When do consumers favor overly precise information about investment returns?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Consumers are often shown investment returns with high levels of precision, which could lead them to misunderstand the inherent uncertainty. We test whether consumers are drawn to precision-that is offset the uncertainty in investment decisions by over-relying on precise numerical information. Five incentivized experiments compared decisions when expected growth is presented in precise forecasts as opposed to ranges. Consumers are more likely to prefer and invest more in precise forecasts when they are evaluated jointly with ranges and when the range features a potential loss. Under these circumstances, precise forecasts give consumers more confidence to invest. This effect holds when consumers are told investment returns are uncertain. On the other hand, experiencing discrepancies between expected and actual growth dissipates the preference for precise forecasts. We identify conditions under which consumers are more likely to favor precise forecasts and how this could be avoided if necessary. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it