## 2 FRAM and Decision Ladder Models' Development
### Decision ladder Models  
The functions from the FRAM model are used to populate the FRAM-Decision Ladder Matrix and the functions' aspects are stored as information requirements. We evaluate and map the functions to the appropriate information processing activities in the decision ladder and elicit the information requirements by expanding the prompts and generic keywords composed by [42]. The matrix is completed through multiple iterations of FRAM and decision ladder analysis. The final version of the matrix is shown in Table 2. In the following, we discuss the main insights from the decision ladder model, i.e., (1) the function-to-activity mapping rule, (2) complex functions with a higher number of activity-knowledge pairs, (3) the 'Goals' state of the decision ladder model, (4) the 'Execute' activity of the decision ladder model, (5) linear progression without shortcuts, (6) decision ladder model limitations. **(1) The function-to-activity/knowledge mapping rule. ** As shown in the matrix (Table 2), a function in FRAM is mapped into one or multiple pairs of activity-knowledge of the decision ladder. This mapping result is based on the following analysis principles. First, we created the FRAM model by following the FRAM guidelines by its inventor in [12], thus the identified functions are related to the FRAM modeling philosophies. To maintain FRAM modeling philosophies and the resulting systemic property, the functions cannot be modified without restrictions. Second, we did not find any scientific reason to keep a fixed mapping rule, for example, one function must be mapped into one pair of activities-knowledge. Keeping this kind of mapping rule can unnecessarily clutter the FRAM model. Third, we focused on our goal of information requirements elicitation, so we did not complicate the models. A limitation of our heuristic approach is that the results are highly dependent on the analysts. In our team, the main model developer has three years of FRAM modeling experience and the developer's work is supervised by an expert in this field with more than 30 years of experience. An example of a FRAM function and decision ladder mapping is shown in Figure 4 and the information requirements from the decision ladder analysis are listed in Table 3.  
**(2) Complex functions with a higher number of activity-knowledge pairs. ** Similar to the FRAM model analysis, complex functions are reflected in the matrix as having longer steps in the decision ladder. The functions that have a high number of activity-knowledge pairs are: 'F.11 Identify and evaluate the devices/facilities recovery measures', 'F.12 Identify and evaluate the measures to mitigate the occurred negative impacts', 'F.13 Evaluate the recovery measures', and 'F.14 Evaluate the alternative strategies', as shown in Table 2. These functions start from activation, for example, 'F.11 Identify and evaluate the devices/facilities recovery measures' is carried out after (1) the devices/facilities required for the strategy implementation are not available and need to be recovered, or (2) the operators decided not to proceed with the mitigation strategy because they believe that some devices/facilities are not in acceptable condition and need to be recovered. Then, the information processing steps in the decision ladder were followed linearly. Continuing the F.11 example, after activation, the operators observe the information related to the current strategy, the status of devices/facility involved, recovery measures, and other related information. Then, the operators proceed with identifying the current state and predict the consequences of the recovery measures on the power plant and the workload, and so on following the decision ladder. Functions F.11, F.12, F.13, and F.14 are also identified as high workload functions in the FRAM model analysis. **(3) Including the 'Goals' state of the decision ladder model.