## 1 Introduction  
Cyber-physical-social systems (CPSS) represent a significant advancement over traditional cyber-physical systems by integrating physical and digital components with human interactions and social dynamics. These systems have become increasingly critical for addressing complex challenges across various domains, i.e., challenges that conventional approaches are often ill-equipped to manage effectively [1; 2; 3]. The integration of the social dimension, however, introduces significant uncertainty, as human behavior is inherently variable and subjective, complicating the prediction and efficient management of resources [4; 5; 6]. As CPSS increasingly encompass diverse human behaviors and social interactions, it becomes evident that traditional methodologies are inadequate for managing the resulting qualitative uncertainties. This complexity underscores the need for multidisciplinary frameworks capable of addressing the dynamic interplay among cyber, physical, and social components [7; 8]. Traditional approaches often prove insufficient in dealing with these uncertainties, leading to inefficiencies in service delivery, particularly in dynamic environments like 5G networks [9], where demand and network conditions fluctuate rapidly. Such constraints not only reduce system efficiency but also compromise the quality of service in critical applications, including real-time resource allocation in densely populated urban areas. Furthermore, the inability to accurately predict human behavior in highly variable situations can result in either underutilization or overload of network resources, adversely affecting user experience. This issue is particularly acute in applications like autonomous vehicles, where unexpected driver behaviors can destabilize the network and create bottlenecks [10]. Additionally, the lack of integration of specific, representative data in current models limits systems' ability to adapt to the unique characteristics of different urban environments, hindering effective resource management strategies in complex, dynamic scenarios [11; 12]. To address these limitations, recent research has developed multidisciplinary frameworks aimed at better integrating human behavior into CPSS modeling [13; 14]. These frameworks acknowledge the complex interactions among cyber, physical, and social components, emphasizing the need for CPSS to evolve adaptively in response to dynamic changes [15]. Nonetheless, managing uncertainty within these systems remains a central challenge, particularly in ensuring the reliability and validity of decisions made under uncertain conditions. This study extends our previous work that introduced a model aimed at quantifying human uncertainty in human-centric CPSS. Now formally designated as the multidisciplinary model to quantify human uncertainty (MMtQHU), this model integrates a broad spectrum of contextual factors into a situational analysis to enhance the adaptability and overall performance of CPSS in the presence of unpredictable human behavior [16]. The MMtQHU is a conceptual framework designed to quantify and manage the uncertainty generated by human behavior in CPSS. Human actions, often unpredictable and influenced by a variety of social, personal, and environmental factors, introduce qualitative uncertainty that can significantly affect the performance of technological systems. The model operates by incorporating three main contextual layers:  
* Natural Context: Accounts for environmental factors such as time and location, which can influence both individual and collective behaviors. * Social Context: Includes institutional, organizational, and social network influences, recognizing that people's decisions are shaped by their social environments and interactions with others. * Personal Context: Focuses on personal data and characteristics, including personality traits and information collected through technological devices, which affect individual decision-making processes.