Predatory pricing in platform markets: a modified test for firms within the scope of Article 3 of the DMA and super-dominant platform firms under Article 102 TFEU
Bibliographic record
Abstract
The paper examines predatory pricing in the context of two-sided digital platforms, arguing that traditional tests based on Average Variable Cost (AVC) may be inadequate for these markets. While predatory pricing by dominant firms is prohibited in both EU and US competition law, the current standards may not effectively capture predatory behavior in platform markets characterized by strong network effects and low marginal costs. The paper analyses cases where cross-subsidization between platform sides had predatory elements and resulted in findings of abuse of dominant position. Given platforms' unique characteristics, it proposes a modified test under Article 102 TFEU for super-dominant platforms and those within the scope of Article 3 of Digital Markets Act's scope. The proposal extends the Akzo test by presuming prices below Average Total Cost (ATC) to be abusive, rather than using AVC, with LRAIC as a proxy for ATC. This addresses the current test's limitations for low marginal cost businesses while allowing for objective justification.
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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 | 0.010 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".