A moral trade-off system produces intuitive judgments that are rational and coherent and strike a balance between conflicting moral values
Bibliographic record
Abstract
How does the mind make moral judgments when the only way to satisfy one moral value is to neglect another? Moral dilemmas posed a recurrent adaptive problem for ancestral hominins, whose cooperative social life created multiple responsibilities to others. For many dilemmas, striking a balance between two conflicting values (a compromise judgment) would have promoted fitness better than neglecting one value to fully satisfy the other (an extreme judgment). We propose that natural selection favored the evolution of a cognitive system designed for making trade-offs between conflicting moral values. Its nonconscious computations respond to dilemmas by constructing "rightness functions": temporary representations specific to the situation at hand. A rightness function represents, in compact form, an ordering of all the solutions that the mind can conceive of (whether feasible or not) in terms of moral rightness. An optimizing algorithm selects, among the feasible solutions, one with the highest level of rightness. The moral trade-off system hypothesis makes various novel predictions: People make compromise judgments, judgments respond to incentives, judgments respect the axioms of rational choice, and judgments respond coherently to morally relevant variables (such as willingness, fairness, and reciprocity). We successfully tested these predictions using a new trolley-like dilemma. This dilemma has two original features: It admits both extreme and compromise judgments, and it allows incentives-in this case, the human cost of saving lives-to be varied systematically. No other existing model predicts the experimental results, which contradict an influential dual-process model.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".