For many reasons, innovation strategy is poorly understood and even more poorly executed.
This series of posts will discuss some of my thinking on innovation and creativity, and as such outlines a concept I have developed: the Combinatorial Theory of Innovation from first principles. Others may have written something like this in the past, and if so, super. My concepts draw on methodologies and frameworks in high-dimensional combinatorial spaces that I both created and used at places like NASA and for the DoD for complex systems. For me, these could (and should) be applied everywhere, but especially to how we think about innovation.
I would also like to acknowledge that there are a handful of superb scholars in the field of innovation theory, and I hope there is some overlap in our thinking. There are unfortunately also some less superb innovation scholars roaming the halls of academia and policy and I hope that by outlining my theory of combinatorial innovation, I can more adequately explain why some of their policies are bad, and maybe even at times immoral.
[NB: This set of posts in particular comes from a series of ongoing discussions between myself and Turlough Downes as we create an innovation policy working paper using combinatorial and chaos theory, focusing on Ireland's innovation ecosystem. Thanks Turlough!]
Art by Amina McConvell: "A Combinatorial Explosion"
As mentioned in Part One and Part Two, the goal is to create an understanding of innovation strategy such that sound economic and social policy can be created, which is often hampered due to (1) complexity and (2) a lack of visible policy feedback. By way of deriving the first principles of the innovation ecosystem’s behavior, this theory will then enable:
1. A transparent understanding of innovation strategy (reduce complexity)
2. A generalizable input-output response model for implemented policy (create feedback loop)
This will be achievable by modeling the innovation ecosystem as a complex system, and moving from a mathematics-based economics solution based on system equilibrium (bad!), to a complex system solution based on dynamic and adaptive behavior (good!). The process being pursued is as follows:
1. Map ecosystem constituents*
2. Map ecosystem drivers*
3. Derive system first principles
[Only * will be included in this post.]
1. Mapping Ecosystem Constituents
The innovation ecosystem lacks a formal and widely accepted definition for terminology and mapping around the nature of innovation, stakeholder involvement and measured outcomes. This clearly adds to the complexity of the nature of industrial strategy and measuring policy feedback: without a defined map of the innovation ecosystem, it is not possible to do activities such as compare two policies or even comprehend policy outcomes qualitatively or quantitatively. This leads to increased reliance on narrative and as such further creates discordant economics.
Table 1 below demonstrates some of the terminology involved when comparing several economic policies on innovation strategy, thus adding to complexity:
It is a necessary first step to map the constituents of the innovation process such that a strategy can be created and
(1) actors in the ecosystem are defined,
(2) roles are assigned to those actors and
(3) expected input-output mapping can be created.
Firstly, I will break down the constituents within the table by activity, and the constituent outputs described in terms of complexity theory, which will allow activities to be modeled not as a linear set of processes, but instead as a ‘space’ (mapping) of behaviors and likely outcomes.
The innovation ecosystem is broken down into three distinct set of processes, as follows:
Discovery: is the activity of discovering the basic science of the universe. Actors in this space only discover existing relationships between a fixed number of variables that allow us to better understand phenomena about the world in which we live, such as quantum mechanics, fluid mechanics, electricity or mathematical relationships. The discovery space is finite: actors in this space only find what already exists, they do not create anything new. This is a critical aspect of discovery. There are a finite number of discoveries that can be made about our universe; the universe itself does not change, only our understanding of it as we discover it.
People in the discovery space produce building blocks of basic science. Once they have produced a building block, they produce another, and then another. Actors in this space seek only to produce basic building blocks, nothing more and nothing less, of which there is a finite amount. The space is so large that it may seem that, when compared to achievable accomplishments within our lifetime, there are infinite building blocks that can be created, but it is important that we realize and model it as a finite space. Big but finite.
Application: activity in the application space takes as an input the output of the discovery space seeks (building blocks). The output of the application process is the creation of variations of the discovery space. For example, if the discovery space produces building blocks A, B, C and D, the application space will create combinations of these blocks (such as AA, AC, AD, BAC, DDAC). Agents in this space will play with the building blocks and build variation after variation of building blocks, each variation known as an application block. An application block is a combination of building blocks. Whereas the discovery space is finite, the size of the application space is a combinatorial explosion (has an exponential growth rate of application space outputs to application space inputs).
Commercialization: activity within this space takes application blocks (from the application space) and combines them with the real-world of markets, people, businesses, society, etc. to create outputs with inherent value (societal or financial value for example). These are known as commercial blocks. The size of the commercialization space is infinitely large: it takes inputs from a combinatorial explosion (the application space), and combines it with the chaos-driven space of markets, businesses, humans and policy. It is like multiplying one space of extraordinarily high dimensions with another space of even higher orders of dimensions, and the result is an infinite dimensionality space. Unlike discovery and application spaces (which are both large but not infinite), the commercialization space is infinite. For example, an application block “DDAC” may not be commercializable with the first ten billion interactions with the ‘real-world’, but it may be commercializable with the ten-billionth-and-one interaction.
Thus, this report defines innovation as the entire process of three sub-processes:
Taking this new innovation process definition, and reconsidering Table 1, we can create more clarity and reduce complexity around definitions. Each of the “Innovation Activities” can be defined in terms of what they produce: basic building blocks, application blocks (combinations of basic building blocks), or commercial blocks (interaction of application blocks with the real world).
For example, basic science research is a discovery activity, research and design (R&D) is an application activity and transformations usually take place in the commercialization space.
We can now start to better understand what the inputs and outputs should be at each stage of the innovation process. Why do we care about inputs and outputs per stage? Because we want to better understand how different policy decisions impact the entire innovation ecosystem, and this is a good place to start.
A visual representation of a combinatorial explosion space: high dimensionality. Each point on this graph is represented as a value along x dimensions.