Why do some people suffer from mental illness after a disruptive, stressful life event, while others seem to bounce back relatively rapidly from such adversity without developing (lasting) psychopathology? The ability to return to normal psychological functioning despite facing adversity is called resilience. A better understanding of how to strengthen resilience in people across different situations is vital to improve mental health and hinder the further development of mental disorders. The work in this dissertation takes a complexity approach to propose novel ways in which resilience can be investigated. Existing complexity models of mental health are combined with simulation modeling to develop a novel framework for investigating psychological resilience. The complexity models originate in the network theory of psychopathology, which proposes that mental disorders act as complex systems organized in a network of interconnected symptoms. These symptom networks can be in a healthy state, meaning that most symptoms are absent, or evolve towards a disorder state, in which many symptoms are present. By studying the dynamics of these networks and adding simulated clinical interventions – which pull the network towards a healthy state – or stressful perturbations – pulling the network towards a disorder state, it becomes possible to investigate how the symptom network may behave under different conditions. The proposed framework further expands the symptom network models to accommodate behavior from the resilience literature. This includes adding protective factors and risk factors to the networks, which help or hinder a person in maintaining good mental health. As such, this dissertation presents a framework to investigate psychological resilience from a complex systems perspective.