In this thesis we present two different models of Complex Social Systems. The first model represents a vector-borne disease that takes place in a heterogeneous environment composed of areas of different types. Two populations take part in the epidemic process: humans and vectors. The population of humans moves around the heterogeneous environment. The idea of this model is to understand how the movement of people in the heterogeneous environment can affect the dynamics of the disease. The second model represents a Susceptible-Infected-Susceptible process on a social network. The population is represented as nodes, and the edges represent the possible transmissions between two people. We investigate how different topologies in the network affect the spread of the disease in the system. We simulate both models, and we perform a mathematical analysis of both of them. For the mathematical analysis we use an adapted version of the Random Heuristic Search framework, which was originally used for the understanding of Genetic Algorithms. In this thesis we investigate the predictability power of the mathematical approach.