Explicit Collusion Research Articles

Heavy-tailed distributions of volume and price-change resulting from strategy coordination and decision noise

An agent-based model is created to determine the distribution of volume and price change resulting from orders to purchase a stock from high-frequency/short-term, boundedly rational trading agents. The agents who place the orders follow different strategies. Agents who follow the same strategy have coordinated behavior, even if they do not share information—this is tacit collusion (explicit collusion happened during the GameStop and other short squeezes). We look at incoming orders that are composed of a given number of agents that can range from one to ten thousand. It is assumed that each incoming order is a result of a trigger from a particular strategy. A square root price impact function is used to determine the effect on price as a result of the order. The profit of an agent is determined from this price change since they are high-frequency traders. The model produces power-law distributions for individual quantity, aggregate quantity, and price-change, and we fit three stocks to the model by adjusting the temperature. When the market reflects reversion-to-the-mean behavior, the distributions appear to have lighter tails than when the market reflects the hot-hand behavior. The higher risk of heavier tails is seen to be a result of hot-hand trading behavior for this model. The model also has a phase transition in the hot-hand component, in the sense of a nonanalyticity, where the order parameter is the exponent of the power-law tail of the c.d.f. function for aggregate quantity Q. This order parameter is a function of the temperature, varies linearly with inverse temperature in a high-temperature region, and varies nonlinearly in a low-temperature region. The relationship predicted between temperature and aggregate volume has been validated by data from the GameStop short squeeze.

Coordinated Effects in Merger Review

Coordinated effects are merger-related harms that arise because a subset of postmerger firms modify their conduct to limit competition among themselves, particularly in ways other than explicit collusion. We provide a measure of the risk of such conduct by examining the individual rationality of participation by subsets of firms in market allocation schemes. This measure of risk for coordinated effects distinguishes markets that are at risk from those that are not and distinguishes mergers that increase risk from those that do not. A market’s risk for market allocation by a subset of firms varies with the degree of outside competition, symmetry and strength of the subset of firms, buyers’ power, and vertical integration. We make precise the widely used but rarely rigorously defined notion of a maverick firm and provide foundations for a maverick-based approach to coordinated effects. In addition, we identify previously unrecognized trade-offs between unilateral and coordinated effects.

The anti-collusion dilemma: Information sharing of the supply chain under buyback contracts

While previous studies did not consider that buyback contracts in collusive research of supply chains and the buyback contract particularly strengthens collusion between retailers in the supply chain, we address a new research question: What is the effect of different retailer collusive patterns and information sharing mechanisms on the benefits of each member in supply chains with buyback contracts? To answer this research question, we develop four two-echelon supply chain models. The model consists of one supplier and two competing retailers. We consider the two dimensions of collusion mode and information sharing type and derive the optimal equilibrium solutions for each member. The theoretical analysis demonstrates the following results: (i) With explicit collusion, retailers are less willing to vertically share information with the supplier. (ii) Tacit collusion with low demand uncertainty motivates information sharing because the wholesale price can correctly convey the supplier’s information to retailers. (iii) Increasing the probability of high demand or reducing the buyback rate can benefit all members in the supply chain. (iv) Different from the wholesale price and revenue sharing contract, in the buyback contract, the whole supply chain sustains losses from vertical information sharing under the condition of collusion with high demand uncertainty. A numerical experiment shows interesting insights: Although explicit collusion generally brings profit improvement to retailers, they prefer to choose tacit collusion to effectively identify price signals when the demand uncertainty is lower. We check the robustness of results and find that the main results will not be changed in the extended model where the buyback price is lower than the wholesale price.

