Thesis

Modelling parameter interdependence in system dynamics : a data-driven Bayesian network approach to assessing uncertainties in simulations

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Awarding institution
  • University of Strathclyde
Date of award
  • 2026
Thesis identifier
  • T17628
Person Identifier (Local)
  • 201958018
Qualification Level
Qualification Name
Department, School or Faculty
Abstract
  • System Dynamics (SD) modelling supports decision-making by simulating and projecting key performance indicators (KPIs) of a system. To assess uncertainty in these KPIs, modellers typically vary model parameters and run multiple simulations. This process illustrates potential scenarios and generates KPI distributions that provide quantified measures of uncertainty through statistical analysis. However, parameters often do not vary independently. Studies across sectors have reported correlations and dependencies among parameters (Krefeld-Schwalb et al., 2022; Li and Vu, 2013). When varying parameters in SD models, whether these dependencies are accounted for can shape the combinations of parameter values, influence the distributions of projected KPIs and the derived insights. This issue has not been thoroughly addressed in the SD literature. To highlight the importance of parameter dependence, we present a copula-based experiment, modelling dependencies between SD model parameters in several different ways and comparing the resulting KPI distributions. The experiment demonstrates that both the strength of correlations and the structure of dependencies can affect KPI uncertainty. These findings motivate the adoption of more flexible approaches to adequately model such dependencies. The main contribution of this thesis is a method that models dependencies among SD model parameters using Bayesian Networks (BNs) to improve KPI uncertainty assessment. BNs provide a flexible framework and algorithms for uncovering complex conditional relationships from data and integrating them with expert knowledge. We apply the approach to an epidemic SD model, where dependencies among epidemiological parameters are estimated from a crosscountry COVID-19 dataset using a BN and validated against domain knowledge. The learned BN produces input for analysing KPIs of the epidemic SD model and yields uncertainty envelopes that differ from those generated by independent or single-copula priors. This study offers SD practitioners a practical way to incorporate empirically grounded multiparameter dependencies into their models, enhancing the defensibility of uncertainty assessments while keeping additional data-collection effort manageable. The proposed method contributes to both the SD and mixed-methods research literatures.
Advisor / supervisor
  • Howick, Susan
Resource Type
DOI

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