**9. Summary and conclusions**

442 International Perspectives on Global Environmental Change

**Final state –eq. (2) Scenario (a)** 

AOSP 1.00 0.7103 0.8808 0.5 0.8800 0.2835 **Bd 1.00 0.0004 0.0005 0.5 0.0007 0.9974**  BW 1.00 0.7290 0.8069 0.5 0.8063 0.3024 CD 1.00 0.8113 0.9097 0.5 0.8868 0.3855 CW 1.00 0.8558 0.9721 0.5 0.9718 0.1635

D-Distrust to State 1.00 0.8284 0.9303 0.5 0.9295 0.3751

S- Urban Sewage 0 0.7332 0.8591 0 0.8572 0.3698 Si-Siltation 1.00 0.9169 0.9379 0.5 0.9378 0.0933 SLR 1.00 0.8314 0.8655 0.5 0.8653 0.1948 Sphi 1.00 0.9970 0.9953 0.5 0.9952 0.0061 Tourism 1.00 0.2055 0.1535 0.5 0.1520 0.7777 **ECOL 1.00 0.0026 0.0055 0.5 0.0056 0.9913** 

Table 3. Initial and final concepts' state after 25 iterations for Scenario (a) and (b).

DFS 1.00 0.9903 0.9974 0.5 0.9972 0.0174 HAB 0 0.9938 0.9986 0 0.9986 0.0162 HA 0 0.7297 0.3376 0 0.3346 0.2689 IA 0 0.5125 0.7095 0 0.7049 0.4638 ISP 1.00 0.7903 0.8367 0.5 0.8365 0.2359 LF 1.00 0.7103 0.8162 0.5 0.8161 0.4114 M- Mining 1.00 1.0000 1.0000 0.5 0.5000 0.5000 MC 1.00 0.9842 0.9924 0.5 0.9922 0.0235 MR 1.00 0.3199 0.4806 0.5 0.4808 0.5738 MSW 0 0.9664 0.9964 0 0.9950 0.0955 PPP 1.00 0.7270 0.8281 0.5 0.8280 0.4026 PSA 1.00 0.7581 0.8754 0.5 0.8737 0.3187 RAW 1.00 1.0000 1.0000 0.5 0.5000 0.5000 RP 1.00 0.6315 0.7144 0.5 0.7140 0.3678

**Initial values-Scenario (b)** 

**Final state – eq. (2) Scenario (b)** 

**Final stateeq. (5) Scenario (b)** 

**Final state –eq. (5) Scenario (a)** 

**Concepts** 

**Initial values-Scenario (a)** 

In this chapter, the FCM methodology was presented and analyzed for the elicitation and understanding of individual and collective knowledge, preferences and beliefs. The aim was to present to the reader both a theoretical underpinning of FCMs as well as a grasp of their empirical modalities.

A cognition model, like FCM, represents a system in a form that corresponds closely to the way humans perceive it. Therefore, the model is easily understandable, even by a nonprofessional audience and each parameter has a perceivable meaning. The model can be easily altered to incorporate new phenomena, and if its behavior is different than expected, it is usually easy to find which factor should be modified and how. In this sense, a FCM is a dynamic modeling tool in which the resolution of the system representation can be increased by applying a further mapping. The FCM methodology developed makes it possible, if the initial mapping of the risk factors and future prospects of marine ecosystem is incomplete or incorrect, to make further additions to the map, and to predict the effects of the new parameters considered.

FCMs have some specific advantageous characteristics over traditional mapping methods: they capture more information in the relationships between concepts, are dynamic, combinable, and tunable, and express hidden relationships (Kosko, 1986, 1992). The resulting fuzzy model can be used to analyze, simulate, and test the influence of parameters and predict the behavior of the system. Summarizing, FCM helps describe the schematic structure, represent the causal relationships among the elements of a given decision environment, and the inference can be computed by a numeric matrix operation. With FCM it is usually easy to find which factor should be modified and in which way.

To illustrate the FCM methodology, an empirical application for modelling lay people perceptions is presented. We describe the main features of a FCM exercise designed to elicit the Black Sea stakeholder views/ perceptions about the risks that the Black Sea may face in the future 20 years. A generic model for environmental management is constructed by augmenting the individual FCMs drawn by lay people-stakeholders from Ukraine. The graph theoretical indices were calculated out of the individual cognitive maps and the collective cognitive map produced by augmentation. A number of scenarios were run using the FCM inference process to enable us to understand the complex structure of the Black Sea problems and the risks mainly affecting its marine ecosystem. This knowledge is further used to design policies that contribute in environmental management. The results show its functionality and demonstrate that the use of FCMs is reliable and efficient for this task.
