Bibliographic record
Abstract
This paper deals with volume of trade and distribution of surplus in markets subject to adverse selection. The benchmark case -- a variation of Akerlof's lemons model -- is that of a market where two qualities of a good are offered, in proportions such that, if a single price is required to clear the market, only the low-quality units of the good are traded. I show that if trade is decentralized, i.e. allowed to take place at different prices simultaneously in different parts of the market (via random pairwise meetings of agents), then all units of the good are traded, and all agents have positive ex-ante expected payoffs. This fundamental difference with the centralized benchmark does not diminish as discounting is gradually removed from the decentralized framework. The result holds for both the steady-state and non-steady-state versions of the model. Cet article traite du volume d'échange et de la distribution des gains dans les marchés sujets à la sélection adverse. Le point de repère est une variante du modèle d'Akerlof (1970) dans laquelle deux qualités différentes d'un bien sont disponibles sur le marché mais une seule, la moindre, n'est vendue à l'équilibre. Je démontre que si le mécanisme d'échange est décentralisé, c'est-à-dire que les échanges peuvent s'effectuer à différents prix dans différentes parties du marché (via l'appariement aléatoire des agents), alors toutes les unités du bien seront vendues à l'équilibre, peu importe leur qualité. De plus, tous les agents ont un paiement anticipé positif au départ. Ces différences fondamentales avec les résultats d'Akerlof ne s'effacent pas lorsque l'escomptage des paiements dans le marché décentralisé est graduellement éliminé. Ce résultat est obtenu dans deux versions du modèle décentralisé: une avec états stationnaires, l'autre sans.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".