Introduction
The increasing use of artificial intelligence in pricing has transformed how firms interact with consumers. Advances in data collection and machine learning enable firms to estimate individual willingness to pay and to adjust prices at a highly granular level. These developments have renewed policy concerns about price discrimination, particularly regarding fairness, transparency, and potential consumer harm (OECD, 2022; Zhao, 2023; Calvano et al., 2021).
Individualized pricing is no longer a purely theoretical possibility and is already observed across a range of industries. In the airline sector, firms have long relied on sophisticated revenue management systems to adjust fares based on demand conditions and booking behavior. In e-commerce, platforms such as Amazon adjust prices dynamically and have been reported to vary pricing across users and over time. Ride-hailing services like Uber implement algorithmic pricing that adjusts fares in real time based on demand, location, and user characteristics. Similarly, digital advertising markets rely extensively on individualized pricing mechanisms, where advertisers bid differently for users based on detailed profiles. These developments have been widely documented in policy and industry analyses (OECD, 2018; Federal Trade Commission, 2021; The Economist, 2019). These examples illustrate that the ability to tailor prices to individual consumers is already embedded in many sectors, reinforcing the need to understand its implications for competition and regulation.
Much of the policy debate has focused on regulating the inputs to algorithmic pricing systems, including data usage, transparency, and explainability. While these dimensions are important, they do not directly address the economic forces that determine pricing outcomes. Prices emerge from strategic interaction among firms, and algorithms operate as tools within that interaction rather than as independent decision-makers.
This observation has important implications for regulatory design. If firms use algorithmic pricing strategically, regulation must be evaluated within a framework that accounts for equilibrium responses. Policies that ignore strategic interaction risk mischaracterize both the problem and the effects of intervention.
More generally, evaluating algorithmic pricing requires understanding how regulation changes the strategic incentives firms face when competing for consumers. Policies with similar stated objectives may affect pricing behavior through very different mechanisms. Some policies directly constrain pricing strategies, while others operate indirectly by altering enforcement incentives or the informational environment surrounding pricing decisions. These distinctions are important because firms respond strategically to regulation, and equilibrium outcomes depend on how policy interventions reshape the underlying pricing game.
This paper develops a game-theoretic framework to analyze the regulation of algorithmic pricing. The model embeds personalized pricing in a differentiated oligopoly and introduces a regulator who sets policy prior to firms’ pricing decisions. Firms then choose pricing strategies in response to both market conditions and regulatory constraints, and equilibrium outcomes arise from this interaction. In this paper, algorithmic pricing is modeled as the ability of firms to implement consumer-specific pricing functions at scale.
The contribution of this paper is not to derive new results on personalized pricing itself. The effects of individualized pricing under competition are well established in the industrial organization literature (Thisse & Vives, 1988; Corts, 1998; Stole, 2007; Rhodes & Zhou, 2024). Rather, the contribution lies in embedding these results within a regulatory framework and using them to compare how different policy instruments affect firm incentives, equilibrium pricing behavior, and welfare outcomes.
More specifically, the paper develops a mechanism-based framework in which regulatory tools operate through distinct transformations of the firm’s pricing problem. Constraint-based interventions, such as guardrails, directly limit feasible pricing strategies, whereas enforcement-based mechanisms alter incentives by altering expected regulatory costs. Transparency affects the informational environment that governs enforcement. By linking these mechanisms to equilibrium outcomes, the framework clarifies why policies with similar objectives may generate different effects on price dispersion, competition, and consumer welfare.
Existing research typically examines these margins separately. The industrial organization literature characterizes personalized pricing equilibria, the regulatory literature distinguishes among policy instruments, and the emerging AI policy literature emphasizes data, transparency, and accountability. This paper links these strands by asking how regulatory instruments change the mapping from consumer information to equilibrium prices, and how those changes affect competition, price dispersion, and welfare.
A central insight is that the effects of algorithmic pricing depend critically on market structure. In monopoly settings, improved information enables firms to extract more surplus (Varian, 1989). In oligopolistic markets, however, the same information may intensify competition by enabling firms to target marginal consumers more effectively. This distinction implies that regulatory interventions must be sensitive to the competitive environment in which they operate.
By modeling regulation as part of a strategic game, the paper provides a unified framework for evaluating policy tools based on how they alter firms’ pricing incentives and equilibrium outcomes. This approach shifts the focus of regulation away from controlling algorithms per se and toward understanding how policy shapes the competitive use of information in pricing. The framework also generates empirical implications regarding price dispersion, markup compression, enforcement effects, and competitive responses, offering a mechanism-based perspective for both ex ante policy analysis and retrospective regulatory evaluation.
This project was made possible through the support of Grant 63641 from the John Templeton Foundation. The opinions expressed in this publication are those of the author(s) and do not necessarily reflect the views of the John Templeton Foundation. For more information, visit The Next Frontier: Rethinking Regulation in an Era of Rapid Innovation.