Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Aģentu bāzēta scenāriju analīze× | Politikas scenāriju aģentu modelēšana× | |
|---|---|---|
| Nozare | Simulācija | Simulācija |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads | 1990s–2000s | 1990s–2000s |
| Autors≠ | Axelrod, R.; Schoemaker, P. J. H. (combined lineage) | Axelrod, R. and colleagues in computational social science |
| Tips≠ | Hybrid simulation–scenario method | Simulation-based policy comparison |
| Pirmavots≠ | Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press. Princeton, NJ. ISBN: 9780691015675 | Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press. ISBN: 9780691015675 |
| Citi nosaukumi | ABSA, ABM scenario analysis, agent-based scenario planning, scenario-driven ABM | Policy ABM, Policy Scenario ABM, Scenario-Based ABM, PS-ABM |
| Saistītās≠ | 4 | 5 |
| Kopsavilkums≠ | Agent-based scenario analysis embeds agent-based simulation models inside a structured scenario planning framework. Researchers define two to four contrasting future scenarios, configure agent populations and environmental rules to reflect each scenario's assumptions, run the simulation under each condition, and compare emergent outcomes. This makes it possible to explore how decentralized individual behaviors aggregate into system-level consequences under radically different futures. | 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. |
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