Identification Standards for Collective Dominance in the Digital Era

Collusion among enterprises can restrict market competition, and anti-monopoly laws in various countries all regulate collusion. Collusion is divided into explicit collusion and tacit collusion: The former stresses mental connection or information exchange among operators, which can be proved by direct evidence; the latter refers to actual coordinated actions among operators without explicit and perceivable agreements and connections, which mostly occurs in oligopoly markets. The monopoly agreement system once constituted the main legal basis for tacit collusion, but it is difficult to prove the mental connection in tacit collusion that is the basis of identifying monopoly agreement. Based on this, the European Union introduced the collective dominance system. This approach solves the problem of proof of mental connection under the monopoly agreement system. However, there are controversies over why several operators are identified as a whole to prove the existence of collective dominance, and what standards should be satisfied to identify the existence of such collective dominance.The collective dominance system has its unique value in the digital era. On the one hand, the digital market features high concentration, and the higher the market concentration, the more likely operators are to coordinate their actions by tacit collusion. On the other hand, even if the oligopoly market has not yet emerged, the combination of algorithms and big data also makes the market more transparent. Accompanied by the continuous development of predictable and autonomous algorithms, and the gradual improvement of virtual supervision and punishment mechanisms, it is easier for operators in the digital market to conduct consistent acts. When it is difficult to prove monopoly agreements, the collective dominance system matters in solving the tacit collusion among enterprises in the digital era.However, the application of the collective dominance system is facing the confusion of identification standards in China: The Anti-Monopoly Law fails to establish special standards for the identification of collective dominance, and in practice, the anti-monopoly enforcement agencies also presume and identify collective dominance in a too simple manner. The concept of collective dominance originates from the EU that regards “economic links” and “market structure” as the main standards for identifying collective dominance through case law. But in different cases, identification standards are different, and the opinions of the European Commission and the courts are not consistent. The standards for identifying collective dominance can be divided into two classes: the identification of “collective” and the identification of “dominance”. Then the two classes can be divided into four elements: There is a specific market environment or economic links that are conducive to the conduct of consistent acts by operators; operators form a whole by conducting consistent acts so that they can be regarded as a collective entity and regulated under the single dominance system; operators have integrated anti-competitive consciousness to conduct consistent acts; and the whole can behave independently of competitors, customers and consumers.

The Application of Current Antitrust Law to Explicit Collusion by Autonomously Acting Pricing Algorithms

This paper aims to answer the question whether the current (EU) antitrust law is well-equipped to deal with explicit collusion by pricing algorithms that act independently from the companies using them. The paper unfolds in three main parts: first, it outlines the exact circumstances of the later discussed scenario and the differentiation to other ways in which algorithms can be related to infringements of antitrust law. Then it investigates whether the use of algorithms specified in the first part falls under the scope of antitrust law in general, that is whether the term 'agreement' according to Art. 101 (1) TFEU is applicable to independently acting algorithms or whether any other tool of current antitrust law (here: concerted practices) enables for the application of Art. 101 (1) TFEU to algorithmic price-setting. Finally, it assesses if the company using the algorithm can be held responsible for any breach of antitrust law that the algorithm is engaged in. It does so by applying the concept of attribution of liability for acts of employees to the employing firm. My hypothesis is that there is no fundamental difference between the conduct of deep learning algorithms and human employees and that as a result the same principles of attribution of liability to firms can be applied.

Searching for the Concentration-Price Effect in the German Movie Theater Industry

This paper investigates whether a price-concentration relationship can be found on local cinema markets in Germany. First, we test a model of monopolistic pricing using a new set of German micro data and find no significant difference in admission prices on monopoly and oligopoly markets. In a next step, we test whether this can be explained by the existence of local monopolies, but find no hint of that. Implicit or explicit collusion among cinema operators might explain our observations.

Relative Performance Evaluation, Sabotage and Collusion

Relative performance evaluation (“RPE”) is a useful tool for shielding risk averse agents from systematic uncertainty. However, RPE can also destroy firm value by encouraging executives to implement excessively aggressive product market strategies to improve their relative standing through costly sabotage. We posit that explicit collusion restricts a firm’s ability to engage in sabotage, thereby improving the net benefits of RPE. Consistent with this hypothesis, we document that: (1) cartel members are more likely to use RPE, especially in more concentrated markets; (2) conditional on using RPE, cartel members include more economically similar firms in their peer group; (3) firms are disproportionately likely to drop RPE from their executives’ pay package within one year of their cartel being dissolved by a plausibly exogenous intervention; and (4) RPE is associated with greater product market aggression, but only among non-cartel firms. Collectively, our evidence suggests that the potential for costly sabotage is an important deterrent to firms’ reliance on RPE, and that explicit collusion mitigates this effect, thereby facilitating more efficient risk-sharing.

The effect of collusion on efficiency in experimental auctions

This paper examines the effect of collusion on allocative efficiency in a second-price sealed-bid auction, in which bidders' valuations have both private and common value components. We present a theoretical model which shows that explicit collusion improves average efficiency. Furthermore, a reduction in common value signal variance increases the efficiency of allocations when a cartel is present. We test for the presence of these patterns in a laboratory experiment. Subjects can choose whether to compete or to form a cartel. Colluding bidders can communicate and make side payments using a knockout auction. Our results show that a large majority of bidders joins a cartel and collusion has a negative impact on efficiency.

