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

There is no hard limit, but practical considerations usually constrain studies to 3–8 scenarios. Each scenario requires its own set of replications, so computational cost scales linearly. More scenarios are feasible with Latin hypercube or fractional factorial designs over policy parameter spaces.

Standard practice includes face validation (do agent behaviors match expert intuition), empirical validation against historical data (does the baseline scenario reproduce observed trends), and structural validation (do model mechanisms match known causal theory). Sensitivity analysis is also essential — scenario rank-orderings that are robust across uncertain parameters inspire more confidence.

Policy Scenario ABM is a specific application pattern of ABM where the explicit goal is comparative evaluation of defined policy alternatives rather than exploratory model analysis. The distinction is partly one of purpose and study design: scenarios must be pre-specified, the comparison must be systematic, and outcome metrics must be policy-relevant.

NetLogo is widely used for educational and medium-complexity models. Repast Simphony and Mesa (Python) are popular for research-grade studies requiring custom extensions. GAMA platform supports spatially explicit geographic agent models. Large-scale epidemiological policy ABMs often use custom C++ or Python frameworks.

Choose system dynamics when the key mechanisms operate at aggregate stock-and-flow level without meaningful population heterogeneity, when analytical tractability is important, or when the team lacks ABM expertise. PS-ABM is preferable when individual heterogeneity, network effects, or adaptive behavior are central to how the policy works.