Introduction
It started with flies. At its peak, The Making of a Fly, a biology textbook for fly researchers, was priced at almost $24 million on Amazon.
The book, originally printed in 1992, was out of print by 2011, but Amazon had listed seventeen copies for sale.
While the fifteen used copies started at $35, the two new copies—sold by two different sellers—started at well over $1 million.
Every day, the prices of both copies increased by set multiples; the sellers had pegged their prices to each other’s, causing a feedback loop, which only stopped once someone noticed the outlandish amount.
Eventually, the price peaked at $23,698,655.93. The next day, the price dropped to $106.23.
While this particular incident might be explained away by poor oversight on the part of the sellers, the use of algorithmic pricing on Amazon, as well as on other online platforms, has become increasingly common,
sometimes causing incredibly high—or low—prices.
In particular, the prevalence of algorithmic pricing has led to increased concerns about effective regulation of these algorithms to prevent price fixing between competitors, whether inadvertent or not.
Thus, this Note proposes a framework for the federal enforcement of antitrust prohibitions against price collusion between algorithms. As more companies adopt algorithms to determine prices, recent legal scholarship has noted that competitors may be able to collude with one another, resulting in price fixing, without triggering scrutiny under the federal antitrust statutes as they are currently being enforced.
And as these pricing algorithms often use fast-developing technologies like machine learning
or artificial intelligence,
companies may—even unwittingly—coordinate with other competitors to arrive at a supracompetitive price. Well-meaning companies may not intend to collude, but the “black box” nature of algorithms can often result in unintentional price coordination.
Price collusion claims can be litigated under either section 1 of the Sherman Act or section 5 of the FTC Act.
But most price-fixing cases are brought under the Sherman Act, as for years the FTC expressed reluctance to challenge practices on a standalone section 5 basis when the Sherman Act could sufficiently address the uncompetitive practice.
But while price collusion can be per se illegal under section 1 of the Sherman Act, the statutory case law requires evidence, whether direct or circumstantial, of an agreement between competitors to fix prices to prove a statutory violation.
On the other hand, section 5 of the FTC Act does not require an explicit showing of an agreement for antitrust claims.
Section 5 is thus more expansive than the Sherman Act, as it also bars unilateral conduct such as invitations to collude.
Consequently, it is easier to file suit under the FTC Act against individuals or companies that rely on more complex algorithms to fix prices without directly communicating with one another. But after a series of “adverse appellate rulings in the 1980s involving Commission attempts to expand Section 5 beyond the Sherman Act,” the FTC generally abandoned bringing standalone section 5 price-fixing claims in court, a policy that was formalized in 2015.
Several academics have noted the Sherman Act’s deficiencies when it comes to vigorously enforcing against anticompetitive algorithmic conduct.
Others have pointed out that a potential solution for algorithmic collusion may lie in the FTC Act instead.
But no attempt has yet been made to map out an outline for bringing a standalone section 5 claim in algorithmic price-fixing cases. Thus, this Note aims to provide an overview of how section 5 can be used effectively to regulate different types of algorithmic collusion.
Specifically, this Note argues that the FTC Act, given its broader powers, remains the best enforcement mechanism to challenge algorithmic price fixing, and prior cases that limited the scope of section 5 should be revisited in light of rapid technological developments. Part I describes the current statutory regime of federal antitrust law with a focus on the Sherman and FTC Acts. While price fixing is a per se violation of the Sherman Act, a collusion claim requires evidence of an agreement. In contrast, the FTC Act contains a broader prohibition on all unfair methods of competition and does not require an agreement among competitors.
Part II describes four broad categories of pricing algorithms and the challenges they pose to enforcement. While some algorithms are built with a clear intention to collude, others are more opaque. A conventional application of the antitrust framework is sufficient to regulate the simpler algorithms. But with the advent of artificial intelligence and other technologies, the more sophisticated algorithms are capable of evolving beyond what their programmers had initially built them for.
It is these algorithms that will present major challenges to antitrust enforcers.
Consequently, Part III suggests that the best way to tackle these algorithms is under the FTC Act: by treating collusive algorithms as announcements and therefore as invitations to collude. Research has indicated that algorithms can evolve to “broadcast” their pricing intentions to other chosen algorithms while also masking these communications from third parties.
In doing so, they function as announcements. And when a market satisfies certain structural characteristics that can facilitate collusion, courts should more closely examine any evidence of specific plus factors
that might be more prominent in an algorithmic setting. If these structural market characteristics are accompanied by specific plus factors, this should weigh in favor of finding evidence of an invitation to collude.