Learning Manipulation Through Information Dissemination
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
Much of the past learning and control literature has focused on the design of information acquisition processes for the demand side of information and has assumed that information supply is always genuine. However, in many economic and management settings, the information provider has incentives to strategically disseminate his/her private information, even in a possibly biased way. For example, a company may advertise deceptively to sell its products. In the paper “Learning Manipulation Through Information Dissemination,” Keppo, Kim, and Zhang take the perspective of an information provider and study the optimal manipulation of a learning process through the adaptive design of (mis)information. The authors explicitly characterize both the optimal manipulation policy and the learner’s belief process under such manipulation. They also extend their analysis to social learners who rely on public reviews to resist manipulation and show that social learning indeed mitigates misinformation in the long run.
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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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