Strategic information sharing under revenue-sharing contract: Explicit vs. tacit collusion in retailers

This work explores collusion and information sharing in a supply chain consisting of two downstream retailers and a mutual upstream manufacturer with the coordination by a revenue-sharing contract. We first analyze the equilibrium strategies of members in different types of collusion and the effect of collusion on information sharing. Considering the consumer surplus, downstream competition, collusion preference, and sharing with payment, we study the relationship among these factors and information sharing. We find that, under the revenue-sharing contract, explicit collusion will completely inhibit information sharing and tacit collusion can partially discourage the retailers from sharing information only if the wholesale price cannot accurately deliver the manufacturer’s information to retailers, as opposed to that under a wholesale-price contract. Moreover, we show that the downstream market will attend to use the explicit collusion with the increase of the accurate signal and the weaker quantity competition between retailers will contribute to collusion. However it will not change the ways of collusion and information sharing. Finally, we demonstrate that information sharing can be achieved through side payment if the manufacturer gives retailers subsidies and tacit collusion is not always better than explicit collusion in terms of consumer surplus. Our research provides new interesting insights and makes difference with the existing studies which show that tacit collusion can result in lower consumer surplus.

Controlling Algorithmic Collusion: Short Review of the Literature, Undecidability, and Alternative Approaches

Algorithms have played an increasingly important role in economic activity, as they becoming faster and smarter. Together with the increasing use of ever larger data sets, they may lead to significant changes in the way markets work. These developments have been raising concerns not only over the rights to privacy and consumers’ autonomy, but also on competition. Infringements of antitrust laws involving the use of algorithms have occurred in the past. However, current concerns are of a different nature as they relate to the role algorithms can play as facilitators of collusive behavior in repeated games, and the role increasingly sophisticated algorithms can play as autonomous implementers of pricing strategies, learning to collude without any explicit instructions provided by human agents. In particular, it is recognized that the use of ‘learning algorithms’ can facilitate tacit collusion and lead to an increased blurring of borders between tacit and explicit collusion. Several authors who have addressed the possibilities for achieving tacit collusion equilibrium outcomes by algorithms interacting autonomously, have also considered some form of ex-ante assessment and regulation over the type of algorithms used by firms. By using well-known results in the theory of computation, I show that such option faces serious challenges to its effectiveness due to undecidability results. Ex-post assessment may be constrained as well. Notwithstanding several challenges face by current software testing methodologies, competition law enforcement and policy have much to gain from an interdisciplinary collaboration with computer science and mathematics.

How Much Is No Longer A Simple Question – Pricing Algorithms and Antitrust Laws

With the backdrop of increased use of algorithms in doing business, the objective of this paper is examining in-depth the use of one such category of algorithms – pricing algorithms. The scope of this paper is confined to two potential issues associated with the use of pricing algorithms – algorithmic collusion and personalized pricing. Finally, this paper offers solutions that may be used by antitrust agencies in the United States, i.e., the Department of Justice and the Federal Trade Commission, to deal with these issues within the confines of the existing antitrust jurisprudence. Section I of this paper describes what pricing algorithms are, and how they assist with dynamic pricing. The section demonstrates that it is irrefutable that the use of pricing algorithms has improved the competitive landscape by increasing transparency, reducing information asymmetry and improving the allocative efficiency of the market, bringing it closer to the ideal of perfect competition. At the same time, agencies are wary that the use of big data analytics and pricing algorithms could lead to the anti competitive outcomes which both contribute to and are aggravated by concentration of market power. Section II examines the role pricing algorithms play in facilitating in explicit and tacit collusion. The section also concludes that the existing framework of antitrust law would be sufficient while dealing with algorithmic forms of explicit collusion, where an algorithm is used to execute an anti competitive agreement but may not suffice for tackling algorithmic tacit collusion. Further, trying to uncover evidence of anti-competitive agreement and intent may prove challenging as machine learning is an ongoing process and the design of an algorithm can be complicated to decipher. Section III explores the use of algorithms in personalizing pricing. This section notes that while from an economic perspective, personalized pricing fosters static efficiency, it also has the tendency to be used to exploit unsuspecting consumers. Policymakers and antitrust agencies are also concerned about issues of fairness and consumer welfare regarding the use of pricing algorithms. However, given that the unilateral use of a pricing algorithm to set high prices is not an offense under US antitrust laws, agencies are skeptical of prosecuting the use of exploitative personalized pricing. Section IV puts forward some possible solutions. The paper reasons that at this time, rather than making legislative changes, it is essential for the FTC to invest in research into such methods in order to both uncover evidence of anti-competitive agreements and audit algorithms that appear to collude even in the absence of an agreement. Further, this paper argues that FTC may have the locus to investigate exploitative personalized pricing under Section 5 of the FTC Act . Though, the most practical short-term solution seems to be to increase transparency and to put power back into the hands of the consumer.

