The Effect of Temporal Distance on Attitudes toward Imprecise Probabilities and Imprecise Outcomes
Bibliographic record
Abstract
ABSTRACT Many personal, managerial, and societal decisions involve uncertain or ambiguous consequences that will occur in the future. Yet, previous empirical research on ambiguity preferences has focused mainly on decisions with immediate outcomes. To close this gap in the literature, this paper examines ambiguity attitudes toward future prospects, particularly how they may differ from the attitudes toward comparable prospects in the present. On the basis of a recent paradigm, we first distinguish between two types of ambiguity: imprecise probabilities and imprecise outcomes. Then, in accordance with construal level theory, which shows that temporal distance increases the relative importance of outcomes over probabilities in evaluating prospects, we conjecture that temporal distance would moderate attitudes toward imprecise probabilities but amplify attitudes toward imprecise outcomes. Through a series of experiments, we demonstrate that when the prospects are in the future, individuals are less averse toward imprecise probabilities and more seeking toward imprecise outcomes. However, the effect is most prominent for prospects where both the probability and outcome dimensions are concurrently imprecise. The paper ends with a discussion on how dimension salience may have contributed to this result. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.004 | 0.027 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".