Keep It Down: An Experimental Test of the Truncated<i>k</i>-Double Auction
Bibliographic record
Abstract
The introduction of a centralized institution for trading production rights in quota-regulated agricultural sectors can dramatically improve the flow of information among market participants and increase efficiency. On the other hand, prevailing conditions in these small markets can provide sellers with a market advantage, yielding high quota prices that impose important financial costs on quota holders and limit the entry of new producers into the industry. In this paper, we modify the normal allocation rule of thek-double auction (kDA) to counter thin market conditions and to favor buyers who bid low prices. In laboratory experiments, we test the “truncated” kDA (T-kDA) against a regular kDA for its ability to affect buyer and seller behavior and decrease equilibrium prices, and assess how it impacts efficiency. The results show that the T-kDA significantly lowers the equilibrium price and results in moderate efficiency losses. Most importantly, the T-kDA effectively counters the market power of oligopolists when demand far outstrips supply.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".