Dynamic Information Revelation in Cheap Talk
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
Abstract This paper studies a multi-stage version of Crawford and Sobel’s communication game. In every period the receiver determines a test about the unknown state whose result is privately observed by the sender. After the sender sends a costless message about an outcome of the test, the receiver selects a test in the next period. After a finite number of periods of interaction, the receiver makes a decision. The paper offers a sequence of tests that refine sender’s information step-by-step and preserve truthtelling in every period. This sequence allows the receiver to learn the state in a subinterval of the state space with an arbitrary precision and has appealing theoretical properties. It consists of simple binary tests which reveal whether the state is above a certain cutoff, where the cutoffs are monotonic across periods and independent from results of the previous tests. Finally, we show that the relative payoff efficiency of multi-stage interaction compared to a single-stage game increases without a bound as the bias in preferences tends to zero.
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 | 0.010 | 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 | 0.001 |
| Open science | 0.001 | 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