Human AI

OVERVIEW

Human processes of all kinds are complex and adaptive. Mental, social, and health-related processes can all change and adapt over time with human behaviour. Thought-based processes can change as a result of learning, social interactions can evolve over time, and health-related processes are susceptible to change too. 

This course will present theories and findings from a wide range of disciplines, including various branches of cognitive, social, health and neuroscience, to gain insight into underlying mechanisms of human processes that can be exploited in human AI modeling and simulation. The various scientific theories form a factual basis for modelling the processes. We can understand these often adaptive mechanisms through causal relations and causal pathways, which we can model as networks. Using this theoretical framework and the software provided, students can easily simulate a variety of scenarios.

This course invites you to connect your findings with those of your peers and collaborate on research-based projects. During the second week, students will carry out activities that could lay the foundations for a publication that can be finished later on in the course.

LEVEL
Advanced Bachelor/Master

COURSE CONTENT

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. It also extends to integrated physical, mental, social, and health processes.

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.

Lectures will address the following topics:

• How emotions affect other processes and how we can manage them.
• The process of mirroring and the emergence of collective understanding and power.
• Mental and physiological models; how we carry out thought processes using internal mental models.
• The learning effects of different mental or physiological models and the control we have over the learning process.
• Social networks; the phenomenon of social contagion, and how relationships change within them. 
• Health-related network models.

The dynamics of such processes are modeled and simulated using a dedicated and easy to use modelling environment for Network-Oriented modelling; no prior programming knowledge is necessary.

The course consists of both daily lectures and work sessions.

We will assess students based on performance in assignments. 

In the second week of the course, we will present a final assignment. There’s a chance that this assignment would provide an opportunity for a student to submit their paper to an international conference, where they would present it and have it published. To this end, we will provide continued support following the completion of the course.

LEARNING OBJECTIVES

By the end of the course, students will be able to:

• Identify the different types of mental, social, and health-related processes.
• Understand how individual and social behaviour is influenced by the mechanisms identified in various fields of science (cognitive, social, and health) and neuroscience (affective and social).
• Design network models for adaptive mental, social and health-related processes.
• Perform simulations based on these models using the provided Network-Oriented modelling software.

TEACHING METHODS
Lectures, interactive sessions, discussion boards

TYPE OF ASSESSMENT
Short paper

WHO SHOULD JOIN
This course is intended for students or professionals interested in learning more about modelling and the computer simulation of mental, social, and health-related processes. We believe the course will be of particular interest to Ph.D., Master’s, or Bachelor students, though students from any discipline can appreciate the contents of the course. No prior programming or modelling experience is required.

If you have doubts about your eligibility for the course, please let us know. Our courses are multi-disciplinary and therefore are open to students with a wide variety of backgrounds.