Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Симуляция клеточных автоматов для сценариев политики× | Клеточные автоматы× | |
|---|---|---|
| Область | Имитационное моделирование | Имитационное моделирование |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1979–1997 | 1940s–1950s (formalized); 1970 (Conway's Game of Life); 2002 (Wolfram's systematic classification) |
| Автор метода≠ | Tobler, W. (CA foundations); Clarke, K.C. et al. (policy/urban CA scenarios) | John von Neumann and Stanislaw Ulam (1940s–1950s); popularized by John Conway (1970) and Stephen Wolfram (1980s–2002) |
| Тип≠ | Grid-based scenario simulation | Grid-based computational simulation model |
| Основополагающий источник≠ | Clarke, K. C., Hoppen, S., & Gaydos, L. (1997). A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environment and Planning B: Planning and Design, 24(2), 247–261. DOI ↗ | Wolfram, S. (2002). A New Kind of Science. Wolfram Media. ISBN: 978-1579550080 |
| Другие названия | PSCA, CA Policy Scenario Modeling, Policy-driven CA Simulation, Scenario-based Cellular Automata | CA, Hücresel Otomat (Cellular Automata), lattice model, grid-based simulation |
| Связанные | 5 | 5 |
| Сводка≠ | Policy Scenario Cellular Automata (PSCA) combines cellular automata simulation with structured scenario analysis to evaluate how alternative policy decisions reshape spatially distributed systems over time. Each scenario encodes a different set of transition rules or constraints, and the model iterates to reveal divergent spatial outcomes — enabling direct, visual comparison of policy consequences at the local and system level. | Cellular automata (CA) is a grid-based computational simulation model, first formalized by John von Neumann and Stanislaw Ulam in the 1940s–1950s and brought to wide attention by John Conway's Game of Life (1970) and Stephen Wolfram's systematic classification (2002), in which a lattice of cells — each holding a finite discrete state — evolves in discrete time steps according to local neighborhood interaction rules, causing complex global patterns to emerge from simple local specifications. |
| ScholarGateНабор данных ↗ |
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