Generalised fuzzy cognitive maps: Considering the time dynamics between a cause and an effect

Fuzzy Cognitive Maps (FCMs) have been used to quantitatively model the dynamics of complex systems and predict their behaviours. However, they are usually unable to address the issues arising from time lags between causes and effects. Accordingly, Generalised Fuzzy Cognitive Maps (GFCMs) have been introduced to overcome this problem. This article deals with a breed of GFCMs that addresses time lags between cause(s) and effect(s), demonstrated by a case-study that deals with the social, economic and technological consequences of heavy rainfall in Kampala, Uganda. The results show that the inclusion of time lags alters both, the final steady-state values of the social, economic and technological consequences of heavy rainfall and the time taken to stabilise. Thus, the inclusion of time lags increases the reliability of GFCMs as a means to quantitatively model the dynamics of complex systems.