Mandatory Disclosure, Generation of Decision‐Relevant Information, and Market Entry
Bibliographic record
Abstract
Abstract We investigate the interaction of mandatory disclosure and the gathering of decision‐relevant information in a setting in which a competitor may enter the market. Gathering detailed information allows for an efficient allocation of resources, but eventually attracts competition by revealing beneficial information to competitors. In contrast, refraining from generating detailed information implies inefficient decisions, but eventually prevents competitors from entering the market. Our results show that an incentive not to generate internal information arises for two reasons: If the incumbent's cost advantage is sufficiently large, disclosing aggregated information can be an instrument to avoid competition by reducing the likelihood of market entry. If the incumbent's cost advantage is small, disclosing aggregated information attracts competition by increasing the likelihood of market entry. In this case, imprecise cost information serves as a commitment device to reduce the intensity of competition by forcing the competitor to take into account his efficiency disadvantage in making his production decision.
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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 | 0.019 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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".