An experimental investigation of persuasion through selective disclosure of evidence
Bibliographic record
Abstract
Abstract We experimentally study the interaction between a persuader and a decision‐maker. The former would like to persuade the latter to approve a project by providing evidence on the project's value. He may choose a selective disclosure strategy on the basis of his private information. Our experimental design contrasts situations where the persuader observes private information or not and where the decision‐maker interacts with a human or robot persuader. The experimental results confirm the theoretical prediction that the human persuader manipulates the production of evidence. Although the decision‐maker does not adequately take into account such manipulation, the comparative static analysis across treatments is mostly consistent with theoretical predictions with a rational decision‐maker. Our findings on the welfare effect of the persuader's manipulation on the decision‐maker are consistent with theory. In particular, the decision‐maker may benefit from such manipulation. However, the welfare effect on the persuader is not always consistent with theory, in that there are instances in which the persuader is not hurt by manipulation even though theory predicts that he is.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".