Maybe I’m taking on more than I can chew here but I’m going to attempt it. I apologise if it does not work for you, or you simply just give up on this but I am going to try to explain innovation as an complex adapative system. Why? I like the pain involved! I’m certainly not in any shape or form an expert, or even that much of a student of complex systems, and what it fully consists off but I do need to explore this more, and a little shared pain helps in this as I go.
This issue is one I consistently come across in many references to innovation. The trouble is I’ve never been fully clear on what does make up a complex system for innovation. I’m not sure anyone does for complex systems either! But I want to establish a direct and clear set of links across to innovation without it involving me in ploughing through incredibly ‘dense’ academic papers on this subject.
It is amazing how Wikipedia is becoming rapidly a first call of reference, is it because it takes away all this density found in academic papers, or that the academic papers are written mostly for an informed group and for those of us, obviously sitting on the outside of this ‘elite’ group,we gravitate to where we seem welcome to gain a ‘reasonable’ and quick understanding. So this is my starting point.
Irrespective our starting point has to be definitions
Just as an aside, I’m presently having a debate/ discussion on whether social innovation’s definition needs changing and have been arguing do we need any more debates on definitions around (any) innovation but equally, having one, does always clarify the starting point, so borrowing from Wikipedia again, lets define:
A complex system is a system composed of interconnected parts that as a whole exhibit one or more properties (behavior among the possible properties) not obvious from the properties of the individual parts.A system’s complexity may be of one of two forms: disorganized complexity and organized complexity. In essence, disorganized complexity is a matter of a very large number of parts, and organized complexity is a matter of the subject system (quite possibly with only a limited number of parts) exhibiting emergent properties.
Complex adaptive systems are special cases of complex systems. They are complex in that they are dynamic networks of interactions and relationships not aggregations of static entities. They are adaptive in that their individual and collective behaviour changes as a result of experience
So did that help?
Thankfully whoever wrote the Wikipedia entries kindly gave some examples of complex adaptive systems. These include the stock market, social insect and ant colonies, the biosphere and the ecosystem, the brain and the immune system, the cell and the developing embryo, manufacturing businesses and any human social group-based endeavour in a cultural and social system such as political parties or communities. There are close relationships between the field of CAS and artificial life. In both areas the principles of emergence and self-organization are very important.
So does innovation also fit within complex adaptive systems?
If we take the suggested feature list presented on Wikipedia’s entry for complex systems (https://bit.ly/nF5F3G ) I feel innovation fits. Let’s make some comparisons and this is my attempt to quantify innovation for being a complex adaptive system in the table below. It is a work-in-progress.
Components of an innovation complex adaptive system compared.
|Complex System Features||Innovations Adaptive Complex System|
|Wikipedia Entry||My Innovation related view|
|Cascading Failures||Due to the strong coupling between components in complex systems, a failure in one or more components can lead to cascading failures which may have catastrophic consequences on the functioning of the system||The amount of effort we put into the Stage-Gate process for innovation. If this is allowed to be sidetracked, given over to the whims and agenda’s of individuals as we progress innovation through the system we arrive at cascading failure and a poorly functioning end point in value due to consistent compromise.|
|Difficult to determine boundaries||It can be difficult to determine the boundaries of a complex system. The decision is ultimately made by the observer||As we open up more our innovation processes to joint collaborations, the borders between the parties will ‘blur’ and tough decisions made on who owns what will occur. This needs actively managing|
|Complex systems may be open||Complex systems are usually open systems — that is, they exist in a thermodynamic gradient and dissipate energy. In other words, complex systems are frequently far from energetic equilibrium: but despite this flux, there may be pattern stability, see synergetics.||As innovation is allowed to interact increasing outside our four walls it becomes more permeable, more shaped and influenced so we need to become far clearer in our goals and objectives we are trying to achieve. The battle of managing equilibrium against adaptability and agility will not be “Business as Usual”- it can’t be, we are consciously changing it.|
