## 1 Introduction and Backgrounds
### Earlier Works Related to FRAM and Decision Ladder  
**FRAM in the industrial domain and NPP. ** Industrial domain is the third-largest application domain of FRAM, in which it has been used for accidents and resilience analysis [13]. In the domain of NPPs' severe accidents, [28] developed a FRAM model capturing the Fukushima Dai-ichi 2011 accident. The Fukushima accident had been analyzed exhaustively throughout the years and even though [28]'s findings on the accident causes were not new, they showed the applicability of resilience engineering through FRAM for both retrospective and (limited) prospective analysis to expose the hidden risks. The authors of [29] tried to reduce the gap between work-as-imagined and work-as-done to improve the NPP's emergency procedures. This study found that FRAM can contribute to creating procedures for novel situations in NPP, especially in the early stages of procedure design. **FRAM for DSS and requirements development. ** FRAM has been used to develop DSS or elicit requirements since 2016, quite late from its first introduction in 2004. The authors in [15] applied FRAM to develop a DSS, together with a modified Analytical Hierarchical Process (AHP). Compared to our study, [15] elicited a small number of aspects or information requirements while we developed a comprehensive set of information requirements that can be used to develop various types of DSS for safety-critical domains. Moreover, although [15] showed a practical use of FRAM to develop a DSS, they assumed that the data to calculate the probability of the FRAM aspects were available. We considered the importance of identifying the source of information requirements, which is partly provided in the current study and shall be polished in future works. FRAM was applied after a work domain analysis using Abstraction Hierarchy in [23] to create systems requirements for a centralized information display system to support human decision-making. The final outcome of [23] was systems requirements and we couldnot analyze their information requirements in detail, however, we predict that our study shall lead to a higher number of information requirements through the combination of FRAM and the decision ladder. The feasibility of FRAM to identify software requirements in the healthcare field is explored in [22]. By following [PERSON]'s systematic approach for requirements engineering, the authors performed experiments using Business Process Modeling Notation and FRAM to develop software requirements. The experiments results showed that FRAM was able to extract a higher number of requirements, both functional and non-functional, especially, the requirements related to resilience properties. A lean business methodology called GUEST (Go, Uniform, Evaluate, Solve, and Test) is applied to develop a DSS in the automotive industry, together with a mathematical model to tackle the uncertainty, however, the cognitive aspects of the human decision-makers are not included [8]. **Decision ladder in the NPP domain. ** The research problem that led to the invention of the decision ladder was human operators' works analysis in industrial process plants [30]. Thus, an NPP is among one of the most common application domains of the decision ladder. Vice versa, NPPs play a major role in nurturing the scientific and practical research of the decision ladder, under the cognitive systems engineering's umbrella [31]. Research about decision ladder application for NPPs is still carried out steadily, for example, the attempt to define the transformative new state of NPP in the United States was published in 2021 [32]. **Decision ladder for information requirements development. ** Decision ladder has been frequently applied to conduct cognitive task analysis, which is an effective method for deriving interface design concepts and requirements [33]. Consequently, the decision ladder has been used to elicit requirements for quite some time. We observed that most of the studies focused on design requirements from the human users' point of view.