# Systemic Evaluation of PSS in the Early Concept Phase Using Graph-Based Reasoning
## 4 Solution Approach
### PSS Graph Elicitation and Analytic Reasoning (GEAR) Method
Experts discuss each PSS function with respect to the following:  
* Procedural Flow: Understanding how the PSS should operate, identifying key steps that mark important achievements, and determining possible scenarios and outcomes;
* Critical Factors and Properties: Identifying technical, economic, social, or legal factors that impact the functioning of the PSS and present a possible challenge to development and operation as they are a critical prerequisites or potential disruptors. Narrowing down the considered influences requires a shared understanding of their importance, which is established through discussions among the domain experts in the workshop. A common view of the PSS concept is thereby formed, and a consensus on the prioritization of influences to be considered is sought, which can be supported by consensus-building methods (e.g., the Delphi method). Detailing of the causal chain continues until further detailing adds no new information due to ambiguity or a lack of understanding (i.e., when the level of understanding is less than 3). This helps identify critical knowledge gaps among PSS engineers. Relationships are documented using causal maps, highlighting procedural and dependency causalities. During the workshop, all influences to be represented in the graph model have to be documented (e.g., as a causal map [74]), highlighting the procedural and dependent causalities and capturing their semantic meaning as clearly as possible. A thorough documentation of the discussions enables the people conducting the method to revise this a priori knowledge during symbolic modeling in the next step. The relationships are qualitatively discretized based on the semantic understanding captured during workshops. This involves breaking down the discussed scenarios into meaningful categories, intervals, or binary statuses. The discretizations are then validated with experts, and deterministic or probabilistic mapping is performed. Deterministic relationships are modeled using available data or formulas, while probabilistic relationships rely on expert assessments or data-driven probabilities. Knowledge gaps identified during the process are represented using index nodes linked to best-/worst-case scenarios. Relationships are modeled digitally using tools for Bayesian networks, and semantic context is added as meta-information to improve the traceability and understanding of each relationship. Relationships are represented in a digital graph using tools like BayesianLab, GenNie, or Netica for Bayesian networks. Since the symbolic format lacks semantic context, it might be hard to understand. To address this, semantic interpretations and knowledge sources (from simulations, experts, etc.) are documented as meta-information for each relationship, improving traceability and contextual understanding. Step 3: Reasoning Analysis  
The goal of the reasoning analysis is to determine how well the Evaluation Criteria (EVC ) is expected to be achieved by the PSS concept, considering different variations and knowledge gaps. The reasoning analysis focuses only on feasible and desired PSS concept variations. Variations deemed infeasible are excluded by setting the highest-level index node in the concept variation layer (CVL) to \"feasible\". This setting propagates through the graph, updating probabilities across all related nodes in the CVL and evaluation logic layer (EVL) via inference algorithms, ensuring that only combinable PSS element variations are analyzed. Similarly, specific PSS concept variations can be selected by setting the posterior probability of respective PSS element manifestations. Once these probabilities are updated, the analysis focuses on the chosen variation, propagating its effects through the evaluation logic to determine its impact on EVC achievement. The same method applies to evaluating best-/worst-case scenarios of knowledge gaps. By setting probabilities to 100% for either the best- or worst-case scenario, the impact of individual knowledge gaps on the EVC can be assessed.