David S. Evans (Berkeley Research Group) has posted “Some Economic Aspects of Artificial Intelligence Technologies and Their Expected Social Value” (Forthcoming, CPI TechREG Chronicle, September 2023) on SSRN. Here is the abstract:

Artificial intelligence is a general-purpose technology which will result in disruptive innovation across the economy for many decades to come. AI deserves the superlatives that are often associated with it because it can create enormous social value. That is clear from just considering health care. Early evidence indicates that AI can dramatically improve the accuracy of diagnoses, such as for breast cancer. The deployment of AI as an internet-based technology could help billions of people who lack access to essential medical services. Artificial intelligence has come into its own at an important juncture in human history. Birth rates have fallen sharply for a long time, and below replacement rates, in many parts of the world including the EU, the US, and China. The populations of countries, such as Spain, Japan, and most recently China are declining. AI technologies, which can substitute for human brains, can alleviate the social cost of declining populations. Any discussion of the importance of AI comes with “buts” and the need for laws and regulations. There is no doubt about that. The design of public policy, however, must account for the impact of too little, misguided, or too much regulation on the long-run social value of artificial intelligence.

Aslihan Asil (Yale University) and Thomas Wollmann (University of Chicago) on “Can Machines Commit Crimes Under US Antitrust Laws?” on SSRN. Here is the abstract:

Generative artificial intelligence is being rapidly deployed for corporate tasks including pricing. Suppose one of these machines communicates with the pricing manager of a competing firm, proposes to collude, receives assent, and raises price. Is this a crime under US antitrust laws, and, if so, who is liable? Based on the observed behavior of the most widely adopted large language model, we argue that this conduct is imminent, would satisfy the requirements for agreement and intent under Section 1 of the Sherman Act, and confer criminal liability to both firms as well as the pricing manager of the competing firm.

Thibault Schrepel (VU Amsterdam; Stanford Codex; Sorbonne; Science Po) & Alex Pentland (MIT) have posted “Competition between AI Foundation Models: Dynamics and Policy Recommendations” (MIT Connection Science WP 1-2003) on SSRN. Here is the abstract:

Generative AI is set to become a critical technology for our modern economies. If we are currently experiencing a strong, dynamic competition between the underlying foundation models, legal institutions have an important role to play in ensuring that the spring of foundation models does not turn into a winter with an ecosystem frozen by a handful of players.

Michal Gal (U Haifa Law) and Daniel L. Rubinfeld (U Cal Berkeley Law; NBER; NYU Law School) have posted “Algorithms, AI and Mergers” (Antitrust Law Journal (2023)) on SSRN. Here is the abstract:

Algorithms, especially those based on artificial intelligence, play an increasingly important role in our economy. They are used by market participants to make pricing, output, quality, and inventory decisions; to predict market entry, expansion, and exit; and to predict regulatory moves. In a growing number of jurisdictions, algorithms are also used by regulators to detect and analyze anti-competitive conduct. This game-changing switch to (semi-)automated decision-making has the potential to reshape market dynamics. While the effect of algorithms on coordination between competitors has been a focus of attention, and scholarly work on their effects on unilateral conduct is beginning to accumulate, merger control issues have been undertreated. Accordingly, this article focuses on such issues.

The article identifies six main functions of algorithms that may affect market dynamics: collection and ordering of data; improving the ability to use existing data; reducing the need for data, for in-stance by generating synthetic data; monitoring; predicting, to deter-mine how different types of conduct, including mergers, are likely to affect market conditions; and decision-making.

The article demonstrates how such algorithms can exacerbate anti-competitive conduct with respect to both unilateral and coordinated effects. Towards this end, seven scenarios are explored: collusion, oligopolistic coordination, high unilateral prices, price discrimination, predation, selective pricing (in which a buyer offers a higher price to some suppliers in an aggressive bid for an input), and reducing the interoperability of datasets. For each scenario, we analyze how the market conditions necessary for such conduct are affected by algorithms.

These findings are then translated into merger policy. Algorithms are shown to affect substantive as well as institutional features of merger control. Algorithms also challenge some of the assumptions that are ingrained in merger control, suggesting that a more informed approach to some algorithmic-related mergers is appropriate.

Thibault Schrepel (VU Amsterdam; Stanford Codex; Sorbonne; Sciences Po) and Teodora Groza (Sciences Po) have posted “The Adoption of Computational Antitrust by Agencies: 2nd Annual Report” (3 Stanford Computational Antitrust 55 (2023)) on SSRN. Here is the abstract:

In the first quarter of 2023, the Stanford Computational Antitrust project team invited the partnering antitrust agencies to share their advances in implementing computational tools. Here are the 26 contributions we received.

Michal Gal (University of Haifa – Faculty of Law) and Daniel L. Rubinfeld (UC Berkeley Law; NBER; NYU Law) have posted “Algorithms, AI and Mergers” (Antitrust Law Journal (2023) on SSRN. Here is the abstract:

Algorithms, especially those based on artificial intelligence, play an increasingly important role in our economy. They are used by market participants to make pricing, output, quality, and inventory decisions; to predict market entry, expansion, and exit; and to predict regulatory moves. In a growing number of jurisdictions, algorithms are also used by regulators to detect and analyze anti-competitive conduct. This game-changing switch to (semi-)automated decision-making has the potential to reshape market dynamics. While the effect of algorithms on coordination between competitors has been a focus of attention, and scholarly work on their effects on unilateral conduct is beginning to accumulate, merger control issues have been undertreated. Accordingly, this article focuses on such issues.

The article identifies six main functions of algorithms that may affect market dynamics: collection and ordering of data; improving the ability to use existing data; reducing the need for data, for in-stance by generating synthetic data; monitoring; predicting, to deter-mine how different types of conduct, including mergers, are likely to affect market conditions; and decision-making.

