Beleidsmodelling

Beleidsmodelling, often translated as policy modeling, is the practice of constructing models to analyze and compare the potential effects of public policies. It brings together theory, data and computational methods to simulate how policies influence outcomes such as health, income, mobility or the environment. The term is used in Dutch-speaking policy communities and in international collaborations.

Common approaches include system dynamics models that represent feedback loops and time delays; agent-based models that simulate the actions and interactions of individual actors; microsimulation for person-level outcomes; econometric and statistical models for causal relationships; optimization and cost-benefit analyses for resource allocation; and scenario analysis to explore alternative futures.

Modeling relies on empirical data and theoretical assumptions. Calibration and validation against observed data are important to establish credibility, while sensitivity analyses illuminate how results depend on uncertain inputs. The modeling process typically involves problem framing, model design, parameter estimation, validation, scenario development, policy experimentation, and the interpretation and communication of findings.

Beleidsmodelling is applied across domains such as climate policy, transportation and urban planning, health policy, education, social welfare and tax systems. It supports decision making by predicting potential outcomes, comparing policy options, assessing trade-offs, and communicating uncertainties to stakeholders. Ethical considerations include transparency, reproducibility, stakeholder involvement, and privacy. Limitations include model risk, simplifying assumptions, data gaps and the inherent uncertainty of future conditions.