方法对比

	基于主体的建模（ABM）	元胞自动机	最小成本路径 / 成本距离分析
领域	仿真	仿真	空间分析
方法族	Process / pipeline	Process / pipeline	Process / pipeline
起源年份	1970s–1990s (formalized as a field)	1940s–1950s (formalized); 1970 (Conway's Game of Life); 2002 (Wolfram's systematic classification)	1994
提出者	Thomas Schelling and Robert Axelrod (foundational contributions, 1970s–1990s)	John von Neumann and Stanislaw Ulam (1940s–1950s); popularized by John Conway (1970) and Stephen Wolfram (1980s–2002)	Edsger Dijkstra (shortest path); GIS cost-surface adaptation
类型	Computational simulation method	Grid-based computational simulation model	Raster cost-surface routing
开创性文献	Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press.	Wolfram, S. (2002). A New Kind of Science. Wolfram Media. ISBN: 978-1579550080	Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1(1), 269–271.
别名	ABM, Ajan Tabanlı Modelleme (ABM), multi-agent simulation, individual-based modeling	CA, Hücresel Otomat (Cellular Automata), lattice model, grid-based simulation	cost-distance analysis, accumulated cost surface, least-cost corridor, en düşük maliyetli yol
相关	5	5	3
摘要	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.	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.	Least-cost path analysis finds the route between two locations that minimizes accumulated travel cost across a landscape, rather than minimizing straight-line distance. By encoding terrain, slope, land cover, and other frictions into a cost surface and accumulating cost outward from a source, it identifies optimal corridors for roads, pipelines, trails, power lines, and wildlife movement — a core raster-GIS technique built on Dijkstra's shortest-path logic.