The article demonstrates how such algorithms can exacerbate anti-competitive conduct with respect to both unilateral and coordinated effects. Towards this end, seven scenarios are explored: collusion, oligopolistic coordination, high unilateral prices, price discrimination, predation, selective pricing (in which a buyer offers a higher price to some suppliers in an aggressive bid for an input), and reducing the interoperability of datasets. For each scenario, we analyze how the market conditions necessary for such conduct are affected by algorithms.

These findings are then translated into merger policy. Algorithms are shown to affect substantive as well as institutional features of merger control. Algorithms also challenge some of the assumptions that are ingrained in merger control, suggesting that a more informed approach to some algorithmic-related mergers is appropriate.

Anne Witt (EDHEC Business School – Department of Legal Sciences) has posted “The Digital Markets Act – Regulating the Wild West” (Common Market Law Review, Forthcoming 2023) on SSRN. Here is the abstract:

This contribution critically assesses the European Union’s Digital Markets Act (DMA). The DMA is the first comprehensive legal regime to regulate digital gatekeepers in the aim of making platforms markets fairer and more contestable. To this end, the DMA establishes 22 per se conduct rules for designated platforms. It also precludes national gatekeeper regulation by EU Member States, thereby calling into question the legality of the pioneering German sec. 19a GWB. The analysis shows that the DMA’s rules are not as rigid as they may appear at first sight. While it is more accepting of false positives than of false negatives, the DMA contains several corrective mechanisms that could allow the Commission to finetune the rules to address both the danger of over- and under-inclusiveness. A further positive is that the new regulation incorporates key concepts of the GDPR, and requires coordination between the Commission and key EU data protection bodies. On the downside, the DMA does not contain any substantive principles for the assessment of gatekeeper acquisitions, leaving a worrying gap. While the DMA’s conduct rules outlaw specific leveraging strategies in digital ecosystems and may thereby indirectly address certain non-horizontal concerns arising from gatekeeper acquisitions, it remains that the European Union’s existing guidance on merger control is seriously out of date. The merger guidelines therefore urgently need updating to include (workable) theories of harm for concentrations in the digital economy.

Oren Bar-Gill (Harvard Law), Cass R. Sunstein (Harvard Law; Harvard Kennedy School), and Inbal Talgam-Cohen (Technion-Israel Institute of Technology) have posted “Algorithmic Harm in Consumer Markets” on SSRN. Here is the abstract:

Machine learning algorithms are increasingly able to predict what goods and services particular people will buy, and at what price. It is possible to imagine a situation in which relatively uniform, or coarsely set, prices and product characteristics are replaced by far more in the way of individualization. Companies might, for example, offer people shirts and shoes that are particularly suited to their situations, that fit with their particular tastes, and that have prices that fit their personal valuations. In many cases, the use of algorithms promises to increase efficiency and to promote social welfare; it might also promote fair distribution. But when consumers suffer from an absence of information or from behavioral biases, algorithms can cause serious harm. Companies might, for example, exploit such biases in order to lead people to purchase products that have little or no value for them or to pay too much for products that do have value for them. Algorithmic harm, understood as the exploitation of an absence of information or of behavioral biases, can disproportionately affect members of identifiable groups, including women and people of color. Since algorithms exacerbate the harm caused to imperfectly informed and imperfectly rational consumers, their increasing use provides fresh support for existing efforts to reduce information and rationality deficits, especially through optimally designed disclosure mandates. In addition, there is a more particular need for algorithm-centered policy responses. Specifically, algorithmic transparency—transparency about the nature, uses, and consequences of algorithms—is both crucial and challenging; novel methods designed to open the algorithmic “black box” and “interpret” the algorithm’s decision-making process should play a key role. In appropriate cases, regulators should also police the design and implementation of algorithms, with a particular emphasis on exploitation of an absence of information or of behavioral biases.

Marco Almada (EUI Law) and Nicolas Petit (same) have posted “The EU AI Act: Between Product Safety and Fundamental Rights” on SSRN. Here is the abstract:

The European Union (“EU”) Artificial Intelligence Act (the AI Act) is a legal medley. Under the banner of risk-based regulation, the AI Act combines two repertoires of European Union (EU) law, namely product safety and fundamental rights protection. Like a medley, the AI Act attempts to combine the best features of both repertoires. But like a medley, the AI Act risks delivering insufficient levels of both product safety or fundamental rights protection. This article describes these issues by reference to three classical issues of law and technology. Some adjustments to the text and spirit of the AI Act are suggested.

Nicolo Zingales (Getulio Vargas Foundation (FGV); Tilburg Law and Economics Center (TILEC); Stanford University – Stanford Law School Center for Internet and Society) and Bruno Renzetti (Yale University, Law School; University of Sao Paulo (USP), Faculty of Law (FD)) have posted “Digital Platform Ecosystems and Conglomerate Mergers: A Review of the Brazilian Experience” (World Competition 45 (4) (2022)) on SSRN. Here is the abstract:

This paper highlights some of the key challenges for the Brazilian merger control regime in dealing with mergers involving digital platform ecosystems (DPEs). After a quick introduction to DPEs, we illustrate how conglomerate effects that are raised by such mergers remain largely unaddressed in the current landscape for merger control in Brazil. The paper is divided in four sections. First, we introduce the reader to the framework for merger control in Brazil. Second, we identify the possible theories of harm related to conglomerate mergers, and elaborate on the way in which their application may be affected by the context of DPEs. Third, we conduct a review of previous mergers involving DPEs in Brazil, aiming to identify the theories of harm employed (and those that could have been explored) in each case. Fourth and finally, we summarize and results and suggest adaptations to the current regime, advancing proposals for a more consistent and predictable analysis.