Demand for information about potential wins and losses: Does it matter if information matters?
Bibliographic record
Abstract
Abstract The ostrich effect refers to the observation that people prioritize gathering information about prospectively positive financial outcomes. It is especially problematic when information about negative and positive outcomes is equally useful for making sound financial decisions. Yet, it is unclear to what extent this phenomenon is moderated by whether outcome information is useful for making choices. Here, we test whether making outcome information instrumental to choice moderates the ostrich effect by randomly assigning 800 adults to one of two computer‐based gambling tasks, one in which they chose between two 50/50 win/lose gambles and another in which the computer chose one for them at random. The four possible outcomes were concealed by win/loss marked tiles, and participants were required to reveal three of the four possible outcomes before a gamble could be selected. The key finding was that demand for full information about losses increased significantly when participants made their own choices, and thus, outcome information was instrumental. The findings suggest that information about losses is de‐prioritized particularly when people cannot take action to influence payoffs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".