Causal and Agentic AI: Basics and Multidisciplinary Applications

This course investigates the application of causal and agentic AI across various disciplines.

Mental, social, and health-related human processes can all change and adapt over time. This course will present theories and findings from human-directed disciplines such as cognitive, social, health and neurosciences, to gain insight into underlying causal mechanisms and pathways of human processes.

These causal pathways can be modeled as causal agentic AI by the framework and the software provided. In the second week, an own project is addressed that may lead to a publication that can be finished later on in the course.

This course introduces a network-oriented modelling approach based on adaptive networks. This approach is useful for modelling social interactions and mental and health-related processes within their respective networks. These network models cover the dynamics of causal effects, changing causal connections and excitability or sensitivity thresholds. Higher-order adaptiveness is another topic covered in the course, which includes the role of metaplasticity and the extent to which plasticity occurs in the field of cognitive neuroscience.