Social choice with approximate interpersonal comparison of welfare gains
Bibliographic record
Abstract
Suppose it is possible to make approximate interpersonal comparisons of welfare gains and losses. Thus, if w, x, y, and z are personal psychophysical states (each encoding all ethically relevant information about the physical and mental state of a person), then it sometimes possible to say, ``The welfare gain of the state change w --> x is greater than the welfare gain of the state change y --> z.'' We can represent this by the formula ``(w --> x)> (y --> z)'', where `>' is a `difference preorder': an incomplete preorder on the space of all possible personal state changes. A `social state change' is a bundle of personal state changes. A `social difference preorder' (SDP) is an incomplete preorder on the space of social state changes, which satisfies Pareto and Anonymity axioms. The `minimal' SDP is the natural extension of the Suppes-Sen preorder to this setting; we show that it is a subrelation of every other SDP. The `approximate utilitarian' SDP ranks social state changes by comparing the sum total utility gain they induce, with respect to all `utility functions' compatible with `>'. The `net gain' preorder ranks social state changes by comparing the aggregate welfare gain they induce upon various subpopulations. We show that, under certain conditions, all three of these preorders coincide.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".