Confronta i metodi

	Simulazione stocastica a eventi discreti	Modellazione basata su agenti (ABM)
Campo	Simulazione	Simulazione
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	1960s–1970s	1970s–1990s (formalized as a field)
Ideatore	Banks, Carson, Nelson, Nicol; Law, A. M.	Thomas Schelling and Robert Axelrod (foundational contributions, 1970s–1990s)
Tipo	Stochastic simulation model	Computational simulation method
Fonte seminale	Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-Event System Simulation (5th ed.). Prentice Hall. ISBN: 9780136062127	Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press.
Alias	Stochastic DES, SDES, Probabilistic DES, Monte Carlo DES	ABM, Ajan Tabanlı Modelleme (ABM), multi-agent simulation, individual-based modeling
Correlati	6	5
Sintesi	Stochastic Discrete-Event Simulation (Stochastic DES) models complex systems by advancing simulated time from one discrete event to the next, drawing event durations and inter-arrival times from fitted probability distributions. It is the standard technique for analyzing queues, manufacturing lines, healthcare pathways, and logistics networks under uncertainty, producing output statistics with confidence intervals.	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.