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International Perspectives on Global Environmental Change Part 16

Tham khảo tài liệu 'international perspectives on global environmental change part 16', khoa học tự nhiên, công nghệ sinh học phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Using Fuzzy Cognitive Mapping in Environmental Decision Making and Management A Methodological Primer and an Application 439 Fig. 4. The collective fuzzy cognitive map of Ukrainian stakeholders. 440 International Perspectives on Global Environmental Change Fig. 5. The collective FCM with most central variables and strong connections. Table 2 gathers the calculated graph theory indices for the collective map and the sum of the 29 individual maps. Indices Individual Maps Collective FCM Maps 29 1 Variables N 7.86 1.574 26 Number of connections W 11 6.513 145 No. of transmitter variables T 2.21 1.544 2 No. of receiver variables R 1.96 1.267 3 No. of ordinary variables O 3.21 1.445 21 Connection Variable W N 1.33 0.617 4.577778 Complexity Receiver Transmitter 1.02 1.198 1.5 Density D W NA2 0.167 0.065 0.101728 Hierarchy index h 0.017 0.005 0.012944 Table 2. Average SD graph theoretical indices of the individual FCMs and the indices of the collective FCM. The collective FCM was then used for analyzing system behaviour and to run management simulations. Simulations were generated using both inference equations eq. 2 and rescaled eq. 5 by taking the product of the vector of initial states of variables Ao times the square Using Fuzzy Cognitive Mapping in Environmental Decision Making and Management A Methodological Primer and an Application 441 matrix E of the collective map. Simulations were generated using the five steps described previously implemented in Matlab R2008a environment for Windows. At first we determine the steady state condition of the system s convergence before we consider any management options. We are doing this in order to be able to see the perceived tendency of the Black Sea ecosystem based on the collective cognitive map and test for its internal coherence. For this purpose we run the generic FCM model with 50 different random initial states for all variables between 0 and 1 drawn from a uniform distribution. In all of these nomanagement simulations