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Complexity Science Overview

Fish schools are examples of complex systems, with many different parts acting and reacting to each other's behavior

Fish schools are examples of complex systems, with many different parts acting and reacting to each other’s behavior

Complexity science is a new approach or method to science that has arisen over the past few decades to present an alternative paradigm to our standard method of scientific inquiry. It is based upon complexity theory and uses new tools of computer modeling and simulation to try and analyze the complex systems that make up our world.

History

The beginning of the modern era, approximately four to five hundred years ago, saw the development of a systematic and coherent framework for conducting this scientific process. This framework became clearly formulated with the work of Sir Isaac Newton, and Newtonian physics became an example or paradigm of how modern science should be conducted. The Newtonian paradigm is a whole way of seeing the world that describes phenomena as the product of linear cause and effect interactions between isolated objects, that are determined by mathematical laws. This vision of events results in a very mechanical world sometimes called the “clock world universe”. This new paradigm employed a method of inquiry called reductionism. Reductionism is the process of breaking down complex phenomena into simple components that can be modeled using linear equations. By then reassembling these individual components, we can understand the whole system as simply the sum of its individual parts. Having been phenomenally successful within physics, this framework for modern science has gone on over the century to be applied to almost all areas of inquiry, from biology to engineering and business management, placing it at the heart of our modern understanding of the world.

Nonlinear Systems

Chaos theory, that had its origins in the study of weather patterns, was one of the first major areas of complexity science to find widespread acceptance within mainstream science

Chaos theory, that had its origins in the study of weather patterns, was one of the first major areas of complexity science to find widespread acceptance within mainstream science

It is only during the 20th century that this approach to science began to be called into question as the revolutions of quantum physics and relativity showed some of its most basic assumptions about time, space, and causality to be flawed. While later in the century chaos theory began to open up a new world of non-linear systems. Outside of science, the world had also become very different from the one Newton lived in as globalization, information technology, and sustainability present us with the new challenges of understanding, designing and managing systems that are highly interconnected, interdependent and non-linear, that we can now call complex systems.

The Science of Complexity

This is where complexity science comes in to provide us with an alternative scientific method better suited to researching these complex systems, supported by a paradigm that sees the world as a set of interconnected elements whose interaction gives rise to the patterns and phenomena that we observe in the world around us. As opposed to traditional science that tries to eliminate complexity by studying the individual component of a system within an isolated environment, complexity science places a greater focus on open systems, that is, understanding systems within the complex of relations that give it context. Whereas traditional reductionist science primarily uses linear mathematical models and equations as its theoretical foundation, complexity science uses the concepts of complexity theory, such as self-organization, network theory, adapting and evolution.

Methods

Complexity science leverages new information technology to study complex systems through analyzing large datasets

Complexity science leverages new information technology to study complex systems through analyzing large datasets

This new theoretical framework is combined with new methods such as agent-based modeling. As opposed to describing the phenomena we observe in terms of “laws of nature” encoded in equations, agent-based modeling takes a more bottom-up approach, describing them as the emergent phenomena of local level interactions between agents governed by simple rules. Complexity science studies the complex systems in our world that have previously fallen between the gaps of modern science, such as financial networks, cities, and social networks. Studying these large complex systems typically requires significant amounts of data. What the microscope, telescope, and laboratory were for modern science, computation and data are to complexity science, which relies heavily on computer simulations and analysis of the mass of rich and diverse data that information technology has provided us with.

Significance

In a time when science has become highly specialized and focused upon extreme scales from the Big Bang to little strings, complexity science is providing a fresh perspective for refocusing on the everyday world in front of us and helping to bridge the traditional divides between sciences. In so doing, it is helping us expand our scientific body of knowledge to make it richer, more inclusive, and proving particularly relevant as a new method for providing the knowledge needed to tackle some of the core challenges we face at the turn of the 21st century.

Cite this article as: Joss Colchester, "Complexity Science," in Complexity Academy, April 6, 2016, http://complexityacademy.io/complexity-science/.