## 2 Related Works
### Human-Centric Uncertainty in System Design
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Article** & **Approach and Uncertainty Model** & **Methods Used and Key Results** & **Limitations** \\ \hline
[17] & Stochastic uncertainty in resource allocation in 5G networks & Robust optimization: management of fluctuating demand & Does not incorporate human variability, limiting applicability in CPSS \\ \hline
[18] & Mathematical modeling of uncertainty in complex systems & Theoretical approach focusing on uncertainty in technical variables in engineering systems & Lacks practical application in dynamic, human-centric systems like CPSS \\ \hline
[19] & Uncertainty theory applied to human factors & Theoretical framework for quantifying human uncertainty & Does not demonstrate applicability in operational or dynamic systems \\ \hline
[20] & Classification of uncertainties in engineering models & Guidelines for managing technical uncertainties in engineering systems & Does not address human uncertainties in dynamic environments \\ \hline
[21] & Uncertainty modeling for stress prediction uncertainty in stress responses & Analytical model for predicting uncertainty in stress responses & Focuses on technical variables, lacking consideration of human variability \\ \hline
[23] & Adaptive control for networks with uncertainty in _network slice_ demands & Max-min optimization: robust guarantees under uncertain demand & Does not address the influence of human variability in CPSS \\ \hline
[24] & Dynamic resource allocation in the cloud with user demand fluctuations & Stochastic optimization: adaptability to changing demand & Limited to cloud environments, lacks integration of human factors influencing resource use in CPSS \\ \hline
[25] & Resource provisioning for _network slices_ with user fluctuations & Linear integer programming: high compliance with requirements under uncertainty & Does not integrate human variability in resource provisioning \\ \hline
[26] & Predictive video delivery with fluctuating network demand & Recurrence programming: efficient energy usage for adaptable delivery & Limited to video content, not generalizable to other CPSS applications \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of studies on resource management and optimization under uncertainty. This study addresses critical gaps in the existing literature by presenting and evaluating the MMtQHU model, a framework that quantitatively integrates technical, environmental, social, and human behavioral factors in a realistic setting. Unlike previous research that focuses exclusively on technical uncertainties or analyzes human factors qualitatively without rigorous quantification, the MMtQHU model provides a comprehensive and robust approach. By modeling the unpredictable nature of human behavior along with traditional technical factors, the MMtQHU model overcomes the limitations of previous models, which tend to ignore human variability or fail to provide a robust methodology for managing its impact on dynamic systems such as CPSS. In this study, the MMtQHU model is specifically applied to a vehicle-for-hire (VFH) scenario within a 5G network slice, where drivers' behavioral responses to urban environmental factors directly affect network resource allocation. Using detailed urban data from the Ingoldstadt traffic scenario and advanced simulation techniques, the study provides a practical assessment of how human variability influences network performance. This approach represents an advance over previous models, which have focused primarily on managing technical uncertainties without fully considering the unpredictable nature of human behavior.