Risk preferences and aging: The “Certainty Effect” in older adults’ decision making
Bibliographic record
Abstract
A prevalent stereotype is that people become less risk taking and more cautious as they get older. However, in laboratory studies, findings are mixed and often reveal no age differences. In the current series of experiments, we examined whether age differences in risk seeking are more likely to emerge when choices include a certain option (a sure gain or a sure loss). In four experiments, we found that age differences in risk preferences only emerged when participants were offered a choice between a risky and a certain gamble but not when offered two risky gambles. In particular, Experiments 1 and 2 included only gambles about potential gains. Here, compared with younger adults, older adults preferred a certain gain over a chance to win a larger gain and thus, exhibited more risk aversion in the domain of gains. But in Experiments 3 and 4, when offered the chance to take a small sure loss rather than risking a larger loss, older adults exhibited more risk seeking in the domain of losses than younger adults. Both their greater preference for sure gains and greater avoidance of sure losses suggest that older adults weigh certainty more heavily than younger adults. Experiment 4 also indicates that older adults focus more on positive emotions than younger adults do when considering their options and that this emotional shift can at least partially account for age differences in how much people are swayed by certainty in their choices.
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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 | 0.003 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".