Bibliographic record
Abstract
A generalized matching problem consists of a set of agents, a set of objects, the agents’ endowments, a set of feasible matchings, and the agents’ preferences over feasible matchings. Respect for improvement means that when the ranking of an agent’s endowment improves in some other agent’s preference (while keeping other preferences unchanged), then this agent weakly benefits from it. Our main result shows across matching applications that on the strict domain, individual rationality, strategy-proofness, and nonbossiness imply respecting improvement. As a consequence for housing markets, we obtain that top trading with fixed tiebreaking and top trading with random tiebreaking satisfy respecting improvement on the weak domain. We further show that several application-based extensions of the top-trading-cycles mechanism (such as for kidney exchange and school choice) satisfy (a weak version of) respecting improvement. This paper was accepted by Martin Bichler, market design, platform, and demand analytics. Funding: The author acknowledges financial support from the Social Sciences and Humanities Research Council of Canada under Insight Grant 435-2023-0129 and the Fonds de recherche du Québec under Soutien aux équipes de recherche / Universitaire- nouvelle équipe 367853.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".