Young Children use Supply and Demand to Infer Desirability
Bibliographic record
Abstract
In 4 experiments, we show that young children (total N = 290) use information about supply and demand to infer the desirability of resources. In each experiment, children saw scenarios about sandwiches from different shops, which varied in supply (number of sandwiches produced for the day) and demand (number of customers attracted). In Experiments 1 and 2, 5- to 6-year-olds gave higher desirability ratings for sandwiches from shops with greater than lesser demand when supply was held constant. In Experiment 3, 5- to 7-year-olds gave higher desirability ratings for sandwiches from shops with less than more supply when demand was held constant. Finally, in Experiment 4, 5- to 6-year-olds were more likely to judge that sandwiches came from a good shop (rather than from a bad one) when demand exceeded supply than when supply exceeded demand. Together, the findings reveal a way that children can infer how desirable resources are, without needing to incur the costs that would normally be required to obtain and sample the resources themselves.
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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 | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".