Algorithmic Approach of Majority Voting With Agents’ Inclusiveness for Facility Resource Matching
Bibliographic record
Abstract
In a two-sided matching with preferences, an agent has preferences over the agents from the other side. In a head-to-head election between any two matching, the agent votes for the matching with a better allocation in pairing based on its preference. A matchingMis popular when the number of votes forMis not less than the number of votes for any other matchingM′ in such an election. AU-popular matching is introduced recently with a two-sided matching model where U andWare the two sets of agents, but only the preferences of the agents fromUare emphasized, and the preferences of the agents fromWare ignored. The matching which is popular among the votes of the agents fromUis defined asU-popular. We consider such a model for a one-to-one matching where the agents fromUhave an inclusive mindset and want to integrate the voting decision of the agents fromWinto their voting process. In parallel, the agents fromWrely more on their ranking on the preference list of the agents fromUthan their own preferences due to the uncertainty involved in the construction of their preferences.We define an inclusive voting model with such a predominant-subordinate agent scenario (Uas predominant,Was subordinate) and prove that the Boston mechanism matching isU-popular under the model. However, it is possible that theU-popular matching is not the choice of the majority of agents. The choice to be neutral is added in the voting process, and theU-neutral matching type is introduced when the majority of agents vote for neutral. We characterize theU-neutral and max-sizeU-neutral matching under the inclusive voting model and propose a polynomial-time algorithm to determine a max-sizeU-neutral matching. The experiments we performed with synthetic instances endorse the algorithm based on the theoretical foundations established.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".