Sammenlign metoder

	Multi-Objective Celleautomater	Agent-Based Modeling (ABM)
Fagområde	Simulering	Simulering
Familie	Process / pipeline	Process / pipeline
Oprindelsesår	1990s–2000s	1970s–1990s (formalized as a field)
Ophavsperson	Various (Liu et al., White & Engelen, Clarke et al.)	Thomas Schelling and Robert Axelrod (foundational contributions, 1970s–1990s)
Type	Hybrid simulation-optimization	Computational simulation method
Oprindelig kilde	Liu, X., Liang, X., Li, X., Xu, X., Ou, J., Chen, Y., Li, S., Wang, S., Pei, F. (2017). A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects. Landscape and Urban Planning, 168, 94-116.	Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press.
Aliasser	MOCA, Multi-objective CA, Multi-criteria cellular automata, MO-CA	ABM, Ajan Tabanlı Modelleme (ABM), multi-agent simulation, individual-based modeling
Relaterede	5	5
Resumé	Multi-Objective Cellular Automata (MOCA) couples the bottom-up spatial dynamics of cellular automata with multi-objective optimization to simultaneously pursue competing goals — such as maximizing urban compactness while minimizing ecosystem loss. Each grid cell updates its state based on transition rules that are calibrated or steered to satisfy a Pareto-optimal trade-off among two or more objectives, making the method widely used in land-use change simulation, urban growth modeling, and spatial planning under conflicting demands.	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.