ABSTRACT The development of artificial intelligence and the growing use of algorithms to optimize prices have generated significant debate about their benefits and potential adverse effects on competition and consumers. Two key issues dominate this discussion: algorithmic price discrimination through personalized pricing and algorithmic tacit collusion. Although the risks associated with algorithmic tacit collusion have been extensively studied, the potential harms from algorithmic price discrimination remain underexplored. This article examines algorithmic price discrimination from an EU perspective and whether the current EU competition law framework is adequate to tackle algorithmic price discrimination that harms consumers. It argues for robust competition law enforcement under Article 102(a) Treaty on the Functioning of the European Union to ensure that algorithmic pricing does not become a tool for exploitative abuse in the digital economy.

This study investigates whether agents powered by a large language model (LLM)-based service can exhibit algorithmic collusion in construction bidding environments. Using a simulated setting where ChatGPT (GPT-4o) agents repeatedly compete in price-based bidding, we assess their ability to form stable pricing strategies through interaction alone. Experimental results show that these agents frequently adopt reward–punishment mechanisms, adjusting bids in response to competitors’ behavior, and often converge to supracompetitive prices. Such outcomes emerge without explicit coordination or collusive intent, reflecting a form of tacit algorithmic collusion. The findings demonstrate that even with limited memory or learning across rounds, LLM-powered agents can develop strategic behaviors that affect market dynamics. We further show that subtle changes in prompt design influence bidding behavior and that market transparency may facilitate coordination among agents. These results highlight the importance of examining how specific artificial intelligence services behave in market-like environments and the need for regulatory attention to the informational conditions under which such systems operate, particularly in procurement contexts such as construction bidding.

Type of the article: Research ArticleAbstractIn business-to-business contract markets, oligopolistic suppliers often exhibit stable pricing patterns and low customer switching rates that cannot be explained by explicit coordination. This paper investigates how market characteristics enable tacit collusion through customer-specific mutual forbearance, where competitors implicitly treat individual customers as separate market spheres, each served through individualized contracts.We use a controlled laboratory experiment based on a dynamic Bertrand oligopoly with three suppliers and ten distinct customers across 17 trading periods. The design systematically varies two conditions: whether suppliers receive ex-post information on competitors’ transaction prices and customer relationships, and whether they can set individualized prices per customer. Customer decisions are simulated algorithmically to focus on supplier strategies and reduce variance. The experiment thus isolates the combined effect of price differentiation and information transparency on pricing under switching costs.Our results demonstrate that when ex-post information and price differentiation coincide, suppliers develop implicit “customer ownership” understandings without explicit communication. We show that this combination significantly reduces customer-supplier switching and leads to higher offer and transaction prices compared to markets where either factor is absent. Additionally, suppliers engage in less aggressive poaching behavior when both information transparency and pricing flexibility are present.These results confirm that information transparency enables suppliers to monitor customer boundaries and credibly threaten retaliation, while customer-specific pricing provides the mechanism for targeted punishment of boundary violations. Further, the findings provide managers in B2B sectors with actionable tools for strategic coordination.

The exponential growth of artificial intelligence (AI) and machine learning algorithms has fundamentally altered the functioning of modern markets, particularly within digital and e-commerce ecosystems. While these technologies enhance operational efficiency, market transparency, and consumer access, they also introduce unprecedented challenges for competition law enforcement. This paper examines the impact of artificial intelligence and machine algorithms on the competition law framework in India, with specific emphasis on algorithmic price-fixing, discriminatory pricing, personalized advertising, and autonomous or tacit collusion. The study adopts a doctrinal and analytical research methodology, analysing statutory provisions of the Competition Act, 2002, relevant case laws, and comparative international developments to evaluate the adequacy of India’s existing competition regime in addressing AI-driven anti-competitive conduct. It explores how self-learning algorithms, operating without explicit human coordination, may distort market outcomes by facilitating parallel pricing behaviour, reducing consumer choice, and exploiting asymmetries in data and information. A balanced approach is essential to ensure that technological innovation promotes consumer welfare without undermining the principles of free and fair competition in India’s evolving digital economy.

Abstract I study self-learning pricing algorithms and show that they are collusive in market simulations. To derive a counterfactual that resembles traditional tacit collusion, I conduct market experiments with humans in the same environment. Across different treatments, I vary the market size and the number of firms that use a pricing algorithm. I demonstrate that oligopoly markets can become more collusive if algorithms make pricing decisions instead of humans. In two-firm markets, prices are weakly increasing in the number of algorithms in the market. In three-firm markets, algorithms weaken competition if most firms use an algorithm and human sellers are inexperienced.

