Bibliographic record
Abstract
How much information should a policy maker pass on to an ill-informed citizen? In this paper, we address this classic question of Crawford and Sobel (1982) in a setting in which beliefs impact utility, as in Kreps and Porteus (1978). We show that this question cannot be answered using a utility function with standard revealed preference foundations. To solve the model, we go beyond the classical model in two respects, relying on the psychological expected utility model of Caplin and Leahy (2001) to capture preferences, and the psychological game model of Geanakoplos et al. (1989) to capture strategic interactions. How much information should a policy maker pass on to a currently ill-informed citizen? This question was first posed formally in the classic sender-receiver game of Crawford and Sobel (1982). In that model, the citizen in question had standard expected utility preferences. In this paper, we enrich the question by allowing for a broader class of preferences, in particular preferences over the timing of resolu-tion of uncertainty.1 This amendment allows us to address such questions as whether or not a doctor should reveal the truth to a terminally ill patient who is naively optimistic, and whether or not parents should tell their children the truth
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".