The Simpler, the Better: A New Challenge for Fair-Division Theory
Bibliographic record
Abstract
According to John Rawls, there exists a perfect procedural justice for which there is no conflict between process and outcome.One such procedure is the Divide and Choose.Recently, the mathematical theory of fair-division extended this idea by developing procedures that offer fairer outcomes and a better guarantee of justice.Here, we tested the extent to which the distributive and procedural properties of these perfect and improved division procedures were perceived as more satisfactory and fairer than imperfect division procedures.Thirty-nine pairs of participants divided six $10 gift certificates between them using seven division procedures.They rated their satisfaction and their perceived fairness before and after they executed each division procedure.Contrarily to our hypothesis, the results show that perfect procedural justice does not really translate into the perception of a fairer and more satisfactory outcome and process.The most sophisticated division procedures failed to select fair and satisfactory solutions.
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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 | 0.027 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.048 |
| Scholarly communication | 0.009 | 0.022 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".