Uncertainty assessment of spatial dynamic microsimulations

TL;DR

This paper evaluates the uncertainty in spatial dynamic microsimulations, highlighting that modeling choices often impact outcomes more than parameter uncertainties, and emphasizes comprehensive uncertainty analysis for better simulation design and communication.

Contribution

It applies variance-based sensitivity analysis to microsimulation models, revealing the significance of qualitative modeling choices over traditional parameter uncertainties.

Findings

Qualitative modeling choices have greater influence than coefficient uncertainties.

Simple summary measures do not fully capture model uncertainty.

Considering broader uncertainty sources improves simulation reliability.

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.