Using fuzzy cognitive mapping and nonlinear Hebbian Learning for modeling, simulation and assessment of the climate system, based on a planetary boundaries framework

In the present work a fuzzy cognitive map for the qualitative assessment of the Earth climate system is developed by considering subsystems on which the climate equilibrium depends. The cognitive map was developed as a collective map by aggregating different experts opinions. The resulting network was characterized by graph indexes and used for simulation and analysis of hidden pattens and model sensitivity. Linguistic variables were used to fuzzify the edges and were aggregated to produce an overall linguistic weight for each edge. The resulting linguistic weights were defuzzified using the “Center of Gravity”, and the current state of the Earth climate system was simulated and discussed. Finally, a nonlinear Hebbian Learning algorithm was used for updating the edges of the map until a desired state. The overall results are discussed to explore possible policy implementation, environmental decision making and management.