Willingness to pay to reduce future risk: a fundamental issue to invest in prevention behaviour
Bibliographic record
Abstract
At the core of the decision to invest in prevention are individuals who face immediate real costs against future and uncertain benefits. In this paper, we elicit subjects’ willingness to pay to reduce future risk. In our experiments, subjects are given a cash endowment and a risky lottery. They report their willingness to pay to exchange the risky lottery for a safe one. Subjects play the lottery either immediately, eight weeks later, or 25 weeks later. Thus, both the lottery and the future are sources of uncertainty in our experiments. In two additional treatments, we control for future uncertainty with a continuation probability (a stopping rule), constant and independent across periods, that simulates the chances of not being able to return to play the lottery after 8 and 25 periods. We find evidence for a present bias in both the time-delay sessions and the continuation probability sessions, suggesting that this bias robustly persists in environments including both risk and future uncertainty. Therefore, eliciting prevention behaviour is a major challenge.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".