Foregone Opportunities and Choosing Not to Act: Replications of Inaction Inertia Effect
Bibliographic record
Abstract
Inaction inertia is the phenomenon that forgoing an initial attractive opportunity decreases the likelihood of taking a subsequent opportunity that is less attractive, even when the subsequent opportunity still offers positive value. We conducted three preregistered replications of Tykocinski et al.’s Experiments 1 and 2’s four scenarios in four samples ( N = 1,555). We found consistent findings across samples, with the inaction inertia effect dependent on the scenario used. Strongest support was for the car scenario ( d = −0.57 to −0.68) and the ski scenario ( d = −0.18 to −0.67), with mixed findings for the fitness scenario (large-small: d = −0.62; control contrasts: opposite to predictions) and weak to no effects for the flyer scenario ( d = −0.14 to 0.02). We conclude that context is important in studying inaction inertia, recommend the car and ski scenarios for follow-up research on inaction inertia, and discuss implications for future research.
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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 | 0.001 |
| 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".