Algoritmik Anlaşmaların Tespiti: Moment Tarama Yönteminden Çıkarımlar

Öz

Küresel, otomatize ve dinamik üretim süreçlerinin gelişimi endüstriler üzerinde giderek daha önemli bir etkiye sahip olmaktadır. Sanal makineler genellikle sahne arkasında işlev görerek çeşitli operasyonları desteklemektedir. Algoritmalar bu sanal makinelerin temel zekâsıdır ve pazar yerlerinde verimliliği ve etkinliği büyük ölçüde artırmaktadır. Algoritmalar rekabeti teşvik etme ve sonunda pazar rekabetini artırma yeteneğine sahiptir. Ancak, genellikle otomatik fiyatlandırmayla ilişkilendirilen "dinamik fiyatlandırma" tekniklerini kullanarak algoritmik anlaşmalar sürdürülebilir. Algoritmik anlaşma ise, fiyatların artmasına ve/veya ürünlerin ve hizmetlerin kalitesinin azalmasına neden olur. Rekabet otoritelerinin ana hedefi ve işlevi, bu oluşumlarla mücadele etmektir. Bu bağlamda, kartel taraması, anlaşma/ittifak faaliyetlerini tespit etme yolunda önemli bir ilk adımdır. Bu çalışmada, algoritmik fiyatlandırmanın etkilerini yakalamak için moment tarama tekniği kullanılmaktadır. Bulgular, analizde incelenen belirli endüstri ve zaman çerçevesinde algoritmik fiyatlandırmanın anlaşma/ittifak dışı davranış sergilediğini öne sürmektedir.

Anahtar Kelimeler

• Algoritmik Anlaşma
• Davranışsal Tarama
• Kartel Tespiti
• Dinamik Fiyatlama
• Makine Öğrenmesi
• Online Pazar

Detecting Algorithmic Collusion: Insights from Moment Screening Methods

Abstract

The development of global, automated, and dynamic manufacturing processes is having a growing impact on industries. Virtual machines commonly function behind the scenes, supporting a variety of operations. Algorithms are the essential intelligence of these virtual machines, greatly increasing efficiency and effectiveness within marketplaces. Algorithms have the ability to promote competition and increase efficiency, eventually improving market competitiveness. However, algorithmic collusion can be maintained using “dynamic pricing” techniques, which are typically associated with automated pricing. Algorithmic collusion leads to increases in prices and/or decreases in the quality of products and services. The main objective and the function of competition authorities is to fight against those formations. In this regard, cartel screening is an important first step toward detecting collusive activity. In this paper, we used several moment screens to capture the effects of algorithmic pricing. Our findings suggest that algorithmic pricing exhibits non-collusive behavior within the particular industry and time frame examined in our analysis.

Keywords

• Algorithmic Collusion
• Behavioral Screens
• Cartel Detection
• Dynamic Pricing
• Machine Learning
• Online Marketplace

References

1. Abrantes-Metz, R. M. (2013). Proactive vs reactive anti-cartel policy: The role of empirical screens. Available at SSRN 2284740.
2. Abrantes-Metz, R. M., & Pereira, P. (2007). The impact of entry on prices and costs. SSRN-Working paper. [Online]. Available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1013619.
3. Abrantes-Metz, R. M., Froeb, L. M., Geweke, J. F., & Taylor, C. T. (2006). A variance screen for collusion. International Journal of Industrial Organization, 24, 467–486.
4. Abrantes-Metz, R. M., Kraten, M., Metz, A., & Seow, G. (2012). LIBOR manipulation? Journal of Banking and Finance., 36(1), 136–150.
5. Beth, H., & Gannon, O. (2022). Cartel screening–can competition authorities and corporations afford not to use big data to detect cartels?. Competition Law & Policy Debate, 7(2), 77-88.
6. Bolotova, Y., Connor, J. M., & Miller, D. (2008). The impact of collusion on price behavior: Empirical results from two recent cases. International Journal of Industrial Organization, 26(6), 1290–1307.
7. Byrne, D. P., & De Roos, N. (2019). Learning to coordinate: A study in retail gasoline. American Economic Review, 109(2), 591-619.
8. Calvano, E., Calzolari, G., Denicoló, V., & Pastorello, S. (2021). Algorithmic collusion with imperfect monitoring. International journal of industrial organization, 79, 102712.

