Multi-sided platforms and innovation: A competition law perspective
Bibliographic record
Abstract
We propose a simple theoretical model emphasizing the importance of a multi-sided platform (MSP) in fostering innovation. This model aims to assess the effects of structuring industries around an MSP on innovation dynamics and emphasizes the impact of cross-platform competition. It teaches us how to encourage cross-platform competition in terms of competition policy. The outcome is threefold. To begin, the presence of an MSP is critical for market innovation. Second, our findings indicate that skewed market power in favor of the MSP may stifle innovation in this industry, even if the negative impact on the industry’s rate of innovation is not immediately apparent. Finally, we demonstrate that industries with multiple MSPs have a higher rate of innovation. The model’s conclusions emphasize the critical importance of preserving the contestability of digital markets through competition rules enforcement. Even if the inherent technical characteristics of this industry result in a situation of dominance, competition rules should aim to preserve the possibility of market competition through, among other things, interoperability requirements, data portability requirements, and control of exclusivity clauses.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".