Is Utilitarianism Risky? How the Same Antecedents and Mechanism Produce Both Utilitarian and Risky Choices
Bibliographic record
Abstract
Philosophers and psychologists have long been interested in identifying factors that influence moral judgment. In the current analysis, we compare the literatures on moral psychology and decision making under uncertainty to propose that utilitarian choices are driven by the same forces that lead to risky choices. Spanning from neurocognitive to hormonal to interpersonal levels of analysis, we identify six antecedents that increase both utilitarian and risky choices (ventromedial prefrontal cortex brain lesions, psychopathology, testosterone, incidental positive affect, power, and social connection) and one antecedent that reduces these choices (serotonin activity). We identify the regulation of negative affect as a common mechanism through which the effects of each antecedent on utilitarian and risky choices are explained. By demonstrating that the same forces and the same underlying mechanism that produce risky choices also promote utilitarian choices, we offer a deeper understanding of how basic psychological systems underlie moral judgment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".