Digital platforms have outgrown nineteenth-century antitrust: data-driven market power, network effects and cross-border ecosystems require dynamic market tests, stronger international cooperation and algorithmic audit tools to keep markets contestable.

Main Finding

The chapter surveys the global history and institutions of antitrust and argues that the digital economy—characterized by data-driven market power, multisided network effects, ecosystems, platform-mediated commerce, and cross-border integration—strains traditional antitrust tools. To preserve contestability and consumer welfare regulators must modernize: clarify treatment of data monopolies, adopt dynamic (not solely static-share) market assessment, strengthen international coordination, and deploy “computational antitrust” (data analytics, algorithmic audits, and simulation) to enable timely, evidence-based enforcement.

Key Points

• Definitions & mechanisms

  • Monopoly arises from scale, proprietary technology, administrative/governmental protection, natural monopoly, or strategic conduct; effects include price/quality distortions and dampened innovation, though scale can enable R&D benefits.
  • Antitrust aims to curb abuse of dominance while balancing efficiency and innovation incentives.

• Foundational principles

  • Focus on anticompetitive effects (collusive vs. exclusionary conduct); EU also recognizes exploitative abuse (e.g., excessive pricing).
  • Need to integrate international norms with domestic market realities; combine substantive rules with procedural fairness.
  • Some jurisdictions (notably China) emphasize market integration and a balance between market freedom and state oversight.

• Historical and regional evolution

  • US: Sherman Act (1890) inaugurated modern antitrust; later Clayton, FTC, evolving doctrine from structural to effects and economic-analysis approaches.
  • EU: post-war centralization through the EEC/Treaty framework (TFEU Articles 101/102), block exemptions, and vigorous enforcement against tech firms.
  • Asia: Japan’s Antimonopoly Law (post‑1947) and China’s Anti‑Monopoly Law (2008) with subsequent refinements (notably 2022 Article 19 and specialized guidelines on SEPs and platforms).

• International cooperation

  • Antitrust cooperation occurs bilaterally (MOUs, BCAs), regionally (EU frameworks), and multilaterally (WTO/competition policy dialogues).
  • Cross-border cases (e.g., Intel, Microsoft, major platform investigations) demonstrate need for information sharing, coordinated remedies, and joint approaches.

• Digital-era challenges and recurring issues

  • Data-driven market power, multi-sided networks, bundling/self-preferencing, exclusionary contracts, platform gatekeeping, privacy–competition interactions, SEPs/IP tensions.
  • Traditional market-definition and market-share metrics often insufficient in fast-moving, multi-sided platform markets.

• Modernisation agenda

  • Clearer legal treatment of data monopolies and of algorithmic conduct.
  • Dynamic market assessments (entry potential, innovation pathways).
  • Enhanced cross-border and cross-sector coordination.
  • Adoption of computational antitrust tools: algorithmic audits, large-scale data analytics, simulation and counterfactual modelling to support enforcement.

Data & Methods

• Nature of the chapter

  • Policy-oriented, comparative legal and historical survey rather than an original empirical econometric study.
  • Methods include doctrinal/legal analysis, comparative history across jurisdictions (US, EU, Asia), and illustrative case studies.

• Sources and evidence

  • Primary legal instruments: Sherman Act (1890), Clayton Act, TFEU provisions, Council Reg. No. 19/65, national statutes (e.g., China’s AML and 2022 amendment), administrative regulations and guidelines (e.g., China’s SEP guidelines).
  • Representative enforcement cases cited: Microsoft, Intel, major EU investigations into Google/Apple/platform conduct, and Asian e‑commerce/messaging matters.
  • Institutional analysis of bilateral, regional and multilateral cooperation frameworks (e.g., competition policy frameworks under WTO dialogue, BCAs/MOUs).

• Proposed computational methods (as recommended, not empirically implemented in the chapter)

  • Algorithmic audits, large-scale data analytics, anomaly detection for algorithmic collusion, simulation/agent-based modelling for dynamic counterfactuals.
  • Emphasis on needing access to granular platform data, trace logs, and cross-border data-sharing protocols.

• Limitations

  • Qualitative/legal survey: limited primary empirical estimation or formal economic modelling in the chapter itself.
  • Effectiveness of recommended computational tools depends on data access, technical capacity of agencies, legal constraints (e.g., privacy), and international data‑sharing arrangements.

Implications for AI Economics

• Measurement and identification

  • New metrics are needed to quantify “data advantage” and its persistence (e.g., marginal value of additional data, diminishing returns, barriers to replication).
  • Economists should develop methods to estimate market power in multisided/platform markets where user counts, engagement, and cross‑side externalities matter more than unit market shares.

• Algorithmic conduct and tacit collusion

  • AI economists can design detection algorithms and statistical tests for algorithmic coordination/tacit collusion using transaction-level logs, price/offer trajectories, and model-based counterfactuals.
  • Work is needed on distinguishing efficient algorithmic pricing from anticompetitive algorithmic behaviour (causal inference in presence of learning agents).

• Dynamic counterfactuals & innovation effects

  • Build dynamic structural models and agent‑based simulations to evaluate merger effects, foreclosure, and long-run innovation impacts in platform ecosystems.
  • Design counterfactual scenarios that incorporate network effects, multi-sided pricing, and platform entry/exit dynamics.

• Remedies and market design

  • Evaluate design and welfare effects of behavioral remedies (interoperability, non-discrimination rules), access remedies (data portability, APIs), and structural remedies in tech markets.
  • Quantify unintended consequences (privacy tradeoffs, security risks, enforcement avoidance).

• Data access, audits, and governance

  • Develop standards and protocols for algorithmic audits that preserve privacy (e.g., secure multiparty computation, differential privacy) while enabling competition analysis.
  • Push for regulatory sandboxes and data‑sharing agreements that give trusted researchers and agencies needed microdata.

• International coordination

  • Harmonize empirical standards and evidence formats across jurisdictions to support coordinated enforcement; economists can help standardize metrics and testing frameworks.
  • Cross-border research collaborations can pool data to study global platform effects.

• Research agenda suggestions

  • Formalize measures of data-based entry barriers and learning advantages.
  • Create statistical methods to detect algorithm-driven exclusionary conduct (e.g., personalized steering, discriminatory ranking).
  • Simulate merger outcomes in two‑sided markets with endogenous data accumulation.
  • Empirically study the interaction between privacy regulation and competition outcomes (does strong privacy increase or decrease incumbents’ advantages?).

In short, the chapter calls for integrating economic and technical expertise into a modern antitrust toolkit. For AI economists this means prioritizing methods to measure data-driven power, detect and model algorithmic conduct, design and test remedies, and support cross-border, data-enabled enforcement while respecting privacy and due process.