Development of an agent-based demographic model of Russia and its supercomputer implementation
Bibliographic record
Abstract
Рассмотрено применение агент-ориентированного подхода при моделировании естественного движения населения. Представлена демографическая модель России с учетом ее административного деления, в которой на основе моделирования поведения отдельных членов искусственного общества имитируются процессы смертности, рождаемости и миграции. Для моделирования поведения искусственного общества в целом требуется проведение модельных расчетов с числом агентов до $10^9$ и использование суперкомпьютерных технологий. Важной задачей в таких расчетах становится оптимальное распределение агентов по процессорам кластера. Показано применение декомпозиции модели с использованием алгоритма METIS с учетом основных особенностей агентной модели. Обсуждаются результаты апробации модели. The application of the agent-based modeling approach to the problem of natural human migration is considered. A demographic model of Russia is presented. This model takes into account the administrative division of Russia and simulates the processes of fertility, mortality and migration on the basis of modeling the behavior of individual members of the artificial society. In order to simulate the behavior of the artificial society as a whole, it is necessary to perform numerical experiments with the number of agents up to $10^9$ and to use supercomputer technologies. In such experiments, an important problem is the implementation of an optimal automatic distribution of agents across the cluster processors. The application of model decomposition using the METIS algorithm with consideration of the main features of the agent model is shown. The obtained numerical results are discussed.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".