Artificial Intelligent as a Market Participant – Implications for Antitrust Law
Artificial Intelligent as a Market Participant – Implications for Antitrust Law
(FAU Funds)
Titel des Gesamtprojektes:
Projektleitung:
Projektbeteiligte: , ,
Projektstart: January 15, 2023
Projektende: January 14, 2024
Akronym:
Mittelgeber:
URL:
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)