Complexity Science is a set of concepts and tools that helps us to understand Complex Systems.
Examples of Complex Systems are:
What do these examples have in common?
Complex Systems are composed of many parts that are constantly interacting with each other. As a result, these system are:
The whole becomes more than the sum of its parts. This means that the interaction between the individual parts creates a "whole" which has its own dynamics, properties and order not present in the individual parts. This order was not intentional, but emerges spontaneously from the interaction between the parts. Moreover, the systems is then able to maintain itself, meaning it is self-organizing. A classic example of this can be witnessed with ant hills: click here to see the video "Emergence – How Stupid Things Become Smart Together".
Non-linear & dynamical
Complex Systems do not behave in an orderly, constant, linear fashion. Instead, they change phases or states jump between states. Imagine what happens when you break a tree branch. Even if you are applying the same amount of pressure the entire time, it will suddenly snap. It will hop between the state of being whole to one of being broken. Furthermore, this non-linearity entails that even a small change to the system could have a major impact down the line. This phenomenon is also often referred to as the butterfly effect: click here to see the video "Chaos: The Science of the Butterfly Effect.
Given the large number of factors that are influencing a Complex System, it is extremely hard to predict what it will do / what will happen to it next. All parts interact with each other; within a specific environment; and based on a given context. For example: When working with or conducting research on humans, the needs, thoughts, behaviors, and dispositions are influenced by countless factors. No two people or interactions are ever exactly the same. Since I cannot possibly know all the factors at play and it is impossible to recreate two exact same situations, it is likewise impossible to predict any exact outcome. Therefore, models of complex systems are developed to learn how to manage the system, not necessarily predict its future states.
Complex systems are constantly adapting changes. Change is the only constant in this world and systems must adapt, and along the way, develop resilience. Resilience is the capacity for a system to bounce back to its habitual state after perturbation, or as the capacity for a system to adapt to perturbations towards a different state. In order to be resilient, a system must distribute its stability across its components.
To see a simple overview of what complex systems are, check this video out: What is a Complex System?
How can we best grasp, understand, and explain these systems?
This is what Complexity Science aims to answer. Theories and methods within complexity science stem from all sort of disciplines and have led to the development of 4 main theoretical frameworks:
Self-organization theory (rooted in concepts from physics and information theory like entropy, synchronization and concepts from chemistry like non-equilibrium).
Non-linear systems and chaos theory (these are concepts from mathematics and physics)
Network theory (it stems from the social sciences and graph theory in mathematics and is now very popular due to developments in computation and data science)
Adaptive systems theory (rooted in cybernetics and game theory and inspired by observations in biology, ecology, sociology, economics, management)
Complexity Science is not new, however. For a look into the range of topics covered and how complexity science has developed throughout the decades, have a look at this map:
From students for students - have a look!
Complexity resources from RICH students
Don't know where to start?
This collection pools together resources from current and former RICH students that have gotten them interested in complexity science, been helpful with starting their research project and/or dealing with an interfaculty project. This includes websites, videos, books, and more!
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