Bracketing effects on risk tolerance: Generalizability and underlying mechanisms
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 Research has shown that risk tolerance increases when multiple decisions and associated outcomes are presented together in a broader “bracket” rather than one at a time. The present studies disentangle the influence of problem bracketing (presenting multiple investment options together) from that of outcome bracketing (presenting the aggregated outcomes of multiple decisions), factors which have been deliberately confounded in previous research. In the standard version of the bracketing task, in which participants decide how much of an initial endowment to invest into each in a series of repeated, identical gambles, we find a problem bracketing effect but not an outcome bracketing effect. However, this pattern of results does not generalize to the cases of non-identical gambles nor discrete choice, where we fail to find the standard bracketing effect.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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