are closely related to machine learning and have the similar goal of learning from data or their environment.

  2. In the case of new products, consumers may not be aware of their willingness to pay. Personalized recommendations (based on recommender systems and AI methods) may help consumers realize their preferences. Marketing techniques, such as A/B testing, can also help in these situations.

  3. https://tinyurl.com/h4y6cqp.

  4. https://tinyurl.com/llu4t9d.

  5. https://tinyurl.com/yaryjq76.

  6. https://tinyurl.com/k8hkfgv.

  7. If instead of a 1 (yes)/0(no) response, consumers can also rank their preferences at a given bid price ti, then an ordered probit (tobit) can be used. This model can also be extended to rank product alternatives.

  8. A similar mechanism of threats and rewards is also necessary to implement an explicit coordinated outcome as cartels are fundamentally unstable.

  9. https://tinyurl.com/y8ry62b9 and https://tinyurl.com/y93ueqvf.

  10. https://tinyurl.com/yd2ygk84.

  11. Supracompetitive prices are not enough to attest the existence of TC. Brown and MacKay (2019) show that the use of algorithms for pricing modifies the nature of the pricing game. Through the possibility of reacting almost instantaneously to changes in the environment, the use of algorithms by some firms changes a simultaneous pricing game into a sequential one, leading to higher prices. There, however, is no collusion behind that.

  12. https://tinyurl.com/ov3ulp8.

  13. https://tinyurl.com/jtb8qsq.

  14. https://tinyurl.com/y9xsq5bh.

  15. https://tinyurl.com/6yrby4.

  16. These larger platforms are also known for biasing the recommendations they make towards their own products. The European Commission has considered this practice to be anticompetitive in relation to Google Shopping services, see European Commission, 27 June 2017, Case 39,740, Google Search (Shopping). See https://tinyurl.com/ycpyrqqb.

  17. https://tinyurl.com/ybaatqjn.

  18. https://tinyurl.com/y7akxk7w.

  19. https://tinyurl.com/ycq5bxse.

  20. Brown and MacKay (2019) have shown that the use of superior pricing algorithms generally leads to significant increases in markups, even in the absence of collusion.

  21. PD is often analyzed as a static one-shot game with firms having access to profiling data even though behavior-based PD can be formulated in two-stages; the first stage being the analysis of consumer behavior, and the second, the actual PD execution.

References

  • Adami, C., & Hintze, A. (2013). Evolutionary instability of zero-determinant strategies demonstrates that winning is not everything. Nature Communications, 4(2193), 1–8.

    Google Scholar 

  • Ban, G. Y., & Keskin, N. B. (2017). Personalized dynamic pricing with machine learning. SSN. https://doi.org/10.2139/ssrn.2972985. Retrieved January 21, 2019.

  • Belleflamme, P., Lam, W., & Vergote, W. (2019). Price discrimination and dispersion under asymmetric profiling of customers, CESifo Working paper no. 7964. https://ssrn.com/abstract=3498721.

  • Belleflamme, P., & Peitz, M. (2015). Industrial organization market and strategies (2nd ed.). Cambridge UK: Cambridge University Press.

    Book  Google Scholar 

  • Belleflamme, P., & Vergote, W. (2016). Monopoly price discrimination and privacy: The hidden cost of hiding. Economic Letters, 149, 141–144.

    Article  Google Scholar 

  • Bergemann, D., Brooks, B., & Morris, S. (2015). The limit of price discrimination. Amercian Economic Review, 105(3), 921–957.

    Article  Google Scholar 

  • Blockx, J. (2018). Antitrust in digital markets in the EU: Policing price bots. In J. M. Veenbrink, C. S. Rusu, & A. Looijestein-Clearie (Eds.), digital markets in the EU (pp. 75–89). Nijmegen: Wolf Publishers.

    Google Scholar 

  • Bounie, D., Dubus, A., & Waelbroeck, P. (2018). Selling strategic information in digital competitive markets, MimeoTelecom Paris Tech.

  • Brown, Z. Y., & MacKay, A. (2019) Competition in pricing algorithms. Harvard Business School Working Paper, No. 20-067, November 2019.

  • Cahn, A., Alfeld, S., Barford, P., & Muthukrishnan, S. (2016). An empirical study of web cookies. In: Proceedings of the 25th international conference on world wide web (pp. 891–901). International World Wide Web Conferences Steering Committee.

