Artificial Intelligent as a Market Participant – Implications for Antitrust Law

Artificial Intelligent as a Market Participant – Implications for Antitrust Law

(FAU Funds)

Overall project:
Project leader:
Project members: , ,
Start date: January 15, 2023
End date: January 14, 2024
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Abstract

Introduction: Antitrust laws (also known as competition laws) are designed to encourage strong competition and are designed to protect consumers from predatory commercial practices. The primary goals of antitrust law are to ensure the functioning of the markets and to ensure fair competition. A prominent example of an antitrust violation is illegal price fixing. By definition, it is an agreement between competitors that fixes prices or other competitive conditions, and thus violates the principle of the pricing mechanism through free market forces. A typical feature of illegal price fixing is verifiable communication (written or verbal) between human market participants. However, in the age of artificial intelligence and e-commerce, the definition and the detection of this illegal practice faces new challenges as collusive behaviors that violate antitrust laws, such as the pricing mechanism, can be partially or fully automated [1]. Furthermore, the communications between market participants can be both overt and covert. Finally, market participants can be artificial agents which might affected by perverse instantiation [2]. In other words, new technological possibilities are available to disguise illegal pricing policies and business practices.

Recent research, mainly from the economic and jurisprudence point of view, concludes the intensive application of AI algorithms in E-commerce will increase the extend of known forms of anticompetitive behaviors [3][4]. However, the questions regarding whether and to which extent collusive behaviors will emerge by AI itself (which is an unknown form of anticompetitive behaviors) are rarely understood. Feasibility studies and comprehensive analysis comprising the implementation of AI methods and validation of the derived hypothesis has not been conducted so far. Therefore, the main goals of this project are to investigate the possibilities of collusive behaviors stimulated and/or emerged by AI algorithms on digital marketplace and derive consequences on the antitrust law as well as competition policies. To the best of our knowledge, this is the first time that a research project in the field of Antitrust and AI (AAI) is focusing on the mathematical and algorithmic perspective of the question to which extend the utilization of AI methods is facilitating the collusive behaviors in the era of digital economy.

Objectives: In order to validate the hypotheses that AI algorithms is able to develop and communicate collusive behaviors on digital marketplaces both in overt and covert fashion, comprehensive emulators of online marketplaces in different setups will be implemented.  Furthermore, different communication channels (both overt and covert) of digital marketplaces will be discovered and understood, as it is highly relevant to the detection of collusive practices. Finally, different online trading scenarios utilizing AI algorithms will be established and the impact on antitrust law and competition polices will be derived. In total, the main aspects in the intended DFG-application can be summarized as follows:

1.       As the research topic belongs to a highly interdisciplinary field, a comprehensive literature review is necessary to define the problem space of the research and is of great importance to conduct the subsequent experiments successfully. Therefore, a comprehensive literature review on the aspects of antitrust law, game theory, artificial intelligence and cyber security will be conducted.

2.       First step of the implementation is the holistic emulation of the digital marketplace. The market emulator should have the capability to emulate the digital market following various rules (e.g., Cournot vs. collusive competition) in different size (i.e., with different amount of market participants). Moreover, state-of-the-art algorithms for dynamic pricing should be replicated and integrated into the market emulator as well.

3.       A further aspect of this project is the communication mechanism in the era of E-commerce and AI. The know form of collusions mostly utilize overt communications. However, covert communication channels (i.e., communication channels that are not originally designed for the communication purpose, therefore hardly to be detected [5][6]) poses further vulnerabilities of online marketplaces. The mechanisms and capacities of covert channels facilitating the collusive behaviors (e.g., illegal price fixing) as should be investigated with the implemented market emulator.

4.       Finally, artificial agents for price definition of different products should be proposed and implemented following different competition models as well as market complexities, aiming at understanding the central research questions of this research project, i.e., capabilities and conditions of emerging collusive behaviors of artificial agents by themselves. This particular step can be achieved by using reinforcement learning techniques. Technical opportunities and challenges for the discrimination of collusive and non-collusive behaviors that are potentially emerged by the artificial agents should be explored as well.

The entire project will be supervised by experts from three disciplines. Prof. Jochen Hoffmann (chair of Private Business Law) will support this research project with his knowledges and expertise on antitrust law, Prof. Felix Feilling (chair of Cyber Security) will advise on the aspects that are related to covert communication and cyber security, and Prof. Andreas Maier (pattern recognition lab) will mentor this project from the AI point of view.

[1] Künstliche Intelligenz als Marktteilnehmer – Technische Möglichkeiten, Maier A., Bayer S., Mohr Verlag. Submitted, unpublished.

[2] Bostrom N. Superintelligence: Paths, Dangers, Strategies. Minds & Machines 25, Seite 285–289 (2015).

[3] Petit, N. Antitrust and artificial intelligence: A research agenda. In: Journal of European Competition Law and Practice. Vol. 8, Issue 6, pp. 361–362. Oxford University Press. (2017)

[4] Beneke, F., Mackenrodt, M., Remedies for algorithmic tacit collusion, Journal of Antitrust Enforcement, Volume 9, Issue 1, Pages 152–176 (2021).

[5] Hans-Georg Eßer, Felix C. Freiling. Kapazitätsmessung eines verdeckten Zeitkanals über HTTP, Univ. Mannheim, Technischer Bericht TR-2005-10, November 2005. (2005)

[6] Freiling F.C., Schinzel S. Detecting Hidden Storage Side Channel Vulnerabilities in Networked Applications. In: Camenisch J., Fischer-Hübner S., Murayama Y., Portmann A., Rieder C. (eds) Future Challenges in Security and Privacy for Academia and Industry. SEC 2011. IFIP Advances in Information and Communication Technology, vol 354. Springer, Berlin, Heidelberg. (2011)

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