A Theory of Commerce: Competitive Search Under Private Information
Bibliographic record
Abstract
Retail trade absorbs vast scarce resources because the physical process of trading is time con-suming, buyers match with sellers without coordination, and consumers prefer to purchase a diverse basket of goods. Sellers post prices to attract customers, but buyers care also about the expected \ntime it takes to make a purchase. Retail prices can be non-linear due to packaging and quantity discounts. However, prices cannot depend on buyers preferences because these are private information. To capture these features, we adopt directed search and assume that sellers ignore their \nclients preferences. If, realistically, sellers cannot charge a ßat fee to all potential buyers, then \nin equilibrium the average lineup of buyers in front of a seller is inefficiently long. In contrast, the directed search equilibrium is efficient with full information. Our model is easily inserted in a \nNeoclassical growth framework. The retail trade sector can be calibrated using commercial margins \nand resources employed in that sector.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".