Artificial Intelligence Algorithmic Pricing And Collusion
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Artificial Intelligence Algorithmic Pricing And Collusion
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Author : Emilio Calvano
language : en
Publisher:
Release Date : 2018
Artificial Intelligence Algorithmic Pricing And Collusion written by Emilio Calvano and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Artificial intelligence categories.
Pricing algorithms are increasingly replacing human decision making in real marketplaces. To inform the competition policy debate on possible consequences, we run experiments with pricing algorithms powered by Artificial Intelligence in controlled environments (computer simulations). In particular, we study the interaction among a number of Q-learning algorithms in the context of a workhorse oligopoly model of price competition with Logit demand and constant marginal costs. We show that the algorithms consistently learn to charge supra-competitive prices, without communicating with each other. The high prices are sustained by classical collusive strategies with a finite punishment phase followed by a gradual return to cooperation. This finding is robust to asymmetries in cost or demand and to changes in the number of players.
Product Rankings Ai Pricing Algorithms And Collusion
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Author : Liying Qiu
language : en
Publisher:
Release Date : 2022
Product Rankings Ai Pricing Algorithms And Collusion written by Liying Qiu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Reinforcement learning (RL) based pricing algorithms have been shown to tacitly collude to set supra-competitive prices in oligopoly models of repeated price competition. We investigate the impact of ranking systems, a common feature of online marketplaces, on algorithmic collusion in prices. We study experimentally the behavior of algorithms powered by Artificial Intelligence (deep Q-learning) in a workhorse duopoly model of repeated price competition in the presence of product rankings. Through extensive experiments, we find that the introduction of the ranking system significantly mitigates the tacit collusion that stems from RL based pricing. The ranking system increases the incentives for the RL agents to deviate from a collusive price which in turn requires more complicated punishment strategies to prevent deviation and sustain collusive prices. These punishment strategies are harder to learn for RL algorithms in non stationary environments and the high collusive prices are not sustained as a result. The ranking system's mitigation effect is moderated by the horizontal differentiation between the products offered by the firms and the stickiness of product ranks. In particular, when products are more horizontally differentiated from each other and when past sales have a larger influence on product ranks (sticky ranking), the prices charged by the two firms are higher and the ranking system's mitigation effect is weaker. However, in both cases, prices in the presence of ranking are lower than that in the absence of ranking. Our analysis sheds light on the impact of ranking systems on consumer welfare and on design of ranking systems to prevent algorithmic pricing collusion.
Pricing Via Artificial Intelligence
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Author : Weipeng Zhang
language : en
Publisher:
Release Date : 2023
Pricing Via Artificial Intelligence written by Weipeng Zhang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
Classic artificial intelligence (Q-learning) algorithms have been capable of consistently learning supra-competitive pricing strategies in infinitely repeated Nash-Bertrand pricing games without human communication. Such algorithms have been able to converge due to the temporal correlation of consecutive states and actions in the learning process, which restores stationarity in an otherwise highly non-stationary setting. It is difficult for more realistic AI algorithms to converge, as the necessary training processes breaks the aforementioned temporal correlation, rendering the algorithms ineffective in learning reward-punishment strategies that result in collusive market outcomes. We adapt several widely used neural network architectures to the framework of model-free reinforcement learning and experimentally explore how the structure of AI algorithms affects market outcomes in a workhorse oligopolistic model of repeated price competition. While it is possible to train advance AI algorithms to always best respond in environments where the rival exercises a fixed strategy, it is unlikely that such algorithms can learn to coordinate in setting supra-competitive prices due to the non-stationarity of multi-agent learning processes, suggesting that algorithmic collusion may not be an immediate concern for antitrust authorities.
Algorithms Collusion And Competition Law
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Author : Steven Van Uytsel
language : en
Publisher: Edward Elgar Publishing
Release Date : 2023-01-20
Algorithms Collusion And Competition Law written by Steven Van Uytsel and has been published by Edward Elgar Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-20 with Law categories.
What is algorithmic collusion? This evaluative book provides an insight into tackling this important question for competition law, with contrasting critical perspectives, including theoretical, empirical, and doctrinal – the latter frequently from a comparative perspective. Bringing together scholarly discussion on algorithmic collusion, the book questions whether competition law is adeptly equipped to deal with its various facets.
Algorithmic Antitrust
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Author : Aurelien Portuese
language : en
Publisher: Springer Nature
Release Date : 2022-01-21
Algorithmic Antitrust written by Aurelien Portuese and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-21 with Law categories.
Algorithms are ubiquitous in our daily lives. They affect the way we shop, interact, and make exchanges on the marketplace. In this regard, algorithms can also shape competition on the marketplace. Companies employ algorithms as technologically innovative tools in an effort to edge out competitors. Antitrust agencies have increasingly recognized the competitive benefits, but also competitive risks that algorithms entail. Over the last few years, many algorithm-driven companies in the digital economy have been investigated, prosecuted and fined, mostly for allegedly unfair algorithm design. Legislative proposals aim at regulating the way algorithms shape competition. Consequently, a so-called “algorithmic antitrust” theory and practice have also emerged. This book provides a more innovation-driven perspective on the way antitrust agencies should approach algorithmic antitrust. To date, the analysis of algorithmic antitrust has predominantly been shaped by pessimistic approaches to the risks of algorithms on the competitive environment. With the benefit of the lessons learned over the last few years, this book assesses whether these risks have actually materialized and whether antitrust laws need to be adapted accordingly. Effective algorithmic antitrust requires to adequately assess the pro- and anti-competitive effects of algorithms on the basis of concrete evidence and innovation-related concerns. With a particular emphasis on the European perspective, this book brings together experts and scrutinizes on the implications of algorithmic antitrust for regulation and innovation.
