## 3 Model to Quantify Human Uncertainty in Human-Centric Cyber-Physical-Social Systems  
The MMtQHU model was originally introduced in our prior work [16]. To enhance the clarity and flow of this paper, some key details from that publication are reiterated here. However, the complete and comprehensive description of the model, including its foundational principles, can be found in the original work. The MMtQHU model presents a layered framework designed to integrate a range of factors influencing decision-making processes within dynamic environments, particularly in CPSS. The model is organized into three core layers: natural context, social context, and personal context, each contributing to a comprehensive assessment of human uncertainty, as depicted in Figure 1. This human-centric approach enhances the model's adaptability, making it suitable for both current and future applications. The natural context layer includes environmental and situational factors, such as weather conditions, time of day, and traffic density, which are critical variables in urban CPSS environments. The social context layer examines societal norms, peer behaviors, and community expectations, leveraging probabilistic modeling to capture the inherent variability in human social dynamics. Finally, the personal context layer focuses on individual traits, preferences, and cognitive biases modeled using data from psychological assessments and personal histories. These layers work together to simulate varying levels of risk tolerance, decision-making speed, and adaptability, which are crucial for managing broader systemic uncertainties within CPSS. \begin{table}
\begin{tabular}{p{42.7 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
[27] & Handover in Radio Access Networks (RAN) with user behavior uncertainty & Distributed learning: adapts to changing network conditions & Focuses on technical uncertainties, does not consider human variability \\ \hline
[28] & Real-time resource allocation with unpredictable user demand & Dual mirror descent method: improved adaptability to non-constant demand & Limited in handling extreme variability in demand \\ \hline
[29] & Linguistic uncertainty in AI models versus human interpretation & Analysis of probabilistic uncertainty: impact on human-machine interaction & Does not address the impact of human behavior on system performance \\ \hline
[30] & Inference of human uncertainty in real-time system interactions & Proxy ensemble network: quantification of real-time human uncertainty & Does not explore how human decision-making affects overall system performance in CPSS \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cont. To illustrate the practical application of the MMtQHU model, a use case involving a VFH application operating within a 5G network slice is analyzed. VFH apps, such as Uber and InDriver, connect users requiring mobility with drivers offering vehicles in real-time. The process begins when a user selects a starting point and a destination, generating a route for the driver to follow after accepting the service. Ensuring the quality of service on these routes requires the availability of network resources, including the frequencies needed for handovers during the trip. However, the absence of penalties for drivers who deviate from the predefined route introduces uncertainty into resource planning, as route changes may stem from drivers' decisions to maximize profits or avoid certain areas, affecting resource allocation and network performance. The MMtQHU model establishes a reference architecture that systematically documents and analyzes the interaction between system components, as illustrated in Figure 2. Based on this architecture, the key elements necessary for implementation have been formulated and weighted with the aim of quantifying the qualitative uncertainty associated with drivers in the context of a VFH application.