Algorithms Machine Learning And Collusion
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Algorithms Machine Learning And Collusion
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Author : Ulrich Schwalbe
language : en
Publisher:
Release Date : 2018
Algorithms Machine Learning And Collusion written by Ulrich Schwalbe and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
This paper discusses the question whether self-learning price-setting algorithms are able to coordinate their pricing behaviour to achieve a collusive outcome that maximizes the joint profits of the firms using these algorithms. While the legal literature generally assumes that algorithmic collusion is indeed possible and in fact very easy, the computer science literature on cooperation between algorithms as well as the economics literature on collusion in experimental oligopolies indicate that a coordinated and in particular tacitly collusive behaviour is in general rather difficult to achieve. Many studies have shown that some form of communication is of vital importance for collusion if there are more than two firms in a market. Communication between algorithms is also a topic in artificial intelligence research and some recent contributions indicate that algorithms may learn to communicate, albeit in a rather limited way. This leads to the conclusion that algorithmic collusion is currently much more difficult to achieve than often assumed in the legal literature and is therefore currently not a particularly important competitive concern. In addition, there are also several legal problems associated with algorithmic collusion, for example, questions of liability, of auditing and monitoring algorithms as well as enforcement. The limited resources of competition authorities should rather be devoted to more pressing problems as, for example, the abuse of dominant positions by large online-platforms.
Collusion Detection In Auctions Using Machine Learning Algorithms
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Author : Muskan Rathi
language : en
Publisher:
Release Date : 2023
Collusion Detection In Auctions Using Machine Learning Algorithms written by Muskan Rathi 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.
This interdisciplinary honors thesis in Computational Economics investigates the different methods to compute auction equilibria and the impact of collusion on auction outcomes and the effectiveness of various machine learning algorithms in detecting collusive behavior using real-world datasets. We develop a program to analyze the Bayesian Nash equilibrium strategies of bidders in first-price and second-price auctions under scenarios with and without collusion. We further explore the performance of different machine learning algorithms, including Support Vector Machine (SVM), which demonstrates the highest F1 score in detecting collusion among the tested algorithms. The challenges associated with obtaining real-life auction data necessitate the use of synthetic data, providing a valuable resource for developing and validating anti-collusion algorithms in the future.This research contributes to a deeper understanding of auction dynamics and collusion, informing policymakers and regulators in designing robust auction mechanisms, implementing effective anti-collusion measures, and promoting fair and efficient market outcomes.
Collusive Algorithms As Mere Tools Super Tools Or Legal Persons
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Author : Guan Zheng
language : en
Publisher:
Release Date : 2023
Collusive Algorithms As Mere Tools Super Tools Or Legal Persons written by Guan Zheng 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.
The widespread use of algorithmic technologies makes rules on tacit collusion, which are already controversial in antitrust law, more complicated. These rules have obvious limitations in effectively regulating algorithmic collusion. Although some scholars and practitioners within antitrust circles in the United States, Europe and beyond have taken notice of this problem, they have failed to a large extent to make clear its specific manifestations, root causes, and effective legal solutions. In this article, the authors make a strong argument that it is no longer appropriate to regard algorithms as mere tools of firms, and that the distinct features of machine learning algorithms as super-tools and as legal persons may inevitably bring about two new cracks in antitrust law. This article clarifies the root causes why these rules are inapplicable to a large extent to algorithmic collusion particularly in the case of machine learning algorithms, classifies the new legal cracks, and provides sound legal criteria for the courts and competition authorities to assess the legality of algorithmic collusion much more accurately. More importantly, this article proposes an efficacious solution to revive the market pricing mechanism for the purposes of resolving the two new cracks identified in antitrust law.
Collusion By Algorithm
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Author : Jeanine Miklós-Thal
language : en
Publisher:
Release Date : 2018
Collusion By Algorithm written by Jeanine Miklós-Thal and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
We build a game-theoretic model to examine how better demand forecasting due to algorithms, machine learning and artificial intelligence affects the sustainability of collusion in an industry. We find that while better forecasting allows colluding firms to better tailor prices to demand conditions, it also increases each firm's temptation to deviate to a lower price in time periods of high predicted demand. Overall, our research suggests that, despite concerns expressed by policymakers, better forecasting and algorithms can lead to lower prices and higher consumer surplus.
Algorithmic Collusion
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Author : Karsten T. Hansen
language : en
Publisher:
Release Date : 2020
Algorithmic Collusion written by Karsten T. Hansen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Competition categories.
Motivated by their increasing prevalence, we study outcomes when competing sellers use machine learning algorithms to run real-time dynamic price experiments. These algorithms are often misspecified, ignoring the effect of factors outside their control, e.g. competitors' prices. We show that the long-run prices depend on the informational value (or signal to noise ratio) of price experiments: if low, the long-run prices are consistent with the static Nash equilibrium of the corresponding full information setting. However, if high, the long-run prices are supra-competitive -- the full information joint-monopoly outcome is possible. We show this occurs via a novel channel: competitors' algorithms' prices end up running correlated experiments. Therefore, sellers' misspecified models overestimate own price sensitivity, resulting in higher prices. We discuss the implications on competition policy.
