Bibliographic record
Abstract
In this paper, we introduce private information into a market with search frictions and evaluate the relative efficiency of two pricing mechanisms, price posting and bargaining. Each seller chooses investment that determines the quality of the good. This quality is the seller's private information before matching and it will be observed in a match. Sellers enter a search market competitively and can choose either to post prices or to bargain. In this environment, a pricing mechanism affects efficiency through the choice of quality and the number of trades. Bargaining induces the efficient choice of quality but an inefficient number of trades because the division of the match surplus is generically inefficient. By directing buyers' search, posted prices internalize search externalities and induce the constrained efficient outcome in the case of public information. However, when the quality is private information, this role of posted prices in directing search can conflict with their role in signaling quality. Focusing on this conflict, we find that bargaining could yield higher efficiency than price posting. We characterize the parameter regions in which each of the two mechanisms dominates in efficiency.
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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 | 0.013 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".