Does It Pay to Increase Competition in Combinatorial Conservation Auctions?
Bibliographic record
Abstract
Conservation auctions allow landholders to propose conservation projects and associated payments (bids) for consideration by a conservation agency. Recently, the application of iterative combinatorial auction designs has been proposed to improve outcomes of conservation auctions. In combinatorial auctions, landholders are allowed to offer projects each of which involves activities aimed at providing one or multiple services. An iterative format allows bidders the opportunity to gradually explore the type of projects they want to offer, with this process being facilitated through price feedback provided based on intermediate auction round results. Auction designs vary with the type of feedback and respond differently to market conditions. At present there is a lack of information about their performance in markets with varying degrees of competition (in terms of number of bidders and level of target). Therefore, using an agent‐based simulation model, we evaluate a number of iterative auction designs. We observe that a higher degree of competition leads to a higher auction efficiency. In a high competition environment, efficiency outcomes tend to be less sensitive to auction design choices. Therefore, an auctioneer could enjoy freedom in design choice if adequate competition could be ensured. In weak competition environments, however, some auction designs perform better than others.
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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 | 0.011 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".