Normative Properties for Object Allocation Problems: Characterizations and Trade-Offs
Bibliographic record
Abstract
We consider the allocation of indivisible objects among agents when monetary transfers are not allowed. Agents have strict preferences over the objects (possibly about not getting any object) and are assigned at most one object. How should one allocate offices to faculty members at a university when a department moves into a new building or when the current office allocation is not considered optimal anymore? Ideally, an allocation rule would be (1) fair / equitable, (2) efficient, and (3) incentive robust. Of course, our three objectives might find different formulations depending on the exact allocation situation. Unfortunately, often the most natural properties to reflect (1) - (3) are not compatible and thus, an ideal allocation method usually does not exist..We explore trade-offs between and characterizations by various normative properties for various object allocation problems, including Shapley-Scarf exchange problems and problems where the set of objects is commonly owned by the agents.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 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".