Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Policy Scenario Agent-Based Modeling× | Dynamique des systèmes× | |
|---|---|---|
| Domaine | Simulation | Simulation |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1990s–2000s | 1961 |
| Auteur d'origine≠ | Axelrod, R. and colleagues in computational social science | Jay W. Forrester |
| Type≠ | Simulation-based policy comparison | Continuous simulation / feedback modelling |
| Source fondatrice≠ | Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press. ISBN: 9780691015675 | Sterman, J.D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. Irwin McGraw-Hill. ISBN: 978-0072389159 |
| Alias | Policy ABM, Policy Scenario ABM, Scenario-Based ABM, PS-ABM | stock-flow modelling, Sistem Dinamiği (Stock-Flow Modelleme), SD modelling, feedback simulation |
| Apparentées≠ | 5 | 3 |
| Résumé≠ | Policy Scenario Agent-Based Modeling (PS-ABM) is a simulation method that uses agent-based models to evaluate and compare multiple policy scenarios. Heterogeneous autonomous agents interact under different policy regimes, and emergent system-level outcomes are compared across scenarios to inform evidence-based policy decisions. It is widely used in public health, urban planning, economics, and social policy research. | System dynamics is a continuous simulation method, developed by Jay W. Forrester at MIT in 1961, that represents a complex system through stocks (accumulations), flows (rates of change), and feedback loops. By expressing these relationships as coupled ordinary differential equations, it reproduces how policies, delays, and nonlinear feedbacks drive system behaviour over time — making it a cornerstone tool in policy analysis, organisational modelling, and sustainability research. |
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