Assessing patience and predictivity validity for mixed sign intertemporal choices
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 Most research on intertemporal choice has examined choices between smaller, sooner gains and larger, later gains. A much smaller number of papers have examined intertemporal choices for losses. In this article, we explore whether mixed-sign choices with both gains and losses may better correlate with real-world behaviors. In two high-powered studies (pilot: N = 3,200; main study: N = 7,000), participants completed one of four normatively equivalent measures consisting of pure gain, pure loss, or mixed sign (Gain-Now-Loss-Later or Loss-Now-Gain-Later) intertemporal choices. Participants also self-reported a large number of demographic measures and real-world choice behaviors thought to be linked to intertemporal choice. The results indicate that (1) mixed-sign intertemporal choices yield more patient time preferences than pure-gain choices but less patient than pure-loss choices and (2) pure-gain intertemporal choices yield equivalent or superior predictive power across a range of real-world intertemporal choice behaviors.
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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 | 0.004 | 0.002 |
| 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 | 0.004 | 0.002 |
| 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