Fuzzy Cognitive Maps (FCMs) are graph-based simulation models commonly used to model complex systems. They are often built by participants and aggregated to compare the viewpoints of homogenous groups (e.g., anglers and ecologists) and increase the reliability of the FCM. However, the default approach for aggregation may propagate the errors of an individual participant, producing an aggregate FCM whose structure and simulation outcomes do not align with the system of interest. Alternative aggregation methods exist; however, there are no criteria to assess the quality of aggregation methods. We define nine desirable criteria for FCM aggregation algorithms and demonstrate how three existing aggregation procedures from social choice theory can aggregate FCMs and fulfill desirable criteria, enabling the assessment and comparison of FCM aggregation procedures to support modelers in selecting an aggregation algorithm. Moreover, we classify existing aggregation algorithms to provide structure to the growing body of aggregation approaches.