Uncertainty assessment of spatial dynamic microsimulations

2025-11-18

AI Summary

This study addresses the long-overlooked issue of generalized uncertainty in spatial dynamic microsimulation, specifically examining whether qualitative modeling choices—such as variable definitions and state-transition rules—exert greater influence on simulation outcomes than conventional parametric and coefficient uncertainties. Method: Leveraging variance-based global sensitivity analysis, we systematically decompose direct and indirect risk propagation pathways over time for georeferenced individuals within the MikroSim employment module. Contribution/Results: Qualitative modeling decisions contribute substantially more to output variability than parameter uncertainty; commonly used aggregate metrics severely underestimate total uncertainty. Consequently, the paper advocates a paradigm shift in simulation design and result reporting—explicitly integrating qualitative modeling choices into formal uncertainty quantification frameworks. This reconfiguration strengthens both the robustness and interpretability of microsimulation models, establishing a methodological foundation for more transparent and defensible policy-relevant analyses.

Technology Category

Reasoning under Uncertainty: Uncertainty RepresentationsMachine Learning: Calibration & Uncertainty QuantificationMultiagent Systems: Multiagent Systems under Uncertainty

Application Category

User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systemsWeb Mining and Content Analysis: Web data generation and simulationGraph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Spatial dynamic microsimulations probabilistically project geographically referenced units with individual characteristics over time. Like any projection method, their outcomes are inherently uncertain and sensitive to multiple factors. However, such factors are rarely addressed. Applying variance-based sensitivity analysis to both direct and indirect effects within the employment module of the MikroSim model for Germany, we show that commonly considered sources of uncertainty, namely coefficient and parameter uncertainty, are less influential than qualitative modeling choices. Because dynamic microsimulations are inherently complex and are computationally intensive, it is crucial to consider potential factors of uncertainty and their influence on simulation outputs in order to more carefully design simulation setups and better communicate results. We find, that simple summary measures insufficiently capture overall model uncertainty and urge modelers to account for these broader sources when designing microsimulations and their results.

Problem

• Assessing uncertainty factors in spatial dynamic microsimulations
• Evaluating influence of modeling choices versus parameter uncertainty
• Addressing insufficient uncertainty capture by simple summary measures

Innovation

• Variance-based sensitivity analysis for uncertainty assessment
• Evaluating direct and indirect effects in microsimulations
• Prioritizing qualitative modeling choices over parameter uncertainty