Assessing patience and predictivity validity for mixed sign intertemporal choices
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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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 | 0.014 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".