Módszerek összehasonlítása

	Ügynökalapú modellezés (ABM)	Latin Hypercube Sampling
Tudományterület	Szimuláció	Szimuláció
Módszercsalád	Process / pipeline	Process / pipeline
Keletkezés éve	1970s–1990s (formalized as a field)	1979
Megalkotó	Thomas Schelling and Robert Axelrod (foundational contributions, 1970s–1990s)	—
Típus	Computational simulation method	Stratified space-filling sampling design
Alapmű	Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press.	McKay, M.D., Beckman, R.J. & Conover, W.J. (1979). A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code. Technometrics, 21(2), 239-245.
Alternatív nevek	ABM, Ajan Tabanlı Modelleme (ABM), multi-agent simulation, individual-based modeling	LHS, Latin Hiperküp Örnekleme (LHS) ve Duyarlılık Analizi, stratified sampling design, space-filling design
Kapcsolódó	5	4
Összefoglaló	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.	Latin Hypercube Sampling (LHS) is a stratified space-filling design for computer experiments, introduced by McKay, Beckman, and Conover in 1979. It divides each input variable's range into equally probable strata and draws exactly one sample per stratum, ensuring that the full input space is covered with far fewer model evaluations than standard Monte Carlo simulation requires. It is routinely paired with global sensitivity analysis — particularly Sobol indices — to quantify how much each input drives output variability.