## 3 Research and Results
### MCDA Results  
In this study, five computational experiments were conducted. In the first scenario, the research group consisted of thirteen vessels. In the remaining scenarios, the groups included between two and four vessels. This approach made it possible to demonstrate different ways of interpreting the results depending on varying conditions. The results of the MCDA for the examined set of ships are presented in the form of a PROMETHEE complete ranking, based on the net flow values computed for each alternative. The alternatives are ranked according to their aggregated preference scores, which reflect the degree to which they meet or fail to meet the established evaluation criteria. A higher net flow value (closer to +1) indicates a greater concentration of negative or suspicious characteristics in the corresponding vessel, as it is less preferred relative to others in the set. This ranking structure facilitates the efficient identification of outlier ships that deviate significantly from the normative behavioral pattern. In the first scenario (Figure 6), a significant concentration of alternatives is observed at the lower end of the net flow axis, with minimal pairwise preference differences. This part represents ships that exhibit a low degree of compliance with the evaluation criteria and can therefore be classified as non-suspect (or safe) at the current time. Above this group, four alternatives are positioned at intermediate net flow values and are more widely spaced, indicating varying degrees of deviation from the baseline group. Alternatives A7 and A6 occupy mid-ranking positions and may be interpreted as vessels of heightened risk, based on their partial compliance across multiple criteria. Alternatives A3 and A10 are located at the upper end on the net flow scale. These outlier alternatives display significantly higher aggregated preference indices and form a distinct suspect group, suggesting potential anomalies or elevated operational risk. The results can also be visualized using supplementary methods that enable a rapid assessment of which individual criteria, or groups of criteria, had the most significant influence on the evaluation of a given alternative. In other words, they help identify the specific reasons why a vessel is classified as suspect. For this purpose, the PROMETHEE rainbow (Figure 7) visualization is applied, as it allows for the decomposition of the global preference flow into partial contributions from each criterion. Visualization enables the identification of a subgroup of potentially high-risk vessels (A3, A10, A7, A6), based on the intensity and structure of criterion-level contributions. In contrast, the remaining alternatives (A9, A8, A2, A1, A5, A12, A13, A11, A4) form a baseline group, characterized by  
Figure 5: Recorded ship routes in the Polish EEZ. lower cumulative scores and less pronounced indicators of non-compliance. An individual evaluation of each alternative is also possible. The upper section of the PROMETHE rainbow graph displays the criteria numbers that contributed the most to the total score assigned to a given alternative, indicating the presence of undesirable or suspicious features. The lower section shows criteria numbers with a lower impact on the alternative's final assessment. In the case of alternative A3, the most influential criteria include C7, C8, C4, C1, C2, and C6, which collectively contributed the largest share of points. The criteria are grouped and color-coded by criteria group. For A3, the dominant contributions are observed in the purple group (irregularities in vessel registration). In the next step, the profile of the selected alternatives can be analyzed (Figure 8). Criteria that contributed to the overall score are represented as upward-facing bars. Groups of criteria are distinguishable by color. The net flow value is indicated by a gray horizontal line.