Learning to be rational in the presence of news: A lab investigation
Why this work is in the frame
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Bibliographic record
Abstract
We conduct a laboratory experiment in a micro-founded macroeconomic model where participants receive public announcements about future government spending shocks, and are tasked with repeatedly forecasting output over a given horizon. By eliciting several-period-ahead predictions, we can investigate forecast revisions in relation to these announcements. We find that subjects learn the magnitude of the effect of the shocks on output, albeit not with perfect accuracy. We find micro-level evidence that they persistently underreact to the announcements in a way consistent with sticky information, but find little support for fully backward-looking expectations. We rationalize the experimental data with a Bayesian updating model, which provides a particularly good description of the behaviors in longer-horizon environments and among attentive, experienced, and effortful subjects.
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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 | 0.006 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it