Bibliographic record
Abstract
We consider a price discrimination problem in which a seller has a single object for sale to a potential buyer. At the time of contracting, the buyer’s private type is his incomplete private information about his value, and the seller can disclose additional private information to the buyer. We study the question of whether discriminatory infor-mation disclosure can be profitable to the seller under the assumption that, for the same disclosure policy, the amount of additional private information that the buyer can learn depends on his private type. In both discrete-type and continuous-type setting, we show that discriminatory disclosure can be optimal because, compared to full disclosure, it re-duces the information rent accrued to private types of the buyer without much impact on the trade surplus. A complete characterization of the optimal discriminatory disclosure policy is provided in the discrete-type setting. We also establish sufficient conditions for the optimality of full information disclosure in the continuous-type setting. ∗We are indebted to Dirk Bergemann for his support in various stages of this project. We thank the participants of 2013 Cowles Summer Conference in Economic Theory, especially our discussant Philipp Strack, and Simon Board, Alessandro Pavan, Roland Strauss and Balazs Szentes for many helpful comments and suggestions. Shi is grateful to Social Sciences and Humanities Research Council of Canada for financial support. 1
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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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".