Surviving at Any Cost: Guilt Expression Following Extreme Ethical Conflicts in a Virtual Setting
Bibliographic record
Abstract
Studying human behavior in response to large-scale catastrophic events, particularly how moral challenges would be undertaken under extreme conditions, is an important preoccupation for contemporary scientists and decision leaders. However, researching this issue was hindered by the lack of readily available models. Immersive virtual worlds could represent a solution, by providing ways to test human behavior in controlled life-threatening situations. Using a massively multi-player zombie apocalypse setting, we analysed spontaneously reported feelings of guilt following ethically questionable actions related to survival. The occurrence and magnitude of guilt depended on the nature of the consequences of the action. Furthermore, feelings of guilt predicted long-lasting changes in behavior, displayed as compensatory actions. Finally, actions inflicting immediate harm to others appeared mostly prompted by panic and were more commonly regretted. Thus, extreme conditions trigger a reduction of the impact of ethical norms in decision making, although awareness of ethicality is retained to a surprising extent.
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".