Pricing algorithms have shown the potential to both help and harm competition. Our new series, Computational Antitrust, explores how algorithms can abet tacit collusion, allowing competitors to set prices at anticompetitive levels without active human assistance. This series will introduce the legal and economic theory behind algorithmic collusion, explore its theoretical and empirical risks for competition, and discuss what regulators can and should do to protect competition and consumers from this rapidly advancing technology.
Companies increasingly use sophisticated computational tools to compete, particularly in digital markets. Giovanna Massarotto outlines how antitrust agencies must similarly modernize and adopt advanced technologies to address complex antitrust enforcement challenges effectively and remain relevant.
Return next Tuesday for the next addition to our Computational Antitrust series.
Antitrust originated in a time when technological advancements were upending markets. The expansion of railroads in the U.S. in the nineteenth century completely changed the way to do business, opening the door to competition at a larger scale. Similarly, the telegraph, radio, and telephone changed the way people interacted. In the 1890s, however, there was a real wave of popular hostility toward large corporations, such as Standard Oil, probably motivated by the fear of both uncertainty and lack of control in this new technological environment. The government was seen as a safe harbor, a central authority that could regulate these new high-traffic markets.
In this changed economic environment, antitrust law was conceived as a tool to prevent agreements in restraint of trade and forms of monopolization in markets. These new policies were based on Adam Smith’s theories of competition as well as common law concepts linked to contract law. Antitrust law represented a tool to secure economic freedom.
In any game there are winners and losers. Antitrust did not change this paradigm. Rather, the role of antitrust was and still is to ensure that anyone playing in markets acts fairly. Despite the fact that markets have changed, becoming increasingly dynamic and based on digital rather than physical platforms, antitrust’s mission has not and should not change. Antitrust’s focus is still to prevent forms of collusion and monopolization in markets regardless of whether they are digital or physical.
AI and blockchain: The new antitrust toolkit
In this new technological environment, antitrust agencies need to be equipped with the same computational tools that companies are increasingly using to pursue profits and market expansion. Otherwise, it is doubtful that antitrust authorities will be able to interpret market movements correctly and intervene effectively. Computational tools include increasingly sophisticated algorithms like artificial intelligence (AI) and technologies, such as blockchain, that stimulate decentralization and have the potential to increase the quality of data used to run AI systems.
AI algorithms exploit the large amount of data now available thanks to high-speed connectivity and powerful computers that can build models and make predictions with little human intervention, also known as machine-learning algorithms. Antitrust already makes predictions using multi-regression analysis as a learning method. Thus, the adoption of AI techniques is not something entirely new for the antitrust community and could help antitrust regulators respond to anticompetitive behavior more efficiently in today’s data economy.
On the other hand, blockchain is a distributed ledger used to record and store data according to a consensus algorithm. Blockchain was introduced as the infrastructure to record and store bitcoin transactions and is now used to record and store any data in a distributed ledger. The verification of data is performed by a network of computers, rather than a central authority. Network-based verification is why the adoption of these distributed systems potentially increases the quality of data necessary to run AI algorithms and the decentralization of data-driven markets. Blockchain creates the same data set view shared on a network of computers and run by a consensus mechanism to verify data. Blockchain can enable antitrust agencies to better detect anticompetitive practices on digital markets because any data can be permanently recorded and tracked in a blockchain.
Antitrust has the huge responsibility of being the first arm of government to regulate markets while Congress deliberates on new laws and specialized regulatory bodies. Therefore, how antitrust regulators respond to companies adopting new technologies will be, at least for some time, the most significant government effort to protect markets and consumers in this new paradigm.
In other words, Is it time for antitrust to change? Not necessarily. Despite the passage of time and the markets that have changed along with it, the logic that drives markets has not. Antitrust should not change its role. Antitrust needs to adopt the same technologies that markets are increasingly using, including AI and blockchain systems. This is what computational antitrust is all about and what it promises. Computational antitrust is about using computational tools. This does not seem to be an option. It is essential for antitrust to remain relevant.
Computational antitrust is not magic. For example, it does not ensure that companies will play fairly in today’s data-driven economy, but it is the only way to make increasingly sophisticated and dynamic markets comprehensible for antitrust enforcers. If not, the interpretation of market conditions becomes difficult if not impossible and antitrust intervention is made ineffective. AI techniques and blockchain need to become an integral part of the antitrust enforcement process, as they already are central to companies’ operations.
The time is certainly ripe for antitrust enforcers to start testing the adoption of AI and blockchain technologies in their enforcement action and equip themselves with the appropriate tools. Technology runs faster than government action, questioning the role of antitrust and government in general in shaping new technological environments. The issue is relevant and needs to be addressed.
As Judge Richard A. Posner recognized as far back as 2001, “[t]he real problem lies on the institutional side: the enforcement agencies and the courts do not have adequate technical resources, and do not move fast enough, to cope effectively with a very complex business sector that changes very rapidly.” This argument is vibrant today. As in the past, antitrust agencies need to be ahead of technological progress to be effective and maintain their gatekeeper role of competition in increasingly sophisticated markets. Not because of the fears of uncertainty and lack of control, but because in any game we want to ensure that everyone is playing fairly. Computational tools can assist antitrust in promoting competitive markets.
The main critique of antitrust presently concerns the fact that antitrust as it is enforced today would be ill-adapted to ensure competition in a digital economy. The question becomes: Will computational antitrust be the solution to the antitrust enforcement problem in digital markets?
The answer, in a nutshell, is that we do not know. But the government’s adoption of computational tools that increase market predictability, oversight, and data consistency can only benefit both markets and consumers.
Articles represent the opinions of their writers, not necessarily those of the University of Chicago, the Booth School of Business, or its faculty.