9. Calzolari, L. (2021). The Misleading Consequences of Comparing Algorithmic and Tacit Collusion: Tackling Algorithmic Concerted Practices Under Art. 101 TFEU. European Papers-A Journal on Law and Integration, 2021(2), 1193-1228.
10. Descamps, A., Klein, T., & Shier, G. (2021). Algorithms and competition: the latest theory and evidence. Competition Law Journal, 20(1), 32-39.
11. Esposito, F., & Ferrero, M. (2006). Variance screens for detecting collusion: An application to two cartel cases in Italy. Italian Competition Authority, Working Paper.
12. Ezrachi, A., and Stucke, M. E. (2017). Artificial intelligence & collusion: When computers inhibit competition. U. Ill. L. Rev., 1775.
13. Green, E. J., Marshall, R. C., & Marx, L. M. (2014). Tacit collusion in oligopoly. The Oxford handbook of international antitrust economics, 2, 464-497.
14. Harrington Jr, J. E., & Imhof, D. (2022). Cartel screening and machine learning. Stan. Computational Antitrust, 2, 133.
15. Harrington, J. E. (2006). Behavioral screening and the detection of cartels. European competition law annual, 2006, 51-68.
16. Hovenkamp, H. (1988). The Sherman Act and the classical theory of competition. Iowa L. Rev., 74, 1019.
17. Huber, M., & Imhof, D. (2019). Machine learning with screens for detecting bid-rigging cartels. International Journal of Industrial Organization, 65, 277-301.
18. Huber, M., Imhof, D., & Ishii, R. (2022). Transnational machine learning with screens for flagging bid-rigging cartels. Journal of the Royal Statistical Society Series A: Statistics in Society, 185(3), 1074-1114.
19. Jiménez, J. L., & Perdiguero, J. (2012). Does rigidity of prices hide collusion?. Review of industrial organization, 41, 223-248.
20. Li, Y. (2017). Deep reinforcement learning: An overview. arXiv preprint arXiv:1701.07274.
21. Mårtensson, S. (2021). Catching Cartels: An evaluation of using structural breaks to detect cartels in retail markets. 2nd year Master Thesis in Economics, Department of Economics, Lund University.
22. Mehra, S. K. (2015). Antitrust and the robo-seller: Competition in the time of algorithms. Minn. L. Rev., 100, 1323.
23. Montero, D. (2023). Screening data as evidence in EU cartel investigations.
24. Muthusamy, K., McIntosh, C., Bolotova, Y., & Patterson, P. (2008). Price volatility of Idaho fresh potatoes: 1987–2007. American Journal of Potato Research, 85, 438–444.
25. OECD (2017). Algorithms and Collusion: Competition policy in the digital age. www.oecd.org/competition/algorithms-collusion-competition-policy-in-the-digital-age.htm.
26. OECD. (2023). The Future of Effective Leniency Programmes: Advancing Detection and Deterrence of Cartels, OECD Competition Policy Roundtable Background Note, www.oecd.org/daf/competition/the-future-of-effective-leniency-programmes-2023.pdf, last accessed on 29.04.2024.
27. Grout , Paul A and Silvia Sonderegger (2005). Predicting cartels (OFT 773). Office of Fair Trading discussion paper.
28. Samà, D. (2014). Cartel detection and collusion screening: an empirical analysis of the London Metal Exchange. Law & Economics LAB, LUISS “Guido Carli” University, Rome, Italy, 1-18.
29. Schrepel, T., & Groza, T. (2022). The adoption of computational antitrust by agencies: 2021 report. Stan. Computational Antitrust, 2, 78.
30. Silveira, D., Vasconcelos, S., Resende, M., & Cajueiro, D. O. (2022). Won’t get fooled again: A supervised machine learning approach for screening gasoline cartels. Energy Economics, 105, 105711.
31. Stigler, G. J. (1964). A theory of oligopoly. Journal of political Economy, 72(1), 44-61.
32. Stucke, M. E., & Ezrachi, A. (2016). How pricing bots could form cartels and make things more expensive. Harvard Business Review, 27.
33. Stucke, Maurice E. and Ariel Ezrachi (2017). Two Artificial Neural Networks Meet in an Online Hub and Change the Future (of Competition, Market Dynamics and Society). Research Paper #323.
34. The Competition and Markets Authority (CMA). (2021). Algorithms: How they can reduce competition and harm consumers. https://www.gov.uk/government/publications/algorithms-how-they-can-reduce-competition-and-harm-consumers, last accessed on 28.04.2024.
35. Wallimann, H., & Sticher, S. (2023). On suspicious tracks: machine-learning based approaches to detect cartels in railway-infrastructure procurement. Transport Policy, 143, 121-131.
36. Wallimann, H., & Sticher, S. (2024). How to Use Data Science in Economics--a Classroom Game Based on Cartel Detection. arXiv preprint arXiv:2401.14757.
37. Wallimann, H., Imhof, D., & Huber, M. (2023). A machine learning approach for flagging incomplete bid-rigging cartels. Computational Economics, 62(4), 1669-1720.
38. Weinstein, J. (2024). Algorithmic price-fixing cases draw federal interest. Trawel Weekly. https://www.travelweekly.com/Travel-News/Hotel-News/Algorithmic-price-fixing-cases-draw-federal-interest, last accessed on 28.04.2024.
39. Yalçın, Y. (2011). Davranışsal İktisat Yaklaşımıyla Rekabetçi Piyasa Analizi. Uzmanlık Tezi, Rekabet Kurumu Ankara.
40. Yalçın, Y., and Öztürk, S. (2024). The Use of Machine Learning Techniques and Distance Measures in Capturing Collusive Pricing: A Case Study for Algorithmic Pricing in E-Commerce Industry. PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-4019758/v1]