Revisiting the moral forecasting error – A preregistered replication and extension of “Are we more moral than we think?”
Bibliographic record
Abstract
Predictions are often inaccurate. Still, the direction of prediction errors may vary. Contrary to research on the intention-behavior gap, where people fail to live up to their ambitions, a study on “moral forecasting” found that people behaved more honestly than they predicted. In this registered report, we present two close replication attempts and one conceptual replication attempt of this moral forecasting error across two experiments. In Experiment 1 ( N = 1839), we recruited a general population sample from the same country as the original study (Canada) to an online experiment. We successfully replicated the moral forecasting error using a math-based cheating task from the original study: Predicted cheating was much higher in a moral forecasting condition than actual cheating in a moral action condition ( d = 0.69). In Experiment 2 ( N = 1381) we replicated the forecasting error again, using the same task in a general population sample from the U.S. ( d = 0.72). However, we were unable to conceptually replicate the effect using a different dishonesty measure, the “mind game”, in Experiment 1 (φ = 0.03). We also could not reduce the forecasting error through a debiasing intervention in Experiment 2 ( d = 0.01). Across both experiments, participants predicted that others would cheat much more than they would themselves. In this registered report, we conclude that the moral forecasting error is robust for the original cheating task. We also show that it can generalize contextually (from a lab to an online setting), but not to a different task. Future research may show exactly when predictions about one's own honesty are pessimistic rather than optimistic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".