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Policy Scenario Agent-Based Modeling

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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Policy Scenario Agent-Based Modeling — Comparative policy evaluation using agent-based simulation

Taksonomisk metoderegistrering · process-pipeline / simulation

Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press. · ISBN 9780691015675
Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99(S3), 7280-7287. · DOI 10.1073/pnas.082080899

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Same method familyAgent-Based Modelingmachine-suggested · Relational suggestion, not evidence.See alsoMONTE-CARLO-SIMULATIONmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketPolicy Scenario Analysismachine-suggested · Relational suggestion, not evidence.Taxonomic bucketPolicy Scenario System Dynamicsmachine-suggested · Relational suggestion, not evidence.Same method familySystem Dynamicsmachine-suggested · Relational suggestion, not evidence.