Over-the-Air updates have emerged as a critical competitive frontier in the Software-Defined Vehicle era. While offering value creation opportunities, automakers face strategic uncertainty regarding pricing models (e.g., subscription vs. one-time purchase). To clarify these dynamics, this study develops an evolutionary game model of duopolistic pricing competition. Unlike traditional studies with exogenous payoff assumptions, we innovatively employ the Hotelling model to endogenously derive firm profit functions based on consumer utility maximization. The highlights of this study include: (1) We establish an integrated “static–dynamic” framework connecting micro-level consumer choice with macro-level strategy evolution; (2) We identify that product differentiation is the decisive variable governing market stability; (3) We demonstrate that under moderate differentiation, the market exhibits a robust self-correcting tendency towards “Tacit Collusion” (mutual high pricing). However, simulation results also warn that an asymmetric disruptive strategy by a market leader can override this robustness, forcing the market into a low-profit equilibrium. These findings provide theoretical guidance for automakers to optimize pricing strategies and avoid value-destroying price wars.

Cartel development tracks the evolution of coordination technology: from covert gatherings to computer-programmed prices and now to blockchain conspiracy. Blockchain offers an unprecedented anticompetitive conduct architecture, unchangeable, pseudonymous, and self-executing, that turns the very original assumptions of antitrust policy such as intent, trackability, and culpability on their head. Unlike traditional concerns of tacit collusion, which relies on mutual awareness without an explicit agreement, blockchain-based systems can automate explicit coordination without human contact, blurring the conceptual boundary between the two and challenging doctrinal tests. Traditional enforcement mechanisms, rooted in the identification of human interaction or “meetings of minds,” are baffled by decentralized coordination accomplished solely via code. This piece examines the new challenges of blockchain-based algorithmic cartels and outlines how transparency, immutability, and smart contracts can render collusion from a frail pact to an enduring, self-perpetuating system. It also examines how regulators across various jurisdictions like the EU, US, and India are grappling to address these challenges and proposes a forward-looking, hybrid model of enforcement. Leading recommendations are regulatory adaptation through algorithmic examination and levels of openness, technological policing by AI-backed blockchain analysis and smart contracts of compliance, and international coordination for transnational ledger observation. India’s assertive model, as seen in the Standing Committee on Finance and the Draft Digital Competition Bill, earns that label because it adopts an ex ante regulatory architecture through SSDE-style designation and proactive intervention, a departure from purely reactive enforcement. Lastly, the disruptive potential of blockchain necessitates competition law to abandon enforcement by response, implanting oversight into the technological underpinning itself in order to safeguard innovation without permitting conspiracy.

ABSTRACT This paper explains the price drops of legacy firms in the U.S. airline industry during COVID-19 from the perspective of tacit collusion. By employing three independent econometric approaches, we find three lines of evidence supporting the declining of tacit collusion among them: 1) the pandemic had a greater negative impact on the average ticket prices of routes that are more likely to be exposed to tacit collusion; 2) the pandemic widened firm-pair price gaps; and 3) the pandemic weakened the positive impact of multimarket contact on average ticket prices. These phenomena occurred only on routes where Southwest Airlines, known for its anti-collusion effect, was not present. Our findings contribute to a deeper understanding of how legacy firms in the U.S. airline industry respond to demand-side shocks by pricing strategies.

An oligopoly is a market in which the price of goods is controlled by a few firms. Cournot introduced the simplest game-theoretic model of oligopoly, where profit-maximizing behavior of each firm results in market failure. Furthermore, when the Cournot oligopoly game is infinitely repeated, firms can tacitly collude to monopolize the market. Such tacit collusion is realized by the same mechanism as direct reciprocity in the repeated prisoner’s dilemma game, where mutual cooperation can be realized whereas defection is favorable for both prisoners in a one-shot game. Recently, in the repeated prisoner’s dilemma game, a class of strategies called zero-determinant strategies attracts much attention in the context of direct reciprocity. Zero-determinant strategies are autocratic strategies which unilaterally control payoffs of players by enforcing linear relationships between payoffs. There were many attempts to find zero-determinant strategies in other games and to extend them so as to apply them to broader situations. In this paper, first, we show that zero-determinant strategies exist even in the repeated Cournot oligopoly game, and that they are quite different from those in the repeated prisoner’s dilemma game. Especially, we prove that a fair zero-determinant strategy exists, which is guaranteed to obtain the average payoff of the opponents. Second, we numerically show that the fair zero-determinant strategy can be used to promote collusion when it is used against an adaptively learning player, whereas it cannot promote collusion when it is used against two adaptively learning players. Our findings elucidate some negative impact of zero-determinant strategies in the oligopoly market.

