Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Агентное моделирование (АМ)× | Метод Монте-Карло× | |
|---|---|---|
| Область≠ | Имитационное моделирование | Принятие решений |
| Семейство≠ | Process / pipeline | MCDM |
| Год появления≠ | 1970s–1990s (formalized as a field) | 1949 |
| Автор метода≠ | Thomas Schelling and Robert Axelrod (foundational contributions, 1970s–1990s) | Metropolis, N., Ulam, S. |
| Тип≠ | Computational simulation method | Robustness wrapper — Monte Carlo uncertainty propagation |
| Основополагающий источник≠ | Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press. DOI ↗ | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| Другие названия≠ | ABM, Ajan Tabanlı Modelleme (ABM), multi-agent simulation, individual-based modeling | — |
| Связанные≠ | 5 | 0 |
| Сводка≠ | Agent-based modeling (ABM) is a computational simulation method, formalized through the work of Thomas Schelling and Robert Axelrod in the 1970s–1990s, that simulates the behavior of complex systems by specifying and running autonomous agents — individuals, firms, cells, or any bounded entity — whose local interactions with each other and with their environment collectively produce global, system-level patterns that could not be predicted from any single agent's rules alone. | MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result. |
| ScholarGateНабор данных ↗ |
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