## 3 Methodological Approach
### Fuzzy Cognitive Map  
FCMs are mathematical models that represent relationships and interactions between various concepts or variables. These models use fuzzy logic, which help us to handle the inherent uncertainty in human decision making, offering several advantages. They assist in understanding the complex nature of the analyzed systems, providing insights into the contributing factors. By employing FCMs, we can make informed decisions, leading to the development of effective management measures. Specifically, FCMs offer several distinct advantages over other methodologies commonly used for risk assessment. Firstly, FCMs excel in capturing and representing complex, interconnected relationships among various risk factors and variables, making them well suited for modeling the complex nature of real-world systems, especially for cases lacking in statistical data [36]. This inherent capacity for modeling interdependencies sets FCMs apart from linear or deterministic models. Additionally, FCMs can incorporate expert knowledge and qualitative data, assisting managers and companies in attaining organizational goals by pinpointing control points and root causes, guiding risk management, and facilitating effective communication [37]. FCMs also ease the exploration of dynamic scenarios and \"what-if\" analyses, providing decision makers with a powerful tool for assessing the impact of various interventions  
Figure 1: Methodological steps.  
and policy changes [38]. Furthermore, their advantages of strong interpretability, logical reasoning capability, and intuitive knowledge representation [39] allow for adapting FCMs to various domains, making them a versatile choice for risk assessment in diverse fields. As previously observed, FCMs can be beneficial in the field of occupational stress risk evaluation in assessing risk interdependencies. FCMs allow us to evaluate how different stress risks interact and influence one another, providing a comprehensive understanding of how these risks are interconnected within an occupational setting. Another useful issue can be predicting stress outcomes, as FCMs can simulate various scenarios and predict how interventions or changes in stress risk factors may impact occupational stress outcomes. This aids in the development of targeted measures for stress prevention and management. As reported by [PERSON] et al. [40], a significant obstacle in constructing FCMs lies in maintaining the incorporation of human expertise while adapting the segments of the network that require learning from historical data. In our approach, we overcome this challenge by focusing on evaluating the interdependence of risks rather than relying on their specific values. Instead of assigning precise numerical values to risk factors, we assess the strength of their relationships within the FCM, allowing us to capture the qualitative aspects of risk interdependence while preserving human knowledge. By concentrating on the strengths of these relationships, we reduce the necessity to adjust parts of the FCM solely based on historical data, which can be limited and subject to change. This approach ensures that the FCM retains the insights and expertise of human contributors while incorporating the dynamic nature of risk interdependencies.  
For more detailed FCM applications, please refer to [4; 41; 42].