This chapter provides a comprehensive overview of the integration between Artificial Intelligence (AI) and Computational Antitrust within the broader context of the digital economy. It begins by elucidating the definition, scope, and historical development of AI, emphasizing its interdisciplinary nature and its transformative impact on social and economic systems. As a pivotal enabler of digital transformation, AI enhances automation, decision-making, and efficiency across domains such as healthcare, education, finance, and intelligent manufacturing, while serving as a driving force for high-quality economic growth and governance modernization. Building upon this foundation, the chapter introduces the economic concept of data elements as a new production factor, examining their role in fostering emerging productivity and reshaping industrial structures. Through the integration of big data technologies, data elements enhance knowledge creation, resource allocation, and innovation across industries, thereby forming the material and theoretical basis for computational governance. The core of the chapter focuses on Computational Antitrust—a data-driven, technology-enhanced framework for regulating monopolistic behaviors in the digital economy. It defines computational antitrust as the application of AI algorithms, machine learning, and multimodal data analytics to identify and assess monopolistic conduct in real time. The chapter presents a systematic computational antitrust framework featuring human-in-the-loop interactive learning, multimodal feature extraction, and causal relationship modeling. The construction of digital scenarios— exemplified through a detailed analysis of the ride-hailing industry—demonstrates how structured and unstructured data, represented through knowledge and event graphs, can simulate complex business ecosystems and detect anticompetitive risks dynamically. Finally, the chapter explores the necessity of international cooperation in computational antitrust, highlighting collaborative mechanisms, information sharing, and cross-border enforcement as essential pathways to ensuring fairness and transparency in the global digital marketplace. Collectively, these discussions lay the groundwork for a comprehensive, intelligent, and globally coordinated approach to digital economy governance—one that integrates AI technologies, data-driven insights, and international collaboration to foster a fair, competitive, and sustainable economic environment.

Artificial Intelligence and Computational Antitrust

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2026年

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Wei Liu

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This chapter provides a comprehensive overview of the integration between Artificial Intelligence (AI) and Computational Antitrust within the broader context of the digital economy. It begins by elucidating the definition, scope, and historical development of AI, emphasizing its interdisciplinary nature and its transformative impact on social and economic systems. As a pivotal enabler of digital transformation, AI enhances automation, decision-making, and efficiency across domains such as healthcare, education, finance, and intelligent manufacturing, while serving as a driving force for high-quality economic growth and governance modernization. Building upon this foundation, the chapter introduces the economic concept of data elements as a new production factor, examining their role in fostering emerging productivity and reshaping industrial structures. Through the integration of big data technologies, data elements enhance knowledge creation, resource allocation, and innovation across industries, thereby forming the material and theoretical basis for computational governance. The core of the chapter focuses on Computational Antitrust—a data-driven, technology-enhanced framework for regulating monopolistic behaviors in the digital economy. It defines computational antitrust as the application of AI algorithms, machine learning, and multimodal data analytics to identify and assess monopolistic conduct in real time. The chapter presents a systematic computational antitrust framework featuring human-in-the-loop interactive learning, multimodal feature extraction, and causal relationship modeling. The construction of digital scenarios— exemplified through a detailed analysis of the ride-hailing industry—demonstrates how structured and unstructured data, represented through knowledge and event graphs, can simulate complex business ecosystems and detect anticompetitive risks dynamically. Finally, the chapter explores the necessity of international cooperation in computational antitrust, highlighting collaborative mechanisms, information sharing, and cross-border enforcement as essential pathways to ensuring fairness and transparency in the global digital marketplace. Collectively, these discussions lay the groundwork for a comprehensive, intelligent, and globally coordinated approach to digital economy governance—one that integrates AI technologies, data-driven insights, and international collaboration to foster a fair, competitive, and sustainable economic environment.