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024 7 _a10.1007/978-981-95-5037-1
_2doi
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082 0 4 _a303.483
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100 1 _aLiu, Wei.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_923913
245 1 0 _aComputational Antitrust
_h[electronic resource] /
_cby Wei Liu.
250 _a1st ed. 2026.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2026.
300 _aXX, 151 p. 19 illus., 14 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
341 0 _bPDF/UA-1
_2onix
341 0 _bTable of contents navigation
_2onix
341 0 _bSingle logical reading order
_2onix
341 0 _bShort alternative textual descriptions
_2onix
341 0 _bUse of color is not sole means of conveying information
_2onix
341 0 _bUse of high contrast between text and background color
_2onix
341 0 _bNext / Previous structural navigation
_2onix
341 0 _bAll non-decorative content supports reading without sight
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347 _atext file
_bPDF
_2rda
490 1 _aArtificial Intelligence and the Rule of Law,
_x2731-6246
505 0 _aChapter 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.
506 0 _aOpen Access
520 _aThis 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.
532 8 _aAccessibility summary: This PDF has been created in accordance with the PDF/UA-1 standard to enhance accessibility, including screen reader support, described non-text content (images, graphs), bookmarks for easy navigation, keyboard-friendly links and forms and searchable, selectable text. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. Please note that a more accessible version of this eBook is available as ePub.
532 8 _aNo reading system accessibility options actively disabled
532 8 _aPublisher contact for further accessibility information: accessibilitysupport@springernature.com
650 0 _aTechnology
_xSociological aspects.
650 0 _aArtificial intelligence.
650 0 _aInformation technology
_xLaw and legislation.
_9479
650 0 _aMass media
_xLaw and legislation.
_9480
650 0 _aLaw and economics.
650 1 4 _aScience, Technology and Society.
650 2 4 _aArtificial Intelligence.
650 2 4 _aIT Law, Media Law, Intellectual Property.
_9488
650 2 4 _aLaw and Economics.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789819550364
776 0 8 _iPrinted edition:
_z9789819550388
776 0 8 _iPrinted edition:
_z9789819550395
830 0 _aArtificial Intelligence and the Rule of Law,
_x2731-6246
_923914
856 4 0 _uhttps://doi.org/10.1007/978-981-95-5037-1
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