Maybe I’m taking on more than I can chew here but I’m going to attempt to explain why innovation can be so complex and requires an adaptive system.
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 a complex adaptive 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 of, but I do need to explore this more, and a little shared pain helps in this as I go.
This issue is one that I consistently come across many references to innovation being a complex system. The trouble is I’ve never been fully clear on what determines the make up a complex system for innovation. I’m not sure anyone does for complex systems either!
So my aim here is to establish a direct and clear set of links across innovation and complexity without it involving me in ploughing through incredibly ‘dense’ academic papers on this subject.
It is amazing how Wikipedia is becoming rapidly the 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
So borrowing from Wikipedia let’s define:
A complex system is a system composed of interconnected parts that as a whole exhibit one or more properties (behaviour 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 (http://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 agendas of individuals as we progress innovation through the system we arrive at cascading failure and a poorly functioning endpoint 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 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 the 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 an 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 axon projections 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 anything. 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 overtime 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 behaviour and 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, and 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) feedback are 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. |
The source for the features used for a complex adaptive system has been taken from: http://en.wikipedia.org/wiki/Complex_adaptive_system and for the innovation complex adaptive system is 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?
Cracking the complexity code
There was a good article within the McKinsey Quarterly, published in 2007 entitled “Cracking the complexity code” written by three authors Suzanne Heywood, Jessica Spungin and David Turnbull that leads with “one view of complexity holds that it’s largely a bad thing- that simplification generally creates value by removing unnecessary costs”.
Certainly, we all yearn for a more simplified life, structure, organization, approach to systems or just reducing complexity in our daily lives to find time for what we view as improving its ‘quality’.
Within the article, they argue there are two types of complexity- institutional and individual. The former concerns itself with the interactions within the organization, the latter is the way individuals or managers deal personally with complexity.
The real important takeaway from this article is when organizations treat complexity as something they must overcome, reduce or try to ignore they miss opportunities.
Complexity, the authors argue, should be seen as a challenge to be managed, managed well, and its full potential exploited, not as a problem to be reduced or eliminated. It is through the nature of these complexities we achieve competitive advantage and can exploit more of the flow of knowledge for those new sources of new profit and wealth creation.
They suggest that organizations need to decide on where to hold complexity within any design and build the right capabilities where they matter. I would argue innovation certainly matters, and it is complex and needs to be understood as exactly that, and managed accordingly not in a piecemeal fashion.
Complexity matters in building the right processes, skills and culture but because they don’t behave in linear ways and any ‘messing’ with the complexity and relationships within this can have an awful lot of unintended consequences.
The other correlations that work for me
The late Everett Rogers offered us the diffusion of innovation, which gave us a frame to understand the process by which innovation spreads within social systems.
Complex systems are equally about relationships among the members of a system. You move into more the emergent behaviours that become increasingly adaptive in response to the environment and what interact within it.
Diffusion occurs in complex systems where networks overlap, exchange and learn. Both Diffusion and Complex Systems adapt and adopt with the endpoint of making ‘it’ into more of an ordered system.
The more you work the system, the fitter for the purpose it becomes, the more it diffuses out, the more dynamic it becomes and increasingly valuable from these interactions.
Complex adaptive systems don’t operate in equilibrium conditions
I’ve been also in a set of debates in recent days around management looking for stability, for predictably, looking to take as much complexity out of the system as possible- often sometimes labelled as ‘variance’.
This leads to enforcing business as usual as the modus operandi for innovation to ‘fit’ but we are faced with the very opposite in today’s world, the need to ’embrace’ reoccurring change.
We need to manage complexity and we do need innovation so we do need to obtain as much diversity and non-linear structure in what we do to allow diversity and all possible options. Our innovation systems are being forced ‘open’ making them even more complex and our energies will have to turn from ‘containment’ to more ‘adaptive’ and responsive ones to manage going forward.
We need to not reject complex systems we need to understand them, we need to embrace them and learn to determine what needs to be complex and what doesn’t. This requires a real ‘flow’ of different energies to maintain the organization of the system, it needs active managing. It will only become harder.
For innovation to work, to thrive, and to provide a sustaining payback, it needs to be seen as a complex adaptive system. We can’t keep hiding and pretending the ‘bits and pieces we play with and constantly fiddle with, called our innovation system, will be sufficient. We do need to understand innovation in its entirety.
We have choices of complexity
There are different types of complexity to manage. Work conducted by Julian Birkinshaw and Suzanne Heywood suggested four types of complexity. I only summarize these here. Imposed complexity, those interventions both internally and externally that require ‘higher’ insight.
There is the inherent complexity found with any organization and presently managed through striving to be more efficient and effective.
There is designed in complexity, where innovation needs to fit more. These are choices about how, where and why an organization sets about its operation. These can be constrained, underinvested in, and even jettisoned but do have lasting consequences for the future of the organization.
This is the area of strategic consequence as these can limit competitive advantages from the level of innovation intensity chosen as an example.
The fourth is unnecessary complexity where increased misalignment resides, it is sometimes easy to recognize but often hard to let go as it sometimes makes up “the way things are run around here” and has a richness in history.
The challenge of complexity within innovation
If you can begin to identify complexity that hampers effectiveness you can begin to remove it but be really clear on the effects if the complexity part you are removing is not the route to value and often innovation, which certainly does seemingly get constrained and caught up in this often shorter-term pursuit of effectiveness for effectiveness sake and you don’t have the bandwidth for innovation exploration.
Recognize innovation is complex, recognize it does have to be handled carefully but it needs to also be fully understood for what it is, a complex adaptive system. It cannot be treated in the same way as effectiveness or efficiency can.
It needs ‘actively’ manage differently, for all the future opportunities it holds by placing the emphasis on building greater innovation capabilities to make it ‘dynamically’ work.
Otherwise, you end up with unexplained consequences to poorer performance from your innovation activities and are often at a loss to explain why.
We do need to relate more to complexity as it comes with the turf if you want really lasting innovation.
