Butt-dialing the devil: Evil agents are expected to disregard intentions behind requests
Bibliographic record
Abstract
Evil supernatural beings are often depicted as responding to unintended requests, whereas this may be less common in representations of good supernatural beings. This asymmetry suggests that people may expect good and evil agents to differ in their sensitivity to other people's intentions. We investigated this proposal across five experiments on 2231 adult US residents. In Experiments 1 to 4, participants judged whether good or evil agents would grant requests from individuals who varied in their understanding of what they requested, and in whether they executed requests correctly. Across experiments, the good and evil agents were either supernatural beings or regular humans. Participants predicted good agents would be sensitive to intentions behind requests, but predicted evil agents would be comparatively insensitive to these intentions. In some experiments, they also predicted that evil agents would be more sensitive to whether requests were executed correctly. In Experiment 5, participants rated explanations for why an agent would grant a request from someone who did not understand what they were requesting. Participants thought evil agents might grant such requests because they are indifferent to the others' intentions, but participants did not strongly endorse this explanation for good agents. Taken together, our findings suggest that people have distinct expectations of how moral character affects decision-making. They also suggest that people's beliefs about good and evil supernatural beings may be grounded in their views of ordinary humans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".