Artificial Intelligence And Collusion
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Author : Steven Van Uytsel
language : en
Publisher:
Release Date : 2020
Artificial Intelligence And Collusion written by Steven Van Uytsel and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
The use of algorithms in pricing strategies has received special attention among competition law scholars. There is an increasing number of scholars who argue that the pricing algorithms, facilitated by increased access to data, could move in the direction of collusive price setting. Though this claim is being made, there are various responses. On the one hand, scholars point out that current artificial intelligence is not yet well-developed to trigger that result. On the other hand, scholars argue that algorithms may have other pricing results rather than collusion. Despite the uncertainty that collusive price could be the result of the use of pricing algorithms, a plethora of scholars are developing views on how to deal with collusive price setting caused by algorithms. The most obvious choice is to work with the legal instruments currently available. Beyond this choice, scholars also suggest constructing a new rule of reason. This rule would allow us to judge whether an algorithm could be used or not. Other scholars focus on developing a test environment. Still other scholars seek solutions outside competition law and elaborate on how privacy regulation or transparency reducing regulation could counteract a collusive outcome. Besides looking at law, there are also scholars arguing that technology will allow us to respond to the excesses of pricing algorithms. It is the purpose of this chapter to give a detailed overview of this debate on algorithms, price setting and competition law.
Algorithmic Pricing Collusion The Limits Of Antitrust Enforcement
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Author : Sumit Bhadauria
language : en
Publisher:
Release Date : 2020
Algorithmic Pricing Collusion The Limits Of Antitrust Enforcement written by Sumit Bhadauria and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
The combination of big data, large storage capacity and computational power has strengthened the emergence of algorithms in making myriads of business decision. It allows business to gain a competitive advantage by making automatic and optimize decision making. In particular, the use of pricing algorithms allows business to match the demand and supply equilibrium by monitoring & setting dynamic pricing. It benefits consumer alike to see and act on fast changing prices. However, on the downside, the widespread use of algorithm in an industry has the effect of altering the structural characteristic of market such as price transparency, high speed trading which increases the likelihood of collusion. The ability of pricing algorithm to solve the cartel incentive problem by quickly detecting and punishing the deviant further strengthen the enforcement of price fixing agreement. In addition, the use of more advance forms of algorithm such as self-learning algorithm allows business to achieve a tacitly collusive outcome in limited market characteristic even without communication between humans. This raises the fundamental challenge for anti-cartel enforcement as the current law in most jurisdictions is ill-equipped to deal with algorithmic facilitated tacit collusion. The legality of tacit collusion is questionable primarily because the pricing algorithm has the ability to alter the market characteristics where the tacitly collusive outcome is difficult to achieve; thus widening the scope of the so-called 'oligopoly problem'. This paper studies the usages of pricing algorithms by business in online markets. In particular, the paper identify the conditions under which the algorithm prices causes the harm to consumers. It seeks to analyze how algorithms might facilitate or even causes the collusive outcome without human interventions. Further, it looks at the legal challenges faced by the competition authorities around the globe to deal with the algorithmic let collusion and examine the various approaches suggested to counter act it.
What Do We Know About Algorithmic Tacit Collusion
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Author : Ai Deng
language : en
Publisher:
Release Date : 2019
What Do We Know About Algorithmic Tacit Collusion written by Ai Deng and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
The past few years have seen many legal scholars and antitrust agencies expressing interest in and concerns with algorithmic collusion. In this paper, I survey and draw lessons from the literature on Artificial Intelligence and on the economics of algorithmic tacit collusion. I show that a good understanding of this literature is a crucial first step to better understand the antitrust risks of algorithmic pricing and devise antitrust policies to combat such risks.
Artificial Intelligence And Pricing
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Author : Diego Aparicio
language : en
Publisher:
Release Date : 2022
Artificial Intelligence And Pricing written by Diego Aparicio and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
As businesses become more sophisticated and welcome new technologies, artificial intelligence-based methods are increasingly being used for firms' pricing decisions. In this review article, we provide a survey of research in the area of AI and pricing. On the upside, research has shown that algorithms allow companies to achieve unprecedented advantages, including real-time response to demand and supply shocks, personalized pricing, and demand learning. However, recent research has uncovered unforeseen downsides to algorithmic pricing that are important for managers and policy makers to consider.
Algorithmic Pricing And Consumer Sensitivity To Price Variability
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Author : Diego Aparicio
language : en
Publisher:
Release Date : 2023
Algorithmic Pricing And Consumer Sensitivity To Price Variability written by Diego Aparicio and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
Artificial Intelligence (AI) automates human decisions. Algorithmic pricing, a form of AI, sets prices by a computer. It is now common currency in ride-hailing, travel, drugs, gasoline, online goods--And great price variability characterizes all those settings. However, little is known about how consumers respond to encountering frequently changing prices. This paper uses clickstream data from an online retailer in the U.S. that varied pricing methods to examine effects of frequently-changing prices on purchase behavior. The evidence shows that exposure to price variability exacerbates price sensitivity. These findings are confirmed in online lab experiments. Additionally, an underlying mechanism is price salience.