Digital Platforms and Antitrust
Bibliographic record
Abstract
Abstract At the heart of economic activity, digital platforms connect multi-sided markets of producers and consumers of various goods and services. Their market power and privileged ecosystem position raise concerns that they may engage in anti-competitive practices that reduce innovation and consumer welfare. This chapter deals with the role of market competition and regulation in addressing these concerns. Traditional (ex post) antitrust intervention will be less effective in markets driven by network effects unless it is combined with a proper (ex ante) regulatory framework. Antitrust tools should focus on value creation and its distribution before focusing on competition. The scope of regulatory intervention should satisfy three criteria: (1) value creation from operation of the platforms does not decrease due to the policy intervention; (2) allocative efficiency is based on distributing the value created in a fair way among market participants; (3) dynamic efficiency and competition ensure that incentives for market misconduct and anti-competitive strategies such as artificial entry barriers are eliminated. Market interventions that target a firm’s market power should ideally retain value creation while also encouraging small firm entry and innovation. Data has a central role in online markets. Value creation is reinforced through a recursive data capture and data deployment feedback loop enabled by machine learning technologies. A regulatory intervention that facilitates data sharing mechanisms, such that data will not only confer value to market leaders but also to their competitors to the benefit of consumers, is crucial for creating more competitive and innovative digital markets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".