Blind to Bias? Young Children Do Not Anticipate that Sunk Costs Lead to Irrational Choices
Bibliographic record
Abstract
Young children anticipate that others act rationally in light of their beliefs and desires, and environmental constraints. However, little is known about whether children anticipate others' irrational choices. We investigated young children's ability to predict that sunk costs can lead to irrational choices. Across four experiments, 5- to 6-year-olds (total N = 185) and adults (total N = 117) judged which of two identical objects an agent would keep, one obtained at a high cost or one obtained at a low cost. In Experiment 1, adults predicted that the agent would choose the high-cost object over the low-cost one, whereas children responded at chance. Experiment 2 replicated these findings in children, but also included another condition which showed they were sensitive to future costs. They predicted that an agent would be more likely to seek out a low-cost item than a high-cost item. Experiments 3 and 4 then found that children do not anticipate the sunk cost bias in first person scenarios, or in interpersonal sunk cost scenarios, where costs are sunk by others. Taken together, our findings suggest that young children may struggle to understand and predict irrational behavior. The findings also reveal an asymmetry between how they consider sunk costs and future costs in understanding actions. We propose that this asymmetry might arise because children do not consider sunk costs as wasted.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".