The Virtue Blind Spot: Do Affective Forecasting Errors Undermine Virtuous Behavior?
Bibliographic record
Abstract
Abstract Why is it difficult to be virtuous? Although cultural wisdom teaches that cultivating virtue brings happiness—and empirical studies have demonstrated the long‐term benefits of acting virtuously—many people seem to behave as though exercising virtues is difficult, or even painful. When it comes to virtue, any benefits for the self may seem distant: short‐term pain for long‐term gain. We propose, however, that behaving virtuously often provides affective benefits even in the short term, but these benefits are obscured by systematic affective forecasting errors. Using five virtues (humanity, wisdom, courage, temperance, and transcendence), we demonstrate that people tend to feel happier after acting virtuously. We also show that people do not realize that these short‐term emotional benefits will occur; when asked to predict how they will feel, people make inaccurate affective forecasts. We argue that these affective forecasting errors drive people away from the exercise of virtue.
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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 | 0.005 | 0.041 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".