Aim of the course
The aim of this course is to introduce complexity science to master’s students, helping them achieve a good understanding of what complex systems are and why they are relevant to interpret the world from a scientific perspective. Moreover, this module aims to introduce the concept of sustainability of the systems, their resilience and robustness.
The “Introduction to Complexity Science” course is designed to explore the features of complexity and its science. The main characteristic of this module is to allow students to understand the basics of complexity and how everything in our world is connected through complex systems.
The study of complexity is increasingly being researched and taught within university courses given the many complex issues society has been facing. What has been witnessed in recent years, especially with the climate and biodiversity crises, is that individual elements of a system influence and depend on each other. And these interactions involve additional different systems, displaying common features. Complex systems knowledge is considered innovative and useful to understand the complexity of our human society, as well as the natural world, and how everything is interrelated. Studying complex systems is therefore crucial for a better understanding of our world.
This course is designed as a self-directed study, and research. During the course, some examples of practical applications of cities and health are shown, so as to give students some practical material to practice complex systems ways of thinking.
The module is divided into the following lessons:
- Introduction to complex systems: what complex science is, how it has evolved and why it is relevant.
- Introduction to sustainability: what is sustainability, why it is relevant in the study of complexity, and how are the two linked.
- Introduction to Planetary Boundaries and exploring the biodiversity and climate crises.
- Resilience, robustness, and sustainability through systems thinking.
- Regime Shifts and Tipping Points applied to forecasting.
- Introduction to research methods when working with complex systems (GIS, quantitative and qualitative research methods)