Big data and competition policy/ (Registro n. 3414)

006 - Campo Fixo - Material Adicional
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007 - Campo Fixo - Descrição Física
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008 - Campo de Tamanho Fixo
Campo fixo de controle local 210720b2016 bl ||||g |||| 00| 0 eng u
020 ## - ISBN
ISBN 9780198788133
040 ## - Fonte da Catalogação
Fonte de catalogação BR-BrCADE
082 ## - CDD
-- 341.3787 S932b
090 ## - Número de Chamada
Localização na estante 341.3787 S932b
Cutter S932b
100 1# - Autor
Autor STUCKE, Maurice E.
245 ## - Titulo Principal
Título principal Big data and competition policy/
260 ## - Local, Editora e Data
Cidade Nova York:
Editora Oxford University Press,
Data 2016.
300 ## - Descrição Física
Número de páginas 371 p.
505 ## - Conteúdo
Conteúdo CONTENTS<br/><br/>Abbreviations <br/>Tabk of Cases<br/>Table of Legislation<br/><br/>1. Introduction<br/>A. Myth 1: Privacy Laws Serve Different Goals from Competition Law<br/>B. Myth 2: lhe Tools that Competition Officials Currently Use FullyAddress Ali the Big Data Issues <br/>C. Myth 3: Market Forces Currently Solve Privacylssues<br/>D. Myth 4: Data-Driven Online Industries Are Not Sub ject to Network Effects <br/>E. Myth 5: Data-Driven Oriline Markets Have Low Entry Barriers <br/>F. Myth 6: Data Has Little, IfAny, Competitive Sigriificance, Since Data is Ubiquitous, Low Cost, and WidelyAvailable <br/>G. Myth 7: Data Has Little, IfAny, Competitive Significance, as Dominant Firms Cannot Exclude Smaller Companies' Access to Key Data or Use Data to Gain a Competitive Advantage <br/>H. Myth 8: Competition Officials Should Not Concern Themselves with Data-Driven Industries because Competition Always Comes from Surprising Sources<br/>I. Myth 9: Competition Officials Should Not Concern Themselves with Data-Driven Industries Because Consumers Generaily Benefit from Free Goods and Services <br/>J. Myth 10: Consumers Who Use these Free Goods and Services Do Not Have Any Reasonable Expectation of Privacy <br/><br/>I THE GROWING DATA-DRIVEN ECONOMY<br/><br/>2. Defining Big Data<br/>A. Volume of Data<br/>B. Velocity of Data<br/>C. Variety of Data <br/>D. Va!ue of Data <br/>3. Smartphones as an Example of How Big Data and Privacy Intersect<br/>A. Why the Odds Favoured the Government in Riley <br/>B. The Surprising Unanimous Decision <br/>C. Reflections<br/>4. lhe Competitive Significance of Big Data <br/>A. Six ilhemes from the Business Literature Regarding the Strategic Implications of Big Data <br/>B. Responding to Claims of Big Data's Insignificance for Competition Policy <br/>C. If Data is Non-Excludable, Why are Firms Seeking to Preclude Third Parties from Getting Access to Data? <br/>D. lhe Twitter Firehose <br/>E. The Elusive Metaphor for Big Data<br/>5. Why Haven't Market Forces Addressed Consumers' Privacy Concerns?<br/>A. Market Forces Are Not Promoring Services that Afford Great Privacy Protections <br/>B. Why Hasn't the Market Responded to the Privacy Concerns Of So Many Individuais?