  • Calvano, E., Calzolari, G., Denicolo, V. & Pastorello, S. (2018a). Algorithmic pricing: What implications for competition policy? SSRN. https://doi.org/10.2139/ssrn.3209781. Retrieved January 21, 2019.

  • Calvano, E., Calzolari, G., Denicolo, V. & Pastorello, S. (2018b). Artificial Intelligence, algorithmic pricing and collusion. SSRN. https://doi.org/10.2139/ssrn.3304991. Retrieved January 21, 2019.

  • Cavallo, A. (2018). More Amazon effects: Online competition and pricing behaviors. National Bureau of Economic Research. https://www.nber.org/papers/w25138. Retrieved January 21, 2019.

  • Chen, X., Owen, Z., Pixton, C., & Simchi-Levi, D. (2015). A statistical learning approach to personalization in revenue management. SSRN. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2579462. Retrieved January 21, 2019.

  • Colombo, S. (2010). Product differentiation, price discrimination and collusion. Research in Economics, 64(1), 18–27.

    Article  Google Scholar 

  • Crandall, J. W., Oudah, M., Ishowo-Oloko, F., Abdallah, S., Bonnefon, J. F., et al. (2018). Cooperating with machines. Nature Communications, 9(233), 1–12.

    Google Scholar 

  • d’Aspremont C., & Gabszewicz J. J. (1986). On the stability of collusion. In: J. E. Stiglitz, G. F. Mathewson (Eds.), New developments in the analysis of market structure. International Economic Association Series (pp 243–264). London: Palgrave Macmillan.

  • DG Justice. (2018). EU Publications. https://publications.europa.eu/en/publication-detail/-/publication/ed9ce056-c2cf-11e8-9424-01aa75ed71a1/language-en/format-PDF. Retrieved January 21, 2019.

  • Dubé, J. P., & Misra, S. (2018). Scalable price targeting. Wharton (UPenn). https://marketing.wharton.upenn.edu/wp-content/uploads/2018/01/01-25-2018-Misra-Sanjog-PAPER-targeted_pricing.pdf. Retrieved January 21, 2019.

  • Ezrachi, A. & Stucke, M. (2015). Artificial intelligence & collusion: When computers inhibit competition. SSRN. https://doi.org/10.2139/ssrn.2591874. Retrieved January 21, 2019.

  • Ezrachi, A., & Stucke, M. (2016). Virtual competition: The promise and perils of the algorithm-driven economy. Cambridge: Harvard University Press.

    Book  Google Scholar 

  • Ezrachi, A. & Stucke, M. (2017). Algorithmic collusion: Problems and counter-measures. OECD. https://www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=DAF/COMP/WD%282017%2925&docLanguage=En. Retrieved March 25, 2019.

  • Fischer, C., & Normann, H. T. (2019). Collusion and bargaining in asymmetric Cournot duopoly—An experiment. European Economic Review, 111, 360–379.

    Article  Google Scholar 

  • Foerster, J., Chen, R. Y., Al-Shedivat, M., Whiteson, S., Abbeel, P., & Mordatch, I. (2018). Learning with opponent-learning awareness. In Proceedings of the 17th international conference on autonomous agents and multiagent systems (pp. 122–130). International Foundation for Autonomous Agents and Multiagent Systems.

  • Fonseca, M., & Normann, H. T. (2012). Explicit vs. tacit collusion—The impact of communication in oligopoly experiments. European Economic Review, 56(8), 1759–1772.

    Article  Google Scholar 

  • Garces-Tholon, E., Never, D., & Seabright, P. (2009). The ups and downs of the theory of collective dominance: Using game theory for merger policy. In B. Lyons (Ed.), Cases in European competition policy: The economic analysis. Cambridge: Cambridge University Press.

    Google Scholar 

  • Ghose, A., & Yang, S. (2009). An empirical analysis of search engine advertising: Sponsored search in electronic markets. Management Science, 55(10), 1605–1622.

    Article  Google Scholar 

  • Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.

  • Hannak, A., Soeller, G., Lazer, D., Mislove, A., & Wilson, C. (2014). Measuring price discrimination and steering on e-commerce web sites. In Proceedings of the 2014 conference on internet measurement conference (pp. 305–318). ACM.

  • Harrington, J. (2018). Developing competition law for collusion by autonomous artificial agents. Journal of Competition Law & Economics, 14(3), 331–363.

