Two-Sided Matchings: An Algorithm for Ensuring They Are Minimax and Pareto-Optimal
Bibliographic record
Abstract
Gale and Shapley (1962) proposed the deferred-acceptance algorithm for matching (i) college applicants and colleges and (ii) men and women. In the case of the latter, it produces either one or two stable matches whereby no man and woman would prefer to be matched with each other rather than with their present partners. But stable matches can give one or both players in a pair their worst match, whereas the minimax algorithm that we propose, which finds all assignments that minimize the maximum rank of players in matches, avoids such assignments. Although minimax matches may not be stable, at least one is always Pareto-optimal: No other matching is at least as good for all the players and better for one or more. If there are multiple minimax matches, we propose criteria for choosing the most desirable among them and also discuss the settings in which minimax matches seem more compelling than deferred-acceptance matches when they differ. Finally, we calculate the probability that minimax matches differ from deferred-acceptance matches in a simple case.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".