Social Aggregation Without the Expected Utility Hypothesis
Bibliographic record
Abstract
This paper investigates the possibilities for satisfaction of both the ex-ante and ex-post Pareto principles in a general model in which neither individual nor social preferences necessarily satisfy the Expected Utility Hypothesis. If probabilities are subjective and allowed to vary, three different impossibility results are presented. If probabilities are 'objective' (identical across individuals and the observer), necessary and sufficient conditions on individual and social value functions are found (Theorem 4). The resulting individual value functions are consistent not only with Subjective Expected Utility theory, but also with some versions of Prospect Theory, Subjectively Weighted Utility Theory, and Anticipated Utility Theory. Social Preferences are Weighted Generalized Utilitarian and, in the case in which individual preferences satisfy the Generalized Bernoulli Hypothesis, they are Weighted Utilitarian. The objective-probability results for social preferences cast a new light on Harsanyi's Social Aggregation Theorem, which assumes that both individual and social preferences satisfy the Expecte Utility Hypothesis.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".