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| Modellazione Deterministica Basata su Agenti× | Simulazione deterministica a eventi discreti× | |
|---|---|---|
| Campo | Simulazione | Simulazione |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 1996 | 1960s–present |
| Ideatore≠ | Epstein, J. M. & Axtell, R. | Banks, J.; Carson, J. S.; Nelson, B. L. (codified); roots in 1960s simulation pioneers (Tocher, Conway) |
| Tipo≠ | Computational simulation — deterministic rule-based agents | Simulation — deterministic event-driven model |
| Fonte seminale≠ | Epstein, J. M., & Axtell, R. (1996). Growing Artificial Societies: Social Science from the Bottom Up. MIT Press. ISBN: 9780262550253 | Banks, J., Carson, J. S., Nelson, B. L., and Nicol, D. M. (2010). Discrete-Event System Simulation (5th ed.). Prentice Hall. ISBN: 9780136062127 |
| Alias | D-ABM, Deterministic ABM, Rule-Based Agent Simulation, Fixed-Rule Agent-Based Model | Deterministic DES, Fixed-Input DES, Non-Stochastic Discrete-Event Simulation, Deterministic Event-Driven Simulation |
| Correlati≠ | 4 | 5 |
| Sintesi≠ | Deterministic Agent-Based Modeling (D-ABM) is a computational simulation approach in which autonomous agents follow fully specified, non-random behavioral rules within a structured environment. Every run with identical initial conditions produces identical outcomes, making the model fully reproducible and transparent for analysis of emergent system behavior without stochastic noise. | Deterministic Discrete-Event Simulation (Deterministic DES) models a system as a sequence of events occurring at precise, pre-specified times using fixed input parameters. Unlike stochastic DES, no probability distributions are sampled; every arrival, service time, and resource availability is known in advance, making runs fully reproducible and producing a single definitive output trajectory. |
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