Population Protocols: Expressiveness, Succinctness and Automatic Verification.
Bibliographic record
Abstract
Population protocols (Angluin et al., PODC, 2004) are a model of distributed computation in which identical, finite-state, passively mobile agents interact in pairs to achieve a common goal.In the basic model of population protocols, agents compute number predicates by reaching a stable consensus.It is well known that population protocols compute precisely the semilinear predicates, or, equivalently, the predicates definable in Presburger arithmetic, the first-order theory of the natural numbers equipped with addition and the standard linear order.This thesis investigates three fundamental questions of the theory of population protocols: Space complexity, verification complexity, and expressiveness of reasonable extensions.First and foremost, I would like to thank my doctoral advisor, Javier Esparza, for his invaluable guidance and support.Javier gave me plenty of opportunities for personal growth; his focus on clarity and precision helped me become a better researcher, and a better communicator of ideas.With sincere gratitude, I would like to thank my Canadian co-author Michael Blondin, who I now consider a good friend.Even by Canadian standards, Michael is exceptionally nice and
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.017 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".