Introduction to Complexity Science

Overview

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This course explores the features of complexity science. Our world is connected by an abundance of complex systems. Across all levels of organizations from physical, biological world to the social world, we may think of the connectivity between individual elements and how they interact and influence each other. For example, how humans transmit pandemics within a group, how cars interact in the traffic system and how networks connect in governmental organizations. Although these systems are diverse and different, they have surprisingly huge features in common. In the past several decades, the study of complexity science has been increasing. It is widely acknowledged that an innovative, integrated and analytical way of thinking is essential for understanding the complex issues in the human societies. In this course, we will aim to give everyone a comprehensive introduction of the complex systems, to talk about the resilience, robustness and sustainability of the systems and to learn basic mathematical methods for complex system analysis, for example regime shifts and tipping points, the agent-based modelling, the dynamic and network theories. Most importantly, we will implement the theories into practical applications of cities and health to help students gain practice in complex systems way of thinking. This course is co-developed by Associate Professor Cheong Siew Ann, Professor Stephen Lansing and Professor Peter Sloot between 2014 and 2020 at the Complexity Institute, Nanyang Technological University, Singapore.

Syllabus

Introduction to Complex Systems

This module introduces complex systems, explaining their evolution and societal applications. You will understand why a basic grasp of complexity science is vital. To gain practical experience, you will complete two Jupyter Notebook exercises: the Nagel-Schreckenberg traffic model and the Game of Life. This foundational module prepares you for deeper analysis of complex phenomena.

Robustness, Resilience, Sustainability

This module defines robustness, resilience, and sustainability. You will analyze case studies demonstrating these attributes, understanding their importance in real-world systems.

Regime Shifts and Tipping Points

This module examines regime shifts and tipping points, exploring their applications in forecasting. You will understand how these critical changes impact complex systems and learn to identify their indicators.

Introduction to Agent-Based Modeling

This module introduces Agent-Based Modeling (ABM), covering its principles, function, and application. You will gain practical experience with a Jupyter Notebook exercise on Schelling’s Segregation Model, understanding how ABM helps simulate complex social phenomena.

Introduction to Static Complex Network

This module examines complex networks and their attributes, then explores various network models. You will apply this knowledge with a Jupyter Notebook exercise on epidemics within complex networks, understanding real-world health and social dynamics.