Information‐seeking when information doesn't matter
Bibliographic record
Abstract
Abstract Prior research shows that investors check their portfolios less frequently when they believe negative returns on investments are likely. This so‐called ostrich effect is accounted for by belief‐based utility theories that suggest that information demand is determined by the potential of information to evoke or maintain pleasant beliefs. An alternative is that information matters as there are more courses of action that investors would take with positive than negative portfolio returns. Across three experiments, we adapt a non‐instrumental sampling paradigm to verify whether people are more likely to seek out information when expecting small gains as compared to losses when the instrumental utility of outcome information is 0. We also explore whether the effect can be attenuated by making outcome information easier to find. Our findings suggest that people are more likely to initiate and persist in search for prospective financial gains than losses, and for unknown than known financial outcomes, even though confirming any given outcome is effectively useless. The magnitude of the ostrich effect increased with increased certainty of a financial gain and loss. Making outcome information easier to find increased the likelihood that an outcome would be discovered but did not strongly modify intent to seek it out or moderate ostrich effects. We discuss how the findings are consistent with non‐instrumental utility frameworks for information demand, inconsistent with literature showing greater attention to financial loss than gain outcome information, and propose testable hypotheses for resolving the discrepancy.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".