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 the k- 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".