Robustesse des systèmes complexes : étude du rôle de l’aléa dans la dynamique des automates cellulaires
Bibliographic record
Abstract
This thesis presents a selection of research works on the theme of cellular automata and discrete complex systems. We are interested in the robustness of these systems, mainly through the use of asynchronous or probabilistic rules. We start from the problem of analysing and classifying the dynamics of asynchronous elementary cellular automata. We use techniques from probability theory such as Markov chains and martingales in order to understand the convergence properties of such systems. In the second part, we present models which are more complex locally and we estimate their robustness properties mainly with numerical simulations. Experiments show that the various collective phenomena we observe have responses to perturbations which are difficult to predict from the analysis of their local rule. The third parts consists of the presentation of three inverse problems, where one seeks to find the rules that produce a given behaviour, here a consensus on the state of the cells or the agents. The overall variety of models that are discussed in the thesis allow us to study a variety of models with complementary techniques, thus following the path opened by Alan Turing in his 1952 article on morphogenesis.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".