Сравнение методов

	Симуляция клеточных автоматов для сценариев политики	Агентное моделирование (АМ)
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	1979–1997	1970s–1990s (formalized as a field)
Автор метода	Tobler, W. (CA foundations); Clarke, K.C. et al. (policy/urban CA scenarios)	Thomas Schelling and Robert Axelrod (foundational contributions, 1970s–1990s)
Тип	Grid-based scenario simulation	Computational simulation method
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.	Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press.
Другие названия	PSCA, CA Policy Scenario Modeling, Policy-driven CA Simulation, Scenario-based Cellular Automata	ABM, Ajan Tabanlı Modelleme (ABM), multi-agent simulation, individual-based modeling
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.	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.