<br/>C. Are Individuais Concerned About Privacy? <br/>D. lhe Problem with the Revealed Preference Theory <br/>E. lhe Lack ofViable Privacy-Protecting Alternatives<br/><br/>II THE COMPETITION AUTHORITIES' MIXED RECORD IN RECOGNIZING DATAS IMPORTANCE AND THE IMPLICATIONS OF A FEW FIRMS' UNPARALLELED SYSTEM OF HARVESTING AND MONETIZING THEIR DATA TROVE<br/><br/>6. lhe US's and EU's Mixed Record in Assessing Data-Driven Mergers<br/>A. lhe European Com mission's 2008 Decision Nor to Challenge the TomTom/Tele Atlas Merger <br/>B. Facebook/WhatsApp <br/>C. FTC's 'EarlyTermination' of lts Review of theAlliance Data Systems Corp/Conversant Merger <br/>D. Google/Nest Labs and Google/Dropcam <br/>E. Google/Waze<br/>F. The DOJ's 2014 Win against Bazaarvoice/PowerReviews<br/>G. Synopsis of Merger Cases<br/><br/>III WHY HAVEN'T MANY COMPETITION AUTHORITIES CONSIDERED THE IMPLICATIONS OF BIG DATA? <br/><br/>7. Agencies Focus on What Is Measurable (Price), Which Is Not Always Important (Free Goods) <br/>A. The Push Towards Price-Centric Antitrust<br/>B. What the Price-Centric Approach Misses <br/>C. The Elusiveness of Assessing a Merger's Effect on Quality Competition <br/>D. Why Qualiry Competition is Paramount in Many Data-Driven Multi-Sided Markets <br/>E. Challenges in Conducting an SSNDQ on Privacy <br/>F. Using SSNIP for Free Services <br/>G. How a Price-Centric Analysis Can Yield the Wrong Conclusion <br/>H. Reflections <br/>8. Data-Driven Mergers Often Fali Outside Competition Policy's Conventional Categories<br/>A. Categorization ofMergers <br/>B. Belief that Similar Products/Services Compete More Fiercely than Dissimilar Products/Services <br/>C. Substirutability of Data <br/>D. Defining a New Category <br/>9. Belief that PrivacyConcerns Differ from Competition Poilcy Objectives<br/>A. Defining Privacy in a Data-Driven Econorny <br/>B. Whether and When ]Ihere Is a Need to Show Harrn,and IfSo, What Type oU Harm <br/>C. How Shouid the Competition Agencies and Courts Balance the Privacy Interests with Other Interests? <br/>D. Courts' Acceptance ofPrevailing Defaults, in Lieu ofBalancing <br/>E. Setring the Default in Competition Cases <br/>F. Conclusion <br/><br/>IV WHAT ARE THE RISKS IF COMPETITION AUTHORITIES IGNORE OR DOWNPLAY BIG DATA?<br/><br/>10. Importance of Entry Barriers in Antitrust Analysis<br/>A. Entry Barriers in Data-Driven Markets <br/>B. Looking Beyond Traditional Entry Barriers <br/>11.EntryBarriers Can Be Higher in Multi-Sided Markets, Where One Side Exhibits Traditional Network Effects<br/>A. Traditional Network Effects in Facebook/WhatsApp<br/>B. The Commission's Reasoning Why the Merger Was Urilikely to Tip the Market to Facebook <br/>C. Strengths and Weaknesses of the Commission's Analysis of Network Effects<br/>12. Scale of Data: Trial-and-Error, 'Learning-by-Doing' Network Effects<br/>A. Waze's Turn-by-Turn Navigarion App <br/>B. Search Engines <br/>C. Facebook <br/>D. Reflections <br/>13. Two More Network Effects: Scope of Data and Spill-Over Effects<br/>A. Scope of Data <br/>B. Spill-Over Effects: How Networks Effects on One Side of Multi-Sided Platforms Can Increase Market Power on the Other Sides<br/>14. Reflections on Data-Driven Network Effects<br/>A.Ten Implications of Data-Driven Network Effects<br/>B. Why Coritrolling the Operating Sysrem Gives the Platform a Comperitive Advantage Over an Independent App <br/>C. Independent App Developers' Dependence on Google and Apple<br/>D. How Google Benefits from Tbese Network