A PROBABILISTIC ANALYSIS FOR MENTAL HEALTH PROBLEMS: EVALUATION OF POLICIES FOR MANAGING DISTRACTED INDIVIDUALS

Mental health problems are among the problems that negatively affect people's daily lives. Depression, post-traumatic stress disorder and anxiety disorder can be given as examples of these problems. The mind of a person exposed to such problems cannot efficiently perform basic functions such as interpreting, synthesizing and understanding what is read. Such problems are addressed with scientific analysis and mathematical models are developed for them. In this paper, we define a class of mental health problems where the goal is to effectively manage distracted individuals that are required to complete specific tasks. The problem has the property that individuals are classified based on the level of distraction and face distraction with certain probability. We perform probabilistic analysis to evaluate different policies that can be implemented by the respective authority to which distracted individuals are responsible for. Numerical experiments reveal that the policy imposing task-related assignments to individuals and offering them the recompense of high-reward and low-penalty significantly outperforms other policies in all the scenarios we examined. This study will pioneer the mathematical modelling of mental health problems.