    Article  Google Scholar 

  • Helfrich, M., & Herweg, F. (2016). Fighting collusion by permitting price discrimination. Economic Letters, 145, 148–151.

    Article  Google Scholar 

  • Huck, S., Normann, H. T., & Oechssler, J. (2004). Two are few and four are many: Number effects in experimental oligopolies. Journal of Economic Behavior & Organization, 53(4), 435–446.

    Article  Google Scholar 

  • Ittoo, A., & Petit, N. (2017). Algorithmic pricing agents and tacit collusion: A technological perspective. SSRN. https://doi.org/10.2139/ssrn.3046405. Retrieved March 26, 2019.

  • Izquierdo S. S., & Izquierdo L. R. (2015). The “Win-Continue, Lose-Reverse” Rule in Cournot Oligopolies: Robustness of collusive outcomes. In F. Amblard, F. Miguel, A. Blanchet, B. Gaudou (Eds.), Advances in artificial economics (pp. 33–44). Berlin: Springer.

  • Klein, T. (2018). Autonomous algorithmic collusion: Q-learning under sequential pricing. SSRN. https://doi.org/10.2139/ssrn.3195812. Retrieved January 21, 2019.

  • Lee, S. I., Lee, H., Abbeel, P., & Ng, A. Y. (2006). Efficient L1 regularized logistic regression. AAAI. http://www.aaai.org/Papers/AAAI/2006/AAAI06-064.pdf. Retrieved January 21, 2019.

  • Leibo, J. Z., Zambaldi, V., Lanctot, M., Marecki, J., & Graepel, T. (2017). Multi-agent reinforcement learning in sequential social dilemmas. In Proceedings of the 16th conference on autonomous agents and multiagent systems (pp. 464–473). International Foundation for Autonomous Agents and Multiagent Systems.

  • Lerer, A., & Peysakhovich, A. (2017). Maintaining cooperation in complex social dilemmas using deep reinforcement learning. arXiv. https://arxiv.org/abs/1707.01068. Retrieved January 21, 2019.

  • Liu, Q., & Serfes, K. (2004). Quality of information and oligopolistic price discrimination. Journal of Economics and Management Strategy, 13(4), 671–702.

    Article  Google Scholar 

  • Liu, Q., & Serves, K. (2007). Market segmentation and collusive behavior. International Journal of Industrial Organization, 25, 355–378.

    Article  Google Scholar 

  • Lopez-Feldman. (2012). Introduction to contingent valuation using STATA. MPRA. https://mpra.ub.uni-muenchen.de/41018/2/MPRA_paper_41018.pdf. Retrieved January 1, 2020.

  • Lowe, R., Wu, Y., Tamar, A., Harb, J., Abbeel, O. P., & Mordatch, I. (2017). Multi-agent actor-critic for mixed cooperative-competitive environments. In Advances in neural information processing systems (pp. 6379–6390).

  • Competition and Markets Authority Report. (2018). Pricing algorithms. competition and markets authority (CMA). https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/746353/Algorithms_econ_report.pdf. Retrieved January 21, 2019.

  • Massarotto, G. (2019). From digital to blockchain markets: What role for antitrust and regulation. SSRN. https://doi.org/10.2139/ssrn.3323420. Retrieved March 27, 2019.

  • Miklós-Thal, J., & Tucker, C. (2019). Collusion by algorithm: Does better demand prediction facilitate coordination between sellers. Management Science. https://doi.org/10.1287/mnsc.2019.3287.

    Article  Google Scholar 

  • Montes, R., Sand-Zantman, W., & Valletti, T. (2018). The value of personal information in online markets with endogenous privacy. Management Science (in press).

  • Motta, M. (2004). Competition policy theory and practice. Cambridge: Cambridge University Press.

  • Ohlhausen, M. (2017). Should we fear the things that go beep in the night. FTC. https://www.ftc.gov/system/files/documents/public_statements/1220893/ohlhausen_-_concurrences_5-23-17.pdf. Retrieved 26 March 2019.

  • Peysakhovich, A., & Lerer, A. (2017). Prosocial learning agents solve generalized stag hunts better than selfish ones. In Proceedings of the 17th international conference on autonomous agents and multiagent systems (pp. 2043–2044). International Foundation for Autonomous Agents and Multiagent Systems.

