Functional components used in agent-based models of mental illness progression and intervention

![CDATA[As computational models are becoming more common in psychology and psychiatry, there is significant demand for reusable, well understood and tested components to be used in these models. In this presentation we will discuss the technical aspect of developing such components of varying complexity and expressiveness. We will provide examples based on Capability, Opportunity, Motivation, Behaviour (COM-B) model, logistic functions, law of diminishing marginal returns (economics), spring mass (system) based on Hooke’s law, and combined procedure of network structure estimation and network description (Borsboom et al., 2021). We will examine and demonstrate the most important properties of such models when they are used as part of Agent Based Models developed in AnyLogic. Firstly, we will demonstrate the use of the COM-B model as an overarching framework for agent-based models in the mental health domain. Secondly, we will examine the use of generalized logistic function (Richard’s curve) as a limit for the change in measures such as Social and Occupational Functioning Assessment Scale for diagnoses such as neurodevelopmental disorders or brain injury which is then extended to a spring mass model that includes additional representation of rebounding effect of the discontinuation of an intervention. Thirdly, we will demonstrate an implementation of the law of diminishing marginal returns as the model of the effect of an intervention to promote positive mental health. Finally, we will show the use of network structures to create a representation of occurrence of major depression by using partial correlations between symptoms (Van Borkulo et al., 2013).]]