Can dynamic pricing algorithm facilitate tacit collusion? An experimental study using deep reinforcement learning in airline revenue management

In the era of the digital economy, data monopoly has emerged as a central factor reshaping the landscape of market competition. Leveraging exclusive access to data resources and algorithmic control, oligopolistic platforms systematically consolidate their dominant market positions through coordinated self-preferencing conduct, undermining fair competition and threatening consumer welfare. By analyzing the characteristics of data exclusivity, algorithmic coordination, cross-platform ecosystem foreclosure, and the quasi-public nature of platform power, this study reveals the dual harms to competition posed by data monopolies. Existing antitrust frameworks face significant institutional challenges, including outdated criteria for identifying market dominance, difficulties in proving tacit collusion via algorithms, and the lack of mechanisms for evaluating cross-market effects. To address these issues, this paper proposes a three-dimensional regulatory path encompassing technology, institutional reform, and law enforcement. Key recommendations include establishing a dedicated chapter on the abuse of digital platforms, reforming the criteria for assessing market dominance, optimizing the burden of proof allocation, and localizing the EU’s "digital gatekeeper" regime. These measures aim to strike a balance between innovation incentives and competitive fairness, offering a tailored regulatory framework for antitrust governance in China's digital economy.

Abstract This article contributes to the debate about the potential of pricing algorithms to collude and earn supra‐competitive profits without explicit communication. By simulating competition among seven algorithms, we demonstrate that: (1) algorithms can reach supra‐competitive prices in a reasonably short time, taking less than the time taken by algorithms in recent studies; and (2) tacit collusion among the algorithms is robust to the choice of different algorithms by competing firms. These results address the main criticisms concerning the practical relevance of recent studies that demonstrate algorithmic collusion. The top‐performing algorithms possess properties of niceness, forgiveness, provocability, and flexibility.

The rise of digital platforms has profoundly transformed modern markets, particularly through the deployment of algorithmic pricing strategies powered by big data. As firms increasingly rely on sophisticated algorithms to set prices dynamically, questions arise about the implications for market efficiency and consumer welfare. This paper explores how algorithmic pricing, when implemented on data-rich digital platforms, affects competitive behavior, price transparency, and consumer outcomes. While algorithmic systems can theoretically enhance efficiency by matching prices more closely to real-time demand and supply conditions, they may also facilitate tacit collusion, reduce price dispersion, and undermine traditional competitive dynamics. The power of big data enables platforms to segment consumers, personalize prices, and predict purchasing behavior with unprecedented accuracy, raising concerns about fairness, privacy, and market manipulation. Additionally, the opacity of algorithmic processes poses regulatory challenges in ensuring that pricing strategies align with pro-competitive principles and consumer protection goals. This study contributes to the growing discourse on the economic consequences of digitalization by examining how algorithmic pricing impacts allocative efficiency, price stability, and surplus distribution. Ultimately, the paper underscores the dual potential of these technologies to foster innovation and efficiency while also risking distortions that may harm consumer welfare and weaken competition in increasingly data-driven markets.

Abstract The Fourth Industrial Revolution has introduced transformative technologies such as artificial intelligence (AI) and algorithms, fundamentally reshaping global competition and collusion dynamics in digital markets. This article explores how algorithms facilitate collusion through four scenarios – Messenger, Hub-and-Spoke, Predictable Agent, and Digital Eye – and examines the ensuing legal challenges: indirect information exchange, tacit collusion, and liability attribution. By conducting a detailed comparative analysis of the European Union (EU) and Vietnamese competition law frameworks, the study considers their capacity to address these issues and proposes legal reforms. Although the EU’s sophisticated legal system effectively tackles certain aspects of algorithmic collusion, it still struggles with tacit collusion and liability in autonomous algorithmic scenarios, needing innovative regulatory approaches. In contrast, Vietnam’s competition law lacks adequate provisions, leaving it ill-prepared for digital market challenges. This research contributes policy frameworks adapted to Vietnam’s regulatory context and proposes implementable solutions that balance technological innovation with competitive market integrity.