|Complex systems may be nested||The components of a complex system may themselves be complex systems. For example, an economy is made up of organisations, which are made up of people, which are made up of cells– all of which are complex systems.||Innovation is nested. We need to build an innovation business architecture made up of at the highest level, at the strategic level, and working down through several other “layers”, including people and processes. The goal is to deconstruct the important drivers and influencers which will direct innovation activities. From this we identify a innovation framework.|
|Dynamic network of multiplicity||As well as coupling rules, the dynamic network of a complex system is important. Small-world or scale-free networks which have many local interactions and a smaller number of inter-area connections are often employed. Natural complex systems often exhibit such topologies. In the human cortex for example, we see dense local connectivity and a few very long axonprojections between regions inside the cortex and to other brain regions.||The more we connect in the world the more we can reach new thinking for innovation. The internet allows us to make contact with anyone, on any thing. Strangers are being linked by a mutual objective or casual acquaintance that moves innovation into the small world network theory. We are working more towards scale-free networks as ‘hubs’ or centres increase their connections that offer a power-law influence over the others. We do need to layer innovation, like a cortex and we are constantly working on making connections for more innovation discoveries.|
|Complex systems may have a memory||The history of a complex system may be important. Because complex systems are dynamical systems they change over time, and prior states may have an influence on present states. More formally, complex systems often exhibit hysteresis.||The more we infuse ‘dynamics’ into innovation the more we can achieve. As we improve our systems and structures the more dynamic they can become. They can over time in steps evolve to manage multiple innovation types. I call these dynamic capabilities for innovation fitness landscapes and am working towards a model on this.|
|May produce emergent phenomena||Complex systems may exhibit behaviours that are emergent, which is to say that while the results may be sufficiently determined by the activity of the systems’ basic constituents; they may have properties that can only be studied at a higher level. For example, the termites in a mound have physiology, biochemistry and biological development that are at one level of analysis, but their social behaviourand mound building is a property that emerges from the collection of termites and needs to be analysed at a different level.||It is the amount of interactions we can promote; the greater the potential is for breakthrough innovation or more radical concepts. The ability of an organization to allow time for increased interactions, the richer the possibilities can arise. There are lots of potential for unintended consequences in encouraging this consistent exploring but it will be the ability to manage these through the building of absorptive capacity through its three stages of accessing, anchoring and diffusion for new knowledge creation and exploitation.Our innovation behaviours will evolve the more we invest and discover the multiple options that reside in managing innovation as a discipline.|
|Relationships are non-linear||In practical terms, this means a small perturbation may cause a large effect (see butterfly effect), a proportional effect, or even no effect at all. In linear systems, effect is always directly proportional to cause. See nonlinearity.||The argument for innovation is it has to become non-linear. Most innovation is complex involving multiple agents, dynamic interactions combining in often unique ways. These fluctuate and combine and any innovation system has to have higher degrees of flexibility more for today, as many issues are difficult to solve in just (simple) linear ways.|
|Relationships contain feedback loops||Both negative (damping) and positive (amplifying) feedbackare always found in complex systems. The effects of an element’s behaviour are fed back to in such a way that the element itself is altered.||I have been recently discussing the different learning loops for innovation. When an event is part of a chain they often have a cause-and-effect on the next steps in the innovation cycle. These often form a loop, said to “feed back” into itself. These move towards ‘double or triple’ loops needed for greater innovation learning.|
Source for the features used for a complex adaptive system has been taken from: https://en.wikipedia.org/wiki/Complex_adaptive_system and for the innovation complex adaptive system are my thoughts on where the feature does apply in innovation to fit. W-I-P 09 02 2012
Do you agree, do you see other ones, or have I lost you? Perhaps we need to view the complexity within innovation differently- a further blog I hope helps on this. Coming soon.
image credit: organizationalphysics.com
Paul Hobcraft runs Agility Innovation, an advisory business that stimulates sound innovation practice, researches topics that relate to innovation for the future, as well as aligning innovation to organizations core capabilities.