ISSN 0956-8549-600 Efficient Dynamic Coordination with Individual Learning By
Bibliographic record
Abstract
interests lie in information economics and game theory with applications to finance, and has focussed on the theory of financial crises and delegated portfolio management. His research has been published in journals such as the Review of Economic Studies, the Journal of Economic Theory, and the Journal of the European Economic Association. Jakub Steiner graduated from CERGE, Charles University in Prague in 2006. Before he studied economics, he attained MSc in theoretical physics at Charles University. He is mainly interested in microeconomics, and in particular, in game theory. He studies how people coordinate on actions in situations with strategic complementarities. His research has implications for understanding sudden changes in society which occur during currency attacks, bank runs, revolutions etc. Before his postgraduate studies, Jakub worked as a social worker concentrating on the Roma community, and since then he has been interested in problems of discrimination. Colin Stewart is an Assistant Professor of Economics at the University of Toronto specializing in game theory. Any opinions expressed here are those of the authors and not necessarily those of the FMG. The research findings reported in this paper are the result of the independent research of the authors and do not necessarily reflect the views of the LSE. E ¢ cient Dynamic Coordination with Individual Learning
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.398 | 0.160 |
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".