Framing effects and risk‐sensitive decision making
Bibliographic record
Abstract
Prospect theory suggests that people are risk-averse when facing gains, but risk-prone when facing losses, a pattern known as the framing effect. Although framing effects have been widely demonstrated, few studies have investigated framing effects under conditions of need. Risk-sensitivity theory predicts that decision makers should prefer high-risk options in situations of high need, when lower risk options are unlikely to meet those needs. In two experiments, we examined (1) whether framing effects occurred in behavioural tasks involving risky decision making from description and decision making from experience, (2) whether participants' risky decision making conformed to the predictions of risk-sensitivity theory, and (3) whether decision framing interacted with conditions of need to influence decision making under risk. The results suggest that under all circumstances, risky decision making conformed to the predictions of risk-sensitivity theory. Framing effects were at least partially demonstrable under all experimental conditions. Finally, negative frames interacted with situations of high need to produce particularly elevated levels of risky choice. Together, the results suggest that risk-sensitivity theory can augment prospect theory to explain choice under conditions of need.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".