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ANALYSING THE IMPLICATIONS OF ALGORITHMIC COLLUSION: THE WAY FORWARD FOR THE CCI?

Updated: Feb 27

This post is authored by Kunal Singh, 2nd-year B.A.LL.B (Hons.) student at Vivekananda Institute of Professional Studies, GGSIPU.

Image Credits:- OECD

1. OVERVIEW


Enterprises in this modern world are heavily reliant on algorithms to track and amass data for delivering efficient and targeted services to consumers. Although not defined clearly, an algorithm is an application of reasoning by Artificial Intelligence ("AI") for finding a probable solution to a problem. This reliance on algorithms can be attributed to their cost and efficiency benefits. Enterprises such as Netflix and Spotify use algorithms to deliver a personalized user experience by looking into the search history and activity of the user. These algorithms contribute to an enterprise in numerous ways to facilitate the conduct of business, such as by controlling production, collecting market data, and self-learning through regular monitoring of market conditions.


However, they can also raise antitrust concerns depending upon their functionality. For instance, the personalized pricing used by enterprises to curate a user-friendly experience often dodges the antitrust regulators’ eyes. It causes significant consumer harm by subjecting them to higher prices based on their purchasing thresholds. This article seeks to critically analyze the implications of algorithmic collusion and the approach of the Competition Commission of India ("CCI") towards curbing it.


2. ALGORITHMIC COLLUSION


2.1 Dynamic Pricing

AI allows an enterprise to check and monitor market conditions and rival competitors’ activities and enables them to deliver targeted offerings. It mostly functions in two-sided markets where both buyers and sellers meet to exchange a product or service. Enterprises use AI facilitated dynamic pricing, often called ‘surge pricing’ (for cab aggregators), which is different from traditional pricing. It helps to find the right balance between price and availability that offers the maximum profit and modulates price as per the demand. It facilitates continuous prediction, adjusts prices within minutes by reviewing market numbers, and generates revenue by reducing speculation.


Cab aggregators such as Ola and Uber, use surge pricing to determine the price for a particular ride. When someone books a cab on their platform, AI quickly reviews driver availability, traffic in the route, consumer’s history, and frequency of using the platform before displaying a price for that ride. This is the major reason behind the variability of prices during different times. Cab aggregators function in an oligopolistic market whereby an enterprise’s performance depends on the rival firm’s policies and pricing strategies. Easy availability of information of rival competitors allows AI to monitor their pricing strategies and determine their prices, causing indirect price fluctuations. Such fluctuations do not allow prices to change quickly, reducing substitutability and causing harm to consumer welfare. Such fluctuations in prices, however, are not anticompetitive in themselves.


2.2 Price Personalisation

Likewise, many enterprises use AI to look into consumers’ preferences, search history, and the purchasing threshold, to display prices accordingly. This is known as price personalization or more precisely, price discrimination. It is a form of pricing that involves charging different prices to consumers according to their willingness to pay and primarily focuses on B2C relations. A great example of price personalization is the hotel website Orbitz, which uses AI to collect data such as browser type, zip code, and even the type of device consumer uses, and determine prices accordingly. For example, consumers using Mac devices may expect higher prices. Here, AI predicts that consumers using Mac devices are able to pay more than other consumers, and likewise displays higher prices. Even though this form of pricing fosters consumer loyalty and a user-friendly experience, it also entails significant consumer harm.


The absence of any explicit agreement indicating coordination between/among enterprises is a major regulatory challenge for the CCI and gives rise to Tacit Price Collusion ("TPC"). This is different from the type of collusion covered under section 3(3) of The Competition Act, 2002 ("the Act"), which governs collusion through an explicit agreement between enterprises/group of enterprises. TPC arises where enterprises/groups of enterprises collude informally (without explicitly agreeing) with their rivals to determine their strategies.


According to Ariel Ezrachi and Maurice Stucke, AI can collude in four possible ways:

1. Hub and Spoke Cartels: In cases of hub and spoke cartels, there is an informal coordination between people operating at the same level of the production chain (horizontal price coordination) and the persons/enterprises operating at a different level of the production chain (vertical level), in order to determine prices. In the Eturas case, many Lithuanian travel agencies or spokes used a common platform or hub (“Eturas”) for limiting the possibility of applying discounts of more than 3%, which was likely to have an anticompetitive effect. On the other hand, Eturas acted as a facilitator by circulating the travel agencies’ discount cap message. The Court of Justice of the European Union asserted that the travel agencies’ actions constituted a concerted practice leading to collusion since the travel agencies can be presumed to know that other travel agencies might be using the same platform.


2. Messenger: In these cases, enterprises use algorithms to effect anticompetitive agreements that human beings have previously entered into. The Poster & Frames Case (UK) and the David Topkins Case (USA) are classic landmark decisions in determining antitrust regulators’ stand against algorithmic collusion. In both cases, online sellers agreed not to undersell other competitors, and in pursuance of this, they implemented an agreement through ‘automatic pricing software,’ which automatically adjusted online sellers’ prices to remain aligned to each other. Commissioner Margrethe Vestager considered ‘the risk that automated systems could lead to more effective cartels’ as a potential challenge for cartel enforcement.


3. Predictable Agent: In such cases, each firm/enterprise unilaterally creates an algorithm that continuously monitors and adjusts to another algorithm’s pricing strategies. It can be possible that no two firms are using the same set of algorithms, but the unique algorithms used by both firms are programmed to adjust according to pricing fluctuations of each other.


4. Self-Learning: In these cases, algorithms with excellent predictability skills, by continually monitoring and readapting to competitors’ actions, may be able to collude without human intervention. This type of collusion may happen with or without the knowledge of the programmer. Generally, this type of collusion happens due to the failure of the programmers in implementing necessary safeguards. According to an OECD report, it has been ascertained that algorithms will be able to achieve a cooperative equilibrium with continuous hit and trial method and at a faster speed than humans.


3. CRITICALLY ANALYSING THE CCI'S APPROACH


With regards to this issue, the CCI missed a critical opportunity and adopted a parochial view in the case of Samir Agrawal v. ANI Technologies Pvt. Ltd., which was also upheld by the NCLAT. The CCI in this dismissed a case of a possible hub and spoke cartel against cab aggregators, i.e., Ola and Uber. Further, in an appeal to the Hon’ble Supreme Court of India, the court upheld the orders passed by CCI and NCLAT, stating, that Ola/Uber do not aid cartelization between drivers and also held that drivers are independent individuals who function independently of one another.


Both Ola and Uber in their ‘terms and conditions’ state that they are merely a ‘technological platform’ and ask the users to recognize that they do not provide any type of transportation and logistics services and such services are provided by an independent ‘contractor’ or ‘third party.’ This means that the drivers are not employees/workers of Ola/Uber and are merely independent contractors. Albeit they are independent contractors, they entirely lack bargaining power as they are not at liberty to determine the price of each ride, which are determined by Ola/Uber’s pricing algorithms. Here, the concept of hub and spoke cartels gains significance as cartel members do not communicate directly in order to keep their behaviour aligned but use an intermediary to do so.