Abstract: Dynamical systems is a rich and broad field with interdisciplinary applications. Here I will talk about a network-based intrahost virus-antibody population model, a switch mechanism for bimatrix games and a social-climate model. In diseases like hepatitis C, the virus-antibody relationship is not one to one. Antibodies have a strong neutralizing effect on their corresponding virus but they can have a weaker neutralizing effect on other viruses. This cross-immunoreactivity relationship can lead to local immunodeficiency, where the patient’s immune system is not generating antibodies against certain viruses. We can model the virus-antibody population dynamics on a relationship network between the viruses. Simulation results already showed promising results and I found the smallest relationship network that would support robust and stable local immunodeficiency. Classical game theory studies games with fixed payoffs, while evolutionary game theory studies games where the payoff changes, which is more realistic for many social/biological systems. Even if the change is a simple switch between two environments, we can change the long term behavior of the system. Human behavior plays an important role in climate change and climate modeling can be rather complicated and involve numerous parameters. Here I build a simple model that couples forest growth with human behavior and show that we can still get rich dynamics over 200 years simulation of the model and get useful insight for policy making.