What is Complexity

Working with Complexity

The study of complex systems involves understanding how vastly different systems - composed of elements as diverse as sand, ants, or people -  organize into more fit and resilient states by virtue of the specific nature of their internal dynamics.  When we speak of a system being a 'complex system' we wish to identify it as belonging to a particular class of systems, all of which are subject to the same underlying dynamics. Despite the seeming diversity of such systems they are subject to the same Governing Features.

What do we mean by complex?

To understand complexity it is perhaps useful to consider the word 'plexus'  or 'braided' from which we get the word 'complex', referring to being "braided together."  This term is apt as these systems can be understood as involving intertwining dynamics. Here, instead of there being clear chains of case and effect, the behavior of any particular element in the system is both a cause and an effect of the behaviors of other elements in the system.

Example:

Consider a sand dune, etched with the pattern of ripples across its surface. The ripples are beautiful, they form intricate, interweaving patterns, that could have been made by an artist working on a canvas. But the ripples are not the result of artist or a designer envisioning an end product. Instead they are the resulting, emergent pattern that has been created from below: individual grains of sand, tossed by the wind, subject to forces of resistance in an environment characterized by different inputs, and interacting with surrounding grains of sand, appear to coordinate their distribution. We would expect that this coordination would appear random, chaotic, messy - but it does not: it appears as though the grains collaborated with one another to form a beautiful, integrated, complex entity. How do the grains do this? How do they self-organize, from the bottom-up, in ways whereby these emergent patterns appear?

A key aim of this website is to understand what is, and what is not a complex system, so we can better apply our understanding of complexity across research domains. Accordingly, it is important to clarify what we do and don't mean by a 'complex' or intertwined system vs a 'complicated' system.  Complicated systems are subject to an entirely different set of dynamics which, while interesting, and are wholly unrelated to the causally intertwined systems we wish to speak about.

Example:

Consider an aircraft. It is a complicated system made of many sophisticated components that come together in very specific ways in order for the aircraft to fly. We can say that the aircraft is made of many individual parts, that these parts interact and sometimes exchange energy or signals.  Together, these individual components are structured so as to form an 'emergent' aircraft that is 'complex'.  This description of an aircraft is accurate and it employs terminology often used in Complexity theory.

If we were to put an aircraft next to a sand dune and ask what is the more 'complex' system, an intuitive response might be that the aircraft - with all its complicated machinery - is the more complex of the two.

But an aircraft is not a complex system whereas a sand dune - simple as seems - is.

Perhaps this is too extreme a statement - indeed, if we look at the formal definitions of the words 'complex' and 'system', it would be hard to argue that an aircraft is not a complex system. But what is key here is that it is not particularly useful to position an aircraft as a complex system if we are looking to gain a better understanding of it.

To illustrate: suppose we decided to characterize an aircraft as 'a complex system'. We could then spend a lot of time learning about how sand dunes form and how emergent patterns of sand ripples arise, in order to gain an understanding of complexity and emergence.  These studies, informative though they might be, would do little to enlighten us regarding aircraft operations. This is not to say that comparing an aircraft to a sand dune would be pointless - it is possible that this novel lens would provide us with new insights into aircraft design - but we would not be leveraging a transfer of knowledge between systems that are subject to the same kinds of dynamics. The parameters that inform the emergence of aircraft flight are not part of the same class that inform the emergence of sand ripples.

Instead, the systems we wish to focus on are subject to the same universal or general governing dynamics. Other kinds of systems, that might indeed be complicated, are subject to different kinds of governing dynamics. The sand dune is a system composed of many individual grains of sand that come together, are impacted by wind currents (flows) and by global dynamics involving the sand particles interacting both with the wind and one another (feedback). Together, the grains of sand gradually form an 'emergent' dune that has an integrated form, articulated by ripple patterns that are emergent and 'complex'. These same dynamics - a group of agents, a source of energy and flow, and feedback within the system - are played out in many, many systems, that we are only just beginning to fully grasp. The dynamics that play out in these systems, leading to the appearance emergent structure that cannot be found within the individual agent (there is no sense that the grain of sand contains or understands the ripple pattern it participates in) is played out in all kinds of systems. We can think of the fractal cracks of dried lake bed mud, the emergent trails of ants leading to food, or the pathways traced by slime mold.

Beyond the hype

Many people are excited about complexity research and its concepts. There is lot of hype around words like self-organization, emergence, non-linearity, etc., and these are used to describe a host of systems.  While there is nothing inherently wrong with this, it is nonetheless important to understand the difference between insights gained by metaphoric thinking versus insights due to intrinsic correspondences between systems. I might compare a city to a body, then state that suburban growth is like a cancer. I can then equate planning and zoning restrictions with chemotherapy (limiting growth).  The analogy might be productive, but I shouldn't then imagine that by immersing myself into learning about the dynamics of cancerous growth I will glean valuable insights regarding the dynamics of suburban growth.

In order to learn from complexity, it is important to know the appropriate kinds of systems that might be framed this way, so that we can draw appropriate insights from the underlying dynamics operating in all. Regardless of system - be it sand dunes or ant colonies or netflix recommendations - there are consistent dynamics at work in complex systems. Consequently, if we understand one system - and the kinds of mechanisms governing its behavior -  it is much easier to figure out what is going on within another, and much easier to use complexity dynamics to design solutions for specific problems.

Bringing Complexity into Practice

By charting the characteristics of complex systems, this website will help users determine if the domain they are working with in is, in fact, on that is being steered in ways that echo complexity dynamics and, if this is the case, what dynamics might be better steered or intervened upon.

The amazing thing about complexity is that we actually can gain insights about how cities change and evolve if we know more about how ants find food - and not just metaphorically. If we begin to understand that many systems are composed of agents, activated by flows, and then impacted by feedback, we can start to figure out what a system needs in order to function more effectively. Perhaps there are not enough agents in the system; maybe feedback in the system is missing, which keeps agents from learning from one another; perhaps the resources or flows needed to drive the system towards better outcomes are not strong or consistent enough. There are many ways that, armed with better knowledge about what is, and what is not, complex we can more effectively bring complexity into practice.