Transparency and Control in Platforms for Networked Markets
Bibliographic record
Abstract
Platforms: How Much and What Should They Control? Platforms exert control in many ways: from determining which buyers are shown to which sellers to directly controlling allocation of aggregate supply across different consumer markets. How should platforms exercise control to increase social welfare? In “Transparency and Control in Platforms for Networked Markets,” authors J. Pang, W. Lin, H. Fu, J. Kleeman, E. Bitar, and A. Wierman examine the trade-offs that emerge between efficiency loss, transparency, and control across different platform designs. They show that open access platforms incentivize increased production toward perfectly competitive levels and limit efficiency loss, whereas controlled allocation designs can lead to supply-side withholding, resulting in unbounded efficiency loss. Balancing transparency and control, discriminatory access designs strictly improve upon the worst-case efficiency loss of both open access and controlled allocation platforms. Besides providing insights into the design and operation of two-sided platforms, this paper contributes to the growing theory of Cournot competition in networked 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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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".