Effects<br/>E. Domination is not Guaranteed<br/>15. Risk of lnadequate Merger Enforcement<br/>A. The Prediction Business <br/>B. Most Mergers are Cleared <br/>C. The Big Mystery: How Often Do the Competition Agencies Accurately Predict the Mergers' Competitive Effects? <br/>D. The Ex-PostMerger Reviews Paint a B!eak Picture<br/>E. The High Error Costs When the Agencies Examine Only One Side of a Multi-Sided P!atform <br/>F. How Data-Driven Mergers Increase the Risks ofFalse Negatives<br/>16. The Price ofWeakAntitrust Enforcement<br/>A.'lhe Chicago School's Fear of False Positives <br/>B. The Unired States as a Test Case of WeakAntitrust Enforcement <br/>C. Costs of Weak Antitrust Enforcement in the Agricultural Industry <br/>D. Costs of Weak Antitrust Enforcement in the Financial Sector<br/>E. Consumers' Overall Welfare<br/>F. Why Igrioring Big Data Wi!! Compound the Harm<br/>G. The Competition Agencies Cannot Assume that Other Agencies wil! Repair Their Mistakes<br/><br/>V ADVANCING A RESEARCH AGENDA FOR THE AGENCIES AND ACADEMICS<br/><br/>17. Recognizing When Privacy and Competition Law Intersect <br/>A. Promoting Consumers' Privacy Inrerests Can Be an Important Part of Quality Competition<br/>B. Some Simp!e Examples Where Privacy and Competition Law lntersecr <br/>C. Looking Beyond Privacy's Sub jectivity <br/>D. Deve!oping Berter Economic Too!s to Address Privacy<br/>E. Why Competition Po!icy Does Not Have an Efficiency Screen <br/>F. Using a Consumer Wel!-Being Screen <br/>G. Media Mergers as an F.xamp!e of a Consumer WeIl-Being Screen<br/>H. Conclusion<br/>18. Data-opoiy: Identifying Data-Driven Exclusionary and Predatory Conduct <br/>A. In False Praise ofMoriopolies <br/>B. Debunking the Myth that Competition Law is Ili-Suited for New Industries <br/>C. How the 'Waiting for Dynamic Competition' Argument Ignores Path Dependencies<br/>D. How (Even Failed) Antitrust Enforcement Can Open Competitive Portais <br/>E. The Nowcasting Radar—Why Some Data-opolies are More Dangerous than Microsoft in the 1990s<br/>F. Keeping the Cotnpetitive Portais Open <br/>G. An Object Ali Sublime, the Competition Authority Shail Achieve in Time—to Ler the Punishment Fit the Crime <br/>19. Understanding and Assessing Data-Driven Efficiencies Claims<br/>A. Efficiencies Benefit Consumers <br/>B. Efficiencies Must Be Merger-Specific <br/>C. Efficiencies Must Be Verifiable <br/>D. Balancing Efflciency and Privacy <br/>E. Challenges Ahead <br/>20. Need for Retrospectives ofData-Driven Mergers<br/>A. Waiting for the Right Data-Driven Merger<br/>B. Debiasing Through Ex-Post Merger Reviews <br/>C. FTC's Retrospectives of Hospital Mergers <br/>D. The Benefits in Conducting Merger Retrospectives<br/>21. More Coordination among Competition, Privacy, and Consumer Protection Officials<br/>A. Moving Bcyond Notice-and-Consent<br/>B. Several Preconditions to Spur Privacy Competition <br/>22. Conclusion<br/>Index<br/><br/><br/>
650 #0 - ASSUNTO
9 (RLIN) 2389
Assunto Big data
650 #0 - ASSUNTO
9 (RLIN) 644
Assunto Defesa da concorrência
700 1# - Entrada secundária - Nome Pessoal
9 (RLIN) 2386
Nome pessoa GRUNES, Allen P.
Relação Autor
942 ## - Elementos de Entrada Adicionados
Tipo de Material Livros
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