Frequency of interaction, communication and collusion: an experiment

The frequency of interaction facilitates collusion by reducing gains from defection. Theory has shown that under imperfect monitoring flexibility may hinder cooperation by inducing punishment after too few noisy signals, making collusion impossible in many environments (Sannikov and Skrzypacz in Am Econ Rev 97:1794–1823, 2007). The interplay of these forces should generate an inverse U-shaped effect of flexibility on collusion. We test for the first time these theoretical predictions—central to antitrust policy—in a laboratory experiment featuring an indefinitely repeated Cournot duopoly, with different degrees of flexibility. Results turn out to depend crucially on whether subjects can communicate with each other at the beginning of a supergame (explicit collusion) or not (tacit collusion). Without communication, the incidence of collusion is low throughout and not significantly related to flexibility; when subjects are allowed to communicate, collusion is more common throughout and significantly more frequent in the treatment with intermediate flexibility than in the treatments with low or high flexibility.

Controlling Algorithmic Collusion: Short Review of the Literature, Undecidability, and Alternative Approaches

Algorithms have played an increasingly important role in economic activity, as they becoming faster and smarter.Together with the increasing use of ever larger data sets, they may lead to significant changes in the way markets work. These developments have been raising concerns not only over the rights to privacy and consumers’ autonomy, but also on competition. Infringements of antitrust laws involving the use of algorithms have occurred in the past. However, current concerns are of a different nature as they relate to the role algorithms can play as facilitators of collusive behavior in repeated games, and the role increasingly sophisticated algorithms can play as autonomous implementers of pricing strategies, learning to collude without any explicit instructions provided by humanagents. In particular, it is recognized that the use of ‘learning algorithms’ can facilitate tacit collusion and lead to an increased blurring of borders between tacit and explicit collusion. Several authors who have addressed the possibilities for achieving tacit collusion equilibrium outcomes by algorithms interacting autonomously, have also consideredsome form of ex-ante assessment and regulation over the type of algorithms used by firms. By using well-known resultsin the theory of computation, I showthat such option faces serious challenges to its effectivenessdue to undecidability results. Ex-post assessment may be constrained as well. Notwithstanding several challenges face by current software testing methodologies, competition law enforcement and policy have much to gain from an interdisciplinary collaboration with computer science and mathematics.

Big Data and Competition Law

Abstract The emergence of Big Data in the digital economy and its corresponding impact on competition law has led to considerable debate in Europe over the last few years. A key concern for competition agencies is the potential harm that might be caused by a company’s ability to acquire datasets that other industry players cannot replicate. It is feared that the control of data may enable these firms to strengthen their market positions and to engage in anticompetitive behaviour, which could deprive consumers of the potential benefits that Big Data offers. Specifically, dominant companies might use Big Data to limit competition or to exploit consumers. In addition, tacit or explicit collusion amongst competitors could be facilitated by greater market transparency and the increasing application of algorithms. In future data-related transactions the potential aggregation of large datasets might also lead to greater scrutiny on the impacts of Big Data and the potential it might have to erect barriers to entry in the specific market and prevent viable competition. As such, regulators and policy makers are contemplating new approaches to ensure that data-related transactions that could significantly impede effective competition do not escape merger control scrutiny. This article considers these potential impacts of Big Data in competition law and outlines the key issues that might need to be addressed.

Explicit vs tacit collusion: The effects of firm numbers and asymmetries

In an infinitely repeated game where firms with (possibly asymmetric) capacity constraints can make secret price cuts, we analyse the incentives for explicit collusion when firms can alternatively collude tacitly. Tacit collusion can involve price wars on the equilibrium path. Explicit collusion involves firms secretly sharing their private information to avoid such price wars, but this is illegal and runs the risk of sanctions. We find that, in contrast to the conventional wisdom but consistent with some empirical evidence, illegal cartels are least likely to arise in markets with a few symmetric firms, because tacit collusion is relatively more appealing in such markets. We discuss the implications for anti-cartel enforcement policy.

Firm-specific information and explicit collusion in experimental oligopolies

We experimentally study the effect of information about competitors’ actions on cartel stability and firms’ incentives to form cartels in Cournot markets. As in previous experiments, markets become very competitive when individualized information is available and participants cannot communicate. In contrast, when communication is possible, results reverse: markets become less competitive and cartels become more stable when individualized information is available. We also observe that the extra profits that firms obtain thanks to the possibility to communicate are higher when individualized information is present, suggesting that firms have greater incentives to form cartels in that situation.

