## 1 Introduction and Backgrounds  
Complex socio-technical systems consist of nonlinear and dynamic interactions of machines, information technologies, and human workers as individuals and as an organization [1; 2; 3]. Internal and external interactions between the system's components cause the inherent possibility of accidents that can progress in novel, unpredictable, and dynamic ways in a complex socio-technical system. Nuclear power plant (NPP) is a type of complex socio-technical system, consequently, the possibility of accidents is inevitable. In this study, we are interested in the most critical type of accident at NPPs, called the severe accident, which is when the NPP damage has reached the nuclear core, causing a high probability of a dangerous nuclear radiation release to the resident and their environment. The circumstances of severe accidents are unstable and highly uncertain, which makes severe accident mitigation efforts different from other accident management [4]. During the NPP severe accident mitigation, the operators are required to perform frequent decision-making under highly stressful situations to maintain adaptive strategies as the accident unfolds. A common approach to support the decision-making during severe accident management in an NPP is to provide a set of Severe Accident Management Guidelines (SAMG),typically as documents. SAMG acts as the minimum support provided to the operators and the provision of SAMG is mandated through the NPP safety regulations, for example, by the Korea Hydro and Nuclear Power in South Korea. Compared to other NPP's accident management procedures, such as the Emergency Operating Procedures, SAMG is less disciplined, namely, it contains a range of abstract task suggestions, demanding a high cognitive workload from the human decision-makers [4]. Therefore, a Decision Support System (DSS) shall be highly useful to support effective and safer decision-making under high pressure and turbulent conditions [4; 5; 6]. To support the decision-makers effectively, DSS should provide the required information by the operators through a holistic vision of the system [4; 5; 6; 7; 8]. In this study, we aim to identify the information requirements required for managing severe accidents at NPPs using a systemic system modeling method and a human decision-making process analysis method. The systemic viewpoint allows for the understanding of a system, its components, and their interactions to understand the complex socio-technical systems comprehensively [9; 10; 11]. Functional Resonance Analysis Method (FRAM) is a system modeling method that adopts a systemic viewpoint to analyze the system based on the functions and the dynamic interactions between the functions [12; 13]. FRAM is proved as a valuable method for modeling complex socio-technical systems such as the NPP's severe accident management [13; 14; 15]. Moreover, we also applied the decision ladder to understand the fundamental human's decision-making processes in terms of data-processing activities and states of knowledge, as well as the users' interactions with the DSS [16; 17; 18; 19]. We developed FRAM and decision ladder models of severe accident management situations using SAMG as the main source of data. Each model is created to determine the information requirements for the DSS. The critical and large-scale damage from NPP severe accidents require stronger counter-measures than SAMG documentation. Thus, the interest in DSS development designed for managing severe accidents is increasing. In this study, we do not use the term DSS to refer to an information system or software, instead, we consider various forms of tools that serve the goal of anticipating and managing severe accidents. Such DSS can alleviate the operators' cognitive burden from intensive decision-making during severe accident mitigation efforts to increase the probability of effective and safe decision-making results.