Best of Both Worlds: Ex Ante and Ex Post Fairness in Resource Allocation
Bibliographic record
Abstract
Consider the problem of allocating indivisible goods among agents with additive valuations, where monetary payments are not allowed. When randomization is allowed, it is possible to achieve compelling notions of fairness such as EV, which states that no agent should prefer any other agent's allocation to their own. When allocations must be deterministic, achieving exact fairness is impossible but approximate notions such as EV up to one good can be guaranteed. In “Best of Both Worlds: Ex Ante and Ex Post Fairness in Resource Allocation,” H. Aziz, R. Freeman, N. Shah, and R. Vaish ask whether it is possible to achieve both types of guarantees simultaneously. More specifically, they ask whether there exists a probability distribution over deterministic allocations such that every deterministic allocation is envy-free up to one good and the distribution is exactly envy-free in expectation. The main result of the paper answers this question in the affirmative, showing that ex ante and ex post fairness need not be in conflict.
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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 | 0.017 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".