Learning Benevolent Leadership in a Heterogenous Agents Economy
Bibliographic record
Abstract
This paper studies the potential commitment value of cheap talkinflation announcements in an agent-based dynamic extension of theKydland-Prescott model. In every period, the policy maker makesa non-binding inflation announcement before setting the actualinflation rate. It updates its decisions using individual evolutionarylearning. The private agents can choose between two differentforecasting strategies: They can either set their forecast equal tothe announcement or compute it, at a cost, using an adaptive learningscheme. They switch between these two strategies as a function ofinformation about the associated payoffs they obtain throughword-of-mouth, choosing always the currently most favorable one.Weshow that the policy maker is able to sustain a situation with apositive but fluctuating fraction of believers. This equilibrium isPareto superior to the outcome predicted by standard theory. Theinfluence of changes in key parameters and the impact of transmissionof information among nonbelievers on the dynamics are studied.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".