Individual Decision Making in a Negative Externality Experiment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The experimental treatments analysed in this paper are simple in that there is a unique Nash equilibrium resulting in each player having a dominant strategy. However, the data show quite clearly that subjects do not always choose this strategy. In fact, when this dominant strategy is not a “focal” outcome it does not even describe the average decision adequately. It is shown that average individual decisions are best described by a decision error model based on a censored distribution as opposed to the truncated regression model which is typically used in similar studies. Moreover it is shown that in the treatments where the dominant strategy is not “focal” dynamics are important with average subject decisions initially corresponding to the “focal” outcome and then adjusting towards the Nash prediction. Overall, 66.7% of subjects are consistent with Payoff Maximization, 27.8% are consistent with an alternate preference maximization and 5.6% are random.
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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 | 0.001 |
| 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 it