Complexity Bits

If we are to develop new operational tools for taking on complex patterns we need to better understand how they form, fragment and change. Towards this end we must turn to the science of patterns – Complexity Science. While a more detailed overview of its ideas and epistemological development can be found under ‘Building Blocks’, these short bits provide some brief explanations of key complexity concepts in the context of social ecologies, keeping the inevitable “so what” question in mind.

Emergence and Self-Organisation

So how do systemic patterns emerge in the first place? According to Complexity Science they are the product of continuous interactions between numerous individual elements. These could be molecules in chemical soups producing chemical reactions, the neurons in our brain firing patterns of awareness, or buyers and sellers in markets generating an array of prices. Whatever the environment, it is the accumulation of numerous individual interactions that ultimately gives rise to the patterns we observe, both the ones we find highly conducive to our social ecology and the ones we desperately want to change.

Connectivity and Interdependency

Within complex systems everything and everyone is connected to everything and everyone else, whether directly or indirectly. While seemingly obvious, we tend to think about reality in more compartmented ways.

Feedback Effects and Non-linearity

All emergence processes are based on the accumulation of local interactions creating systemic patterns that return to impact the very elements that have created them in the first place (see Emergence). Hence systemic feedback is a key mechanism driving complex structures.

Attractor States

An attractor state is a set of outcomes towards which a system can be expected to settle. While end states cannot be predicted, they will tend to converge within certain boundaries also referred to as the ‘basin of attraction’, a kind of imaginary vessel pulling all outcomes towards it. In other words, an attractor state essentially defines what we would consider as ‘normal behaviour’. Yet complex systems could have several different attractor states.

Tipping Points

‘Tipping points’ are events that push a system over the edge into a new attractor state (see also ‘attractor states’). They are the final straw, giving a pattern that last bit of energy needed to leap into a new “normal”.

Chaos

Coming Soon!