Public Information Precision and Coordination Failure: An Experiment
Bibliographic record
Abstract
ABSTRACT More precise public disclosure reduces uncertainty about economic fundamentals, but it can increase uncertainty about other agents' actions, leading to coordination failure. We conducted a laboratory experiment to study the effects of public information precision and strategic complementarity on coordination failure. Information precision is operationalized in terms of “granularity” (level of detail). We found that (1) granular public disclosure, which is disaggregated and precise, increases the likelihood of coordination failure and decreases coordination efficiency when public information is pessimistic about future economic prospects; (2) the deleterious effect of granular disclosure is stronger when strategic complementarity is high; and (3) higher levels of strategic complementarity decrease coordination efficiency. Overall, the observed likelihood of coordination failure is higher and coordination efficiency is lower than predicted by theory. Our findings have implications for the Federal Reserve's decision to publicly disclose detailed stress test results for distressed banks, and the debate on whether the Public Company Accounting Oversight Board should publicly release reports on firm‐specific quality‐control deficiencies of audit firms.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".