Unbiased criteria identification for two-sided matching: An environment-based design approach
Bibliographic record
Abstract
Two-sided matching problems arise in various domains, such as school admissions, organ donation, and online dating, where elements from two distinct sets are matched based on the set of preferences. Efficient matching algorithms play a crucial role in corresponding market design, aiming to optimize match success. This paper emphasizes the importance of extracting genuine preferences for agents in two-sided matching problems. Traditional methods for identifying criteria face challenges related to sample representativeness, response bias, and inflexibility, highlighting the need for an unbiased approach that minimizes mismatches and turnover rates. This research demonstrates the effective derivation of criteria from the natural language description of the matching problem in an unbiased manner. We propose an unbiased criteria identification methodology for two-sided matching problems based on Environment-Based Design (EBD), Recursive Object Model (ROM), and Environment-based Life Cycle Analysis (eLCA) to uncover relevant, consistent, and transparent criteria for matching. The proposed methodology fosters efficiency, satisfaction, and optimal resource allocation. By bridging the existing gap in the literature, the proposed methodology offers a comprehensive and impartial approach to improving the quality of matches in two-sided matching problems. Furthermore, this work not only addresses the critical need for unbiased criteria identification in two-sided matching markets but also introduces a novel approach to modeling multi-criteria decision-making problems in operations research, where criteria are traditionally determined through expert opinion or surveys. The applicability of the proposed methodology is demonstrated in the context of job matching, aiming to discover inclusive evaluation criteria that lead to stable, and mutually satisfactory job-candidate matches. Successful matches foster a harmonious and productive work environment, benefiting individuals and organizations.
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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".