Bibliographic record
Abstract
a ̀ Montréal for helpful comments. I am also grateful for the hospitality of Princeton University where this paper was completed. 1 A theory of cooperative choice under incomplete information is developed in which agents possess private information at the time of contracting. It is assumed that the group of cooperating agents has agreed on a utilitarian “standard of fairness ” (group preference ordering) governing choices under complete information. The task is to extend this standard to choices whose consequences depend on agents ’ private information. It is accomplished by formulating appropriate axioms of Bayesian coherence at the group level. Assuming the existence of a common prior, the first main result generalizes Harsanyi’s (1955) classical characterization of utilitarian preference aggregation to incomplete information. We then show that Bayesian coherence of group preferences is compatible with Interim Pareto Dominance only if a common prior exists. This second result generalizes and corrects the classical literature on consistent Bayesian preference aggregation under complete information: allowing for incompleteness of information, consistent Bayesian aggregation turns out to be possible even if agents ’ beliefs differ, as long as differences in beliefs can be attributed to differences in information. We finally relax the assumption that the standard of fairness is complete. In the extreme case in which no interpersonal utility-comparisons are made, this leads to an ex-interim justification of ex-ante Pareto efficiency as a criterion of welfare evaluation. 2 1.
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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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".