  • Rana, R., & Oliveira, F. S. (2015). Dynamic pricing policies for interdependend perishable products or services using reinforcement learning. Expert Systems with Applications, 42, 426–436.

    Article  Google Scholar 

  • Reinartz, W., Haucap, J., Wiegand, N., & Hunold, M. (2017). Price differentiation and dispersion in retailing. IFH-Förderer. https://www.marketing.uni-koeln.de/sites/marketingarea/user_upload/Price_Differentiation_and_Dispersion_in_Retailing_Whitepaper_Einzelseiten_Version_1_ohne_letzte_Seite.pdf. Retrieved January 21, 2019.

  • Schwalbe, U. (2019). Algorithms, machine learning, and collusion. Journal of Competition Law & Economics, 14(4), 568–607.

    Article  Google Scholar 

  • Shiller, B. R. (2014). First-degree price discrimination using big data. Federal Trade Commission. https://www8.gsb.columbia.edu/faculty-research/sites/faculty-research/files/finance/Industrial/Ben%20Shiller%20–%20Nov%202014_0.pdf. Retrieved January 21, 2019.

  • Stole, L. (2003). Price discrimination and imperfect competition. Handbook of Industrial Organization, 3, 34–47.

    Google Scholar 

  • Suetens, S., & Potters, J. (2007). Bertrand colludes more than Cournot. Journal of Experimental Economics, 10, 71–77.

    Article  Google Scholar 

  • Tan, M. (1993). Multi-agent reinforcement learning: Independent versus cooperative agents. In Proceedings of the tenth international conference on international conference on machine learning (ICML’93).

  • Tesauro, G., & Kephart, J. O. (2002). Pricing in agent economies using multi-agent Q-learning. Autonomous Agents and Multi-Agent Systems, 5(3), 289–304.

    Article  Google Scholar 

  • Thisse, J., & Vives, X. (1988). On the strategic choice of spatial price policy. American Economic Review, 78(1), 122–137.

    Google Scholar 

  • Townley, C., Morrison, E., & Yeung, K. (2017). Big data and personalized price discrimination in EU competition law. Yearbook of European Law, 36, 683–748.

    Article  Google Scholar 

  • Van Cleynenbreugel, P. (2020). Article 101 TFEU’s association of undertakings notion and its surprising potential to help distinguish acceptable from unacceptable algorithmic collusion. Antitrust Bulletin. https://doi.org/10.1177/0003603X20929116.

    Article  Google Scholar 

  • Van Uytsel, S. (2018). Artificial intelligence and collusion: A literature overview. In M. Corrales, M. Fenwick, N. Forgo (Eds), Robotics, AI and the future of law (pp. 155–182). Singapore: Springer.

  • Varian, Hal R. (2009). Online ad auctions. American Economic Review, 99(2), 430–434.

    Article  Google Scholar 

  • Waltman, L., & Kaymak, U. (2008). Q-learning agents in a Cournot oligopoly model. Journal of Economic Dynamics and Control, 32(10), 3275–3293.

    Article  Google Scholar 

  • Woodcock, R. (2019). Personalized pricing as monopolization. Connecticut Law Review 51: Forthcoming. https://ssrn.com/abstract=2972369.

  • Zhou, N., Zhang, L., Li, S., & Wang, Z. (2018). Algorithmic collusion in cournot duopoly market: Evidence from experimental economics. arXiv. https://arxiv.org/abs/1802.08061. Retrieved January 21, 2019.

Download references

Author information

Authors and Affiliations

  1. HEC Liège, LCII, University of Liège, Liège, Belgium

    Axel Gautier & Ashwin Ittoo

  2. Faculty of Law, LCII, University of Liège, Liège, Belgium

    Pieter Van Cleynenbreugel

Authors
  1. Axel Gautier
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ashwin Ittoo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pieter Van Cleynenbreugel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ashwin Ittoo.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

We thank the handling editor and the reviewers for their helpful remarks and suggestions. This research was funded through the ARC grant for Concerted Research Actions, financed by the French-speaking Community of Belgium.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gautier, A., Ittoo, A. & Van Cleynenbreugel, P. AI algorithms, price discrimination and collusion: a technological, economic and legal perspective. Eur J Law Econ 50, 405–435 (2020). https://doi.org/10.1007/s10657-020-09662-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10657-020-09662-6

Keywords

JEL Classification

Search

Navigation