Tacit collusion among dominant banks: Evidence from round-yard loan pricing

With the rise of artificial intelligence in commercial markets, companies have increasingly adopted algorithm tools for price optimization, including personalized pricing. While such practices may yield efficiency gains and consumer benefits, they also raise significant concerns under competition law, especially in the context of abuse of dominance. In digital markets, concentrated Big Data and analytical algorithms enable undertakings to predict each consumer’s willingness to pay with increasing accuracy and offer consumers personalized recommendations and tailored prices accordingly. In this context, concerns have arisen about whether and when AI-enabled price discrimination amounts to an abuse of dominance under competition law and would require a legal response. To address these concerns, this paper will analyze AI-enabled price discrimination from a comparative law and economics perspective. In economics, price discrimination is not always undesirable as it can increase static efficiency, and, on some occasions, it can promote dynamic efficiency and boost consumer welfare. Nevertheless, it may also lead to exclusionary and exploitative effects, especially once Tech Giants abuse their dominant positions in relevant markets. The development of Artificial Intelligence and the growing use of algorithms to optimize prices have generated significant debate about their benefits and potential adverse effects on competition and consumers. Two key issues dominate this discussion: algorithmic price discrimination through personalized pricing and algorithmic tacit collusion. While the risks and opportunities of algorithmic tacit collusion have been extensively studied, the potential harm from algorithmic price discrimination remains underexplored. This article examines whether the current competition law framework is adequate to tackle algorithmic price discrimination that harms consumers.

Abstract Oligopolistic price-setting has been at the core of antitrust enforcement ever since its existence. Consensus about exempting pure conscious parallelism (tacit collusion) from competition law scrutiny had quickly been reached, which means that non-competitive market conduct by several undertakings is only scrutinized if it was established by certain preceding collusive behavior. The necessary constituents of such illicit collusion, however, are subject to an ongoing debate. This holds particularly true for cases of unilateral collusion where non-competitive market conduct was established based on the individual use of a facilitating practice by (sometimes) only one firm. Competitor-based pricing guarantees by which a company promises to match a competitor’s lower price can serve as an illustration. Economic research shows that such guarantees can impede competition: a firm can deter its rivals from undercutting its price because the resulting quantity effect is lower than usual. At first glance, with “concerted practices'' in Art. 101 TFEU, European competition law provides a suitable tool to tackle such behavior. Under traditional doctrine, however, the concept is not applied to collusion following the individual use of facilitating practices. This notion is put into question, and a new approach distinguishing between (lawful) conscious parallelism and (illegal) unilateral collusion is presented.

Algorithmic collusion essentially constitutes a form of monopolistic agreement that utilizes algorithms as tools for signaling collusion, making it particularly challenging for both consumers and antitrust enforcement agencies to detect. Algorithmic collusion can be primarily categorized into two distinct types: explicit collusion and tacit collusion. This paper specifically investigates the phenomenon of platform-driven tacit algorithmic collusion within the platform economy. Employing an evolutionary game theory approach, we conduct a comprehensive simulation analysis of the economic system involving four key stakeholders: government regulators, platform operators, in-platform merchants, and consumers. This paper primarily investigates the conditions that may reduce the likelihood of platforms engaging in algorithmic tacit collusion, examines how government incentive–penalty mechanisms influence such collusive behaviors, and provides an in-depth analysis of the critical roles played by both in-platform merchants and consumers in detecting and exposing these practices.

Increasingly sophisticated algorithms have the potential to transform market dynamics and, as such, may require a revised response from a competition law perspective. This article aims to demystify common misconceptions about algorithms, elucidate some of the challenges they pose to competition law and propose viable solutions. The discussion focuses on the potential for self-learning algorithms to collude in more instances and without human interference, which could result in supra-competitive prices and reduced consumer welfare. Based on self-learning algorithms’ potential cost to consumers and their tension with the objectives of European competition law, the need arises for a nuanced legal framework to approach algorithmic tacit collusion. Algorithmic tacit collusion can be similar to a cartel in its effects. Regulatory intervention may, therefore, be necessary to ensure consumer welfare and functioning markets in the digital age. By broadening the interpretation of an anticompetitive concertation to establish collusive behaviour and harnessing procedural presumptions, algorithmic tacit collusion may be distinguished from instances of conventional tacit collusion and subsumed under the notion of a concerted practice in which the participants knowingly substitute practical cooperation between them for the risks of competition.

We study the equilibrium sourcing strategies – single sourcing or dual sourcing – of two competing retailers with access to two suppliers that are subject to disruptions. In the absence of minimum order quantity requirements, we show that both retailers will use dual sourcing. However, while dual sourcing is optimal for a monopolistic retailer, competing retailers would be better off under single sourcing, and the dual-sourcing equilibrium arises as a prisoner's dilemma. We show how minimum order constraints allow the retailers to sustain a more profitable single sourcing equilibrium with lower expected market supply and higher market prices. Imposed by suppliers to enable efficiencies and economies in production and logistics, our results thus suggest that minimum order quantities might benefit competing retailers, enabling them to avoid the prisoner's dilemma of dual sourcing. Minimum order quantity requirements will be harmful to consumers though if they have to pay higher prices in a single-sourcing equilibrium. As minimum order quantity requirements might reduce downstream competition and increase consumer prices, our findings suggest that regulators should carefully consider whether such requirements are truly justifiable by supplier-side process efficiencies, or whether they are primarily employed as a means to facilitate tacit collusion.