Informed seller problem : signaling, information design, and mechanism design
Bibliographic record
Abstract
This thesis studies an informed seller problem in which the seller tries to signal her private information through di¤erent channels-information disclosure, selling mechanism and return policy.Chapter 1 analyzes the signalling e¤ect of information disclosure and price posting.Any separating equilibria must have the two types of seller setting di¤erent disclosure rules as well as di¤erent prices.Furthermore, the outcome that survives the intuitive criterion always exists and is unique.This equilibrium outcome is separating, for which a closed-form solution is provided.The signaling concern forces the high-type seller to disclose an ine¢ cient amount of information and charge a higher price, resulting in fewer sales and lower pro…t.A regulation on minimal quality could potentially damage social welfare.In chapter 2, the seller is allowed to design a grand mechanism in which she herself participates in addition to information disclosure.The RSW (Rothschild-Stiglitz-Wilson) mechanism is fully characterized, in which each type of seller separates at the lowest cost.In this mechanism, the low-type seller sells to the buyer with certainty and leaves zero surplus to the buyer.The high-type seller discloses to the buyer whether his value is above a cuto¤, sets a payment di¤erence equal to the conditional expected value, and provides a nonnegative bonus.Furthermore, the RSW mechanism can always be supported as a PBE and its outcome is the unique PBE outcome under certain conditions.Finally, the RSW mechanism always survives the Intuitive criterion, and is the unique one under certain conditions.Chapter 3 studies an second-price auction with return policies.It starts with binary type.In the separating equilibria, the high-type seller's return policy needs to be generous enough to deter the low-type seller from mimicking.Notably, a better return policy may not correspond to a better type.In the pooling equilibria, the return policy cannot be too generous.All separating equilibria have the same outcome and all survive Eso and Schummer's [24] credible deviation criterion while all pooling equilibria fail.Separation is costless and e¢ cient.Similar results apply when sellers have multiple types.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.004 |
| 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".