FCMs to Find Knowledge in Complex Systems

Although the investigation of complex cognitive maps cannot be very easy, the graph theory from matrix algebra provides a suitable analytical framework. Empirical studies in social sciences are referred to social network analysis (SNA), which is considered a key technique in organizational studies. The first step is to put the map into a matrix form. The variables are listed both on the vertical axis and on the horizontal one forming a matrix, technically called the adjacency matrix [47]. 

It shows the existing connections between each couple of variables. By examining the adjacency matrix, it can be determined how stakeholders perceive the system. 

To analyze the features of a cognitive map, several social network indices can be calculated. SNA distinguishes among punctual indices, which refer directly to variable issues, and network indices, which describe the characteristics of the system as a whole. 

Two useful punctual indices are in-degree (iDv.) and out-degree (tOv.). The indegree shows the cumulative strength of connections (at.) entering the variable i 

Another punctual index is the centrahty (or total degree) [47] of a variable (C.). It describes the contribution of a variable in a cognitive map showing how connected the variable is to the other variables and the cumulative strength of these connections. This index is calculated as the sum of the variable in-degree and out-degree indices [47—49]  

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