Focal point pricing: A challenge to the successful implementation of Section 10a (introduced by the Competition Amendment Act)

The Competition Amendment Act introduced section 10A, which provides the Competition Commission with the powers to investigate complex monopoly conduct in a market and allows the Competition Tribunal, under certain conditions, to prohibit such behaviour. Although more than five years have elapsed since the Competition Amendment Act was promulgated, this provision has yet to come into force. However, when it eventually does so, it will mark a significant change in South African competition law, as it seeks to regulate firms’ consciously parallel conduct. This is coordinated conduct that occurs without communication or agreement, but results in the prevention or substantial lessening of competition. Examples of horizontal tacit coordination practices include price leadership and facilitating practices, such as information exchanges and price signaling. The successful implementation of the amendment poses problems for the competition authorities in assessing the competitive effects of complex monopoly conduct and in providing effective remedies. Oligopoly markets result in mutual interdependent decision-making by firms, which can lead to market outcomes similar to explicit collusion. However, a further and little noticed issue is that firms in oligopolistic markets have opportunities to use focal points to determine coordinated strategies. This paper explores the nature and role of focal point pricing, which can lead to prices that are above competitive levels. The South African banking industry is used as an example. We find that focal point pricing is difficult to control, making the successful implementation of section 10A even more problematic. Moreover, the proposed amendment provides scope for the imposition of structural remedies by the Competition Tribunal, a function that the Competition Tribunal is ill-suited to perform.

ECONOMETRIC EVIDENCE TO TARGET TACIT COLLUSION IN OLIGOPOLISTIC MARKETS

Tacit collusion may reduce welfare comparably to explicit collusion, but it remains mostly unaddressed by antitrust enforcement that greatly depends on evidence of explicit communication. We propose to target specific elements of firms' behavior that facilitate tacit collusion by providing quantitative evidence that links these actions to an anticompetitive market outcome. We apply our approach to incidents on the Italian gasoline market, where the market leader unilaterally announced its commitment to a policy of sticky pricing and large price changes that facilitated price alignment and coordination of price changes. Antitrust policy must distinguish such active promotion of a collusive strategy from passive, best-response, alignment. Our results imply the necessity of stronger legal instruments that target unilateral conduct that aims at bringing about collusion.

Focal point pricing: A challenge to the successful implementation of Section 10A (introduced by the Competition Amendment Act)

The Competition Amendment Act introduced section 10A, which provides the Competition Commission with the powers to investigate complex monopoly conduct in a market and allows the Competition Tribunal, under certain conditions, to prohibit such behaviour. Although more than five years have elapsed since the Competition Amendment Act was promulgated, this provision has yet to come into force. However, when it eventually does so, it will mark a significant change in South African competition law, as it seeks to regulate firms’ consciously parallel conduct. This is coordinated conduct that occurs without communication or agreement, but results in the prevention or substantial lessening of competition. Examples of horizontal tacit coordination practices include price leadership and facilitating practices, such as information exchanges and price signaling. The successful implementation of the amendment poses problems for the competition authorities in assessing the competitive effects of complex monopoly conduct and in providing effective remedies. Oligopoly markets result in mutual interdependent decision-making by firms, which can lead to market outcomes similar to explicit collusion. However, a further and little noticed issue is that firms in oligopolistic markets have opportunities to use focal points to determine coordinated strategies. This paper explores the nature and role of focal point pricing, which can lead to prices that are above competitive levels. The South African banking industry is used as an example. We find that focal point pricing is difficult to control, making the successful implementation of section 10A even more problematic. Moreover, the proposed amendment provides scope for the imposition of structural remedies by the Competition Tribunal, a function that the Competition Tribunal is ill-suited to perform.

Comparative Analusis of Antitrust Policy Against Collusion in Some Transition Economies: Challenges for Effectiveness

This article focuses on the development of antitrust policy in transition economies in the context of preventing explicit and tacit collusion. Experience of BRICS, Kazakhstan, Ukraine and CEE countries (Bulgaria, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, Slovenia, Czech Republic, Estonia) in the creation of antitrust institutions was analyzed, including both legislation and enforcement practice. This article analyzes such enforcement problems as: classification problems (tacit vs explicit collusion, vertical vs horizontal agreements), flexibility of prohibitions (“per se” vs “rule of reason”), design of sanctions, private enforcement challenge, leniency program mechanisms, the role of antitrust authorities etc. Main challenges for policy effectiveness in this field were shown.