Is Competition Policy Fit for the Digital Economy? A European Perspective
Bibliographic record
Abstract
Competition policy establishes the institutional framework for competitive dynamics in market economies. Recently, the relevance and impact of traditional competition policy has been challenged by the rise of the digital economy, where we see a small number of large platform firms, frequent takeovers and mergers, and the potential for using customer data to join and dominate previously separate markets. We provide a framework to explain the basis for contemporary competition policy, and explore implications for company strategy within and beyond the digital sector. Some of the most radical thinking about how competition policy might address the challenge of the digital economy originates from Europe, itself a major market for technology firms. We illustrate this thinking with exemplars from the practice of the EU Commission. Although existing competition policy can provide a basis for addressing monopolistic abuses in digital markets, practices are shifting to address novel sources of market power, including the governance architecture of digital platform firms and their ecosystems, the transferability of personal data, and the interoperability of systems and standards. We consider implications for policymakers. Corporate strategists must also understand how the evolving competition policy framework is impacting competitive dynamics of both platform operator and platform complementing entrepreneurs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".