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This is an Open access book which provides a comprehensive framework for identifying monopolistic behaviors in the digital economy, with a focus on discriminatory pricing as one manifestation of these practices. As digital platforms increasingly dominate markets and collect unprecedented volumes of user data, pricing strategies tailored to user profiles often resulting in discriminatory pricing raise major concerns about consumer rights, market fairness, and competition. Differential pricing driven by big data is widespread in sectors like e-commerce, travel, and ride-hailing; however, when adopted by dominant enterprises, it risks evolving into monopolistic practices that challenge existing legal frameworks and consumer protections. On the algorithmic level, this book tackles these challenges by developing an innovative, machine-learning-based approach for real-time detection of discriminatory pricing and related monopolistic behaviors. Recognizing that traditional regulatory oversight heavily relies on consumer complaints and is often retrospective, we propose an advanced Dual Pricing Model Clustering (DPMC) framework, which proactively distinguishes between discriminatory and non-discriminatory pricing using real-world data patterns. Initially, the book focuses on the online ride-hailing industry, where dynamic pricing is common and has attracted widespread public attention. It offers practical insights and a robust, transferable framework applicable to other sectors facing similar issues. From the perspective of antitrust business needs, we have also developed an intelligent antitrust system. Beyond its statistical analysis capabilities, the book explores the application of large models in the antitrust field, proposing a "Computational Antitrust Large Model." This model integrates large language models with monopolistic behavior identification models, combining insights from public sentiment and other intelligence sources to assist regulators in proactively detecting monopolistic behavior clues. The book is designed for professionals and scholars in antitrust regulation, digital economy governance, and data science, aiming to equip them with the knowledge and tools needed to address monopolistic and discriminatory practices in the platform economy.
Sommario
Chapter 1: The Global History and Development of Antitrust.- Chapter 2: Antitrust in the Digital Economy Era.- Chapter 3: Artificial Intelligence and Computational Antitrust.- Chapter 4: Identifying Price Discrimination in the Digital Economy.- Chapter 5: Risk Assessment and Early Warning System for Monopolistic Behaviors.
Info autore
Wei Liu received his Ph.D. from the School of Artificial Intelligence at Beijing Normal University in 2020. From April 2020 to March 2022, he conducted postdoctoral research at the Institute of Automation, Chinese Academy of Sciences. He is currently an Associate Professor at the Institute of Automation, Chinese Academy of Sciences. His primary research interests focus on the interdisciplinary applications of machine learning, including intelligent operation and maintenance of railway transportation systems, astrophysical big data processing, digital economy governance, and neuromorphic computing. Weiming Hu received the Ph.D. degree from the department of computer science and engineering, Zhejiang University in 1998. From April 1998 to March 2000, he was a postdoctoral research fellow with the Institute of Computer Science and Technology, Peking University. Now he is a professor in the Institute of Automation, Chinese Academy of Sciences. His research interests are in visual motion analysis, recognition of web objectionable information, and network intrusion detection. Li Yang received the Ph.D. degree from the Institute of Automation, Chinese Academy of Sciences, Beijing, China, in 2023. He is currently an assistant professor at the Institute of Automation, Chinese Academy of Sciences. His research interests include computer vision and multi-modal learning, with a particular focus on object detection involving multi-modal and open semantic understanding. Bing Li received the PhD degree from the Department of Computer Science and Engineering, Beijing Jiaotong University, in 2009. From 2009 to 2011, he worked as a postdoctoral research fellow with the National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CASIA). He is currently a professor with CASIA. His current research interests include computer vision, color constancy, visual saliency detection, multi-instance learning, and data mining. Jiying Wu holds a Ph.D. in Signal and Information Processing and has completed postdoctoral research in the Department of Electronic and Computer Engineering at the Hong Kong University of Science and Technology. She is currently a Senior Engineer and a recipient of the National Youth Talent Award from the State Administration for Market Regulation. She is now serving at the Competition Policy and Evaluation Center of the State Administration for Market Regulation, where she focuses on competition assessment, AI-based data analysis, and technical support for antitrust investigations. She possesses a strong technical background and extensive industry experience. Qing Gao, Ph.D. in Civil and Commercial Law, Postdoctoral Researcher in Economic Law，is currently serving as an Associate Professor at the School of Juris Master in China University of Political Science and Law, and as the doctoral supervisor of the CUPL Data Law Lab. His major research interests lie in Competition Law and Data Law. Ning Du received the Ph.D. degree from the University of International Business and Economics, Beijing, China, in 2017. He is currently an assistant professor at the East China University of Political Science and Law. His research interests include Antitrust Law, Intersection of Competition Law, Intellectual Property and AI.
Riassunto
This is an Open access book which provides a comprehensive framework for identifying monopolistic behaviors in the digital economy, with a focus on discriminatory pricing as one manifestation of these practices. As digital platforms increasingly dominate markets and collect unprecedented volumes of user data, pricing strategies tailored to user profiles—often resulting in discriminatory pricing—raise major concerns about consumer rights, market fairness, and competition. Differential pricing driven by big data is widespread in sectors like e-commerce, travel, and ride-hailing; however, when adopted by dominant enterprises, it risks evolving into monopolistic practices that challenge existing legal frameworks and consumer protections. On the algorithmic level, this book tackles these challenges by developing an innovative, machine-learning-based approach for real-time detection of discriminatory pricing and related monopolistic behaviors. Recognizing that traditional regulatory oversight heavily relies on consumer complaints and is often retrospective, we propose an advanced Dual Pricing Model Clustering (DPMC) framework, which proactively distinguishes between discriminatory and non-discriminatory pricing using real-world data patterns. Initially, the book focuses on the online ride-hailing industry, where dynamic pricing is common and has attracted widespread public attention. It offers practical insights and a robust, transferable framework applicable to other sectors facing similar issues. From the perspective of antitrust business needs, we have also developed an intelligent antitrust system. Beyond its statistical analysis capabilities, the book explores the application of large models in the antitrust field, proposing a "Computational Antitrust Large Model." This model integrates large language models with monopolistic behavior identification models, combining insights from public sentiment and other intelligence sources to assist regulators in proactively detecting monopolistic behavior clues. The book is designed for professionals and scholars in antitrust regulation, digital economy governance, and data science, aiming to equip them with the knowledge and tools needed to address monopolistic and discriminatory practices in the platform economy.
