Using vNM expected utility theory to facilitate the decision-making in social ethics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The overall objective of this article is to demonstrate that in social ethics, certain problems related to decision-making are easier to resolve using conceptual tools borrowed from mathematics than using philosophical ethics theories, such as classical utilitarianism. With the help of a case study, the first part of the article will attempt to point out that if an agent bases his reasoning on the verbal and purely qualitative concepts of the classical utilitarian theory, he will find himself confronting ‘undecidable’ dilemmas, for which making a specific choice becomes almost arbitrary. The second part of the article proposes a more formal quantification of utility and attitude towards risk that can help the agent to overcome the uncertainties emanating from a strictly qualitative perception of the real world’s configuration. This method for decision-making is inspired by the works of Howard Raiffa, John von Neumann and Oskar Morgenstern.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.036 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it