Artificial Intelligence Collusion
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Author : Ariel Ezrachi
language : en
Publisher:
Release Date : 2019
Artificial Intelligence Collusion written by Ariel Ezrachi 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 development of self-learning and independent computers has long captured our imagination. The HAL 9000 computer, in the 1968 film, 2001: A Space Odyssey, for example, assured, “I am putting myself to the fullest possible use, which is all I think that any conscious entity can ever hope to do.” Machine learning raises many challenging legal and ethical questions as to the relationship between man and machine, humans' control -- or lack of it -- over machines, and accountability for machine activities. While these issues have long captivated our interest, few would envision the day when these developments (and the legal and ethical challenges raised by them) would become an antitrust issue. Sophisticated computers are central to the competitiveness of present and future markets. With the accelerating development of AI, they are set to change the competitive landscape and the nature of competitive restraints. As pricing mechanisms shift to computer pricing algorithms, so too will the types of collusion. We are shifting from the world where executives expressly collude in smoke-filled hotel rooms to a world where pricing algorithms continually monitor and adjust to each other's prices and market data. Our paper addresses these developments and considers the application of competition law to an advanced 'computerised trade environment.' After discussing the way in which computerised technology is changing the competitive landscape, we explore four scenarios where AI can foster anticompetitive collusion and the legal and ethical challenges each scenario raises.
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 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.
How Algorithms Create And Prevent Fake News
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Author : Noah Giansiracusa
language : en
Publisher: Apress
Release Date : 2021-07-15
How Algorithms Create And Prevent Fake News written by Noah Giansiracusa and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-15 with Computers categories.
From deepfakes to GPT-3, deep learning is now powering a new assault on our ability to tell what’s real and what’s not, bringing a whole new algorithmic side to fake news. On the other hand, remarkable methods are being developed to help automate fact-checking and the detection of fake news and doctored media. Success in the modern business world requires you to understand these algorithmic currents, and to recognize the strengths, limits, and impacts of deep learning---especially when it comes to discerning the truth and differentiating fact from fiction. This book tells the stories of this algorithmic battle for the truth and how it impacts individuals and society at large. In doing so, it weaves together the human stories and what’s at stake here, a simplified technical background on how these algorithms work, and an accessible survey of the research literature exploring these various topics. How Algorithms Create and Prevent Fake News is an accessible, broad account of the various ways that data-driven algorithms have been distorting reality and rendering the truth harder to grasp. From news aggregators to Google searches to YouTube recommendations to Facebook news feeds, the way we obtain information today is filtered through the lens of tech giant algorithms. The way data is collected, labelled, and stored has a big impact on the machine learning algorithms that are trained on it, and this is a main source of algorithmic bias – which gets amplified in harmful data feedback loops. Don’t be afraid: with this book you’ll see the remedies and technical solutions that are being applied to oppose these harmful trends. There is hope. What You Will Learn The ways that data labeling and storage impact machine learning and how feedback loops can occur The history and inner-workings of YouTube’s recommendation algorithm The state-of-the-art capabilities of AI-powered text generation (GPT-3) and video synthesis/doctoring (deepfakes) and how these technologies have been used so far The algorithmic tools available to help with automated fact-checking and truth-detection Who This Book is For People who don’t have a technical background (in data, computers, etc.) but who would like to learn how algorithms impact society; business leaders who want to know the powers and perils of relying on artificial intelligence. A secondary audience is people with a technical background who want to explore the larger social and societal impact of their work.
Understanding Ai Collusion And Compliance
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Author : Justin Johnson
language : en
Publisher:
Release Date : 2020
Understanding Ai Collusion And Compliance written by Justin Johnson 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.
Antitrust compliance scholarship, particularly with a focus on collusion, has been an area of study for some time. Changes in technology and the rise of artificial intelligence (AI) and machine-learning create new possibilities both for anti-competitive behavior and to aid in detection of such algorithmic collusion. To some extent, AI collusion takes traditional ideas of collusion and simply provides a technological overlay to them. However, in some instances, the mechanisms of both collusion and detection can be transformed using AI. This handbook chapter discusses existing theoretical and empirical work, and identifies research gaps as well as avenues for new scholarship on how firms or competition authorities might invest in AI compliance to improve detection of wrong doing. We suggest where AI collusion is possible and offer new twists to where prior work has not identified possible collusion. Specifically, we identify the importance of AI to address the “trust” issue in collusion. We also identify that AI collusion is possible across non-price dimensions, such as manipulated product reviews and ratings, and discuss potential screens involving co-movements of prices and ratings. We further emphasize that AI may encourage entry, which may limit collusive prospects. Finally, we discuss how AI can be used to help with compliance both at the firm level and by competition authorities.