Strategy-proof tie-breaking in matching with priorities
Bibliographic record
Abstract
A set of indivisible objects is allocated among agents with strict preferences. Each object has a weak priority ranking of the agents. A collection of priority rankings, a priority structure, is solvable if there is a strategy-proof mechanism that is constrained efficient, i.e., that always produces a stable matching that is not Pareto-dominated by another stable matching. We characterize all solvable priority structures satisfying the following two restrictions: Either there are no ties or there is at least one four-way tie.For any two agents i and j, if there is an object that assigns higher priority to i than to j, there is also an object that assigns higher priority to j than to i.(A)(B) We show that there are at most three types of solvable priority structures: The strict type, the house allocation with existing tenants (HET) type, where, for each object, there is at most one agent who has strictly higher priority than another agent, and the task allocation with unqualified agents (TAU) type, where, for each object, there is at most one agent who has strictly lower priority than another agent. Out of these three, only HET priority structures are shown to admit a strongly group-strategy-proof and constrained efficient mechanism.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".