Abstract for: Addressing Methodological Uncertainties in System Dynamics: Insights from Colombia's Electricity Model

System dynamics models often face methodological uncertainties from assumptions, simplifications, and structural choices. These uncertainties influence model reliability, yet they are rarely addressed systematically for methodological uncertainties. This study explores their impact in an electricity investment model, assessing different investment functions to enhance SD modeling practices. The findings contribute to improving model robustness for better decision-making​. The study employs a multi-model approach using Moxnes’s interfuel substitution model, testing four investment function representations. Calibration is performed with the Powersim optimizer, minimizing mean squared error across historical data. A cross-checking procedure evaluates robustness, assessing how different investment functions affect model behavior. This methodological framework helps quantify and mitigate uncertainty in SD models​. Calibration reduces parametric uncertainty but does not eliminate methodological uncertainty. Different investment functions yield comparable historical fits but diverge in projections, indicating multiple valid representations of system behavior. Cross-checking reveals that parameter adjustments alone do not ensure robustness, emphasizing the need to consider methodological choices in model validation. Overfitting risks and narrative biases in SD models are highlighted​. Methodological uncertainty is an inherent feature of SD models rather than a flaw. This study shows how different modeling choices influence outcomes and decision-making. By systematically evaluating alternative structures, modelers can enhance transparency and reliability. Future research should explore broader applications, integrate adaptive modeling techniques, and refine uncertainty quantification to improve SD model credibility in policy analysis​. For editing