Fury and adrenalinePosted: November 20, 2013
Notes on the practice of innovation and technology commercialization
No one descends with such fury and in so great a number as a pack of hungry physicists, adrenalized by the scent of a new problem.” D. Watts. Small Worlds: The dynamics of networks between order and randomness. Princeton University Press, 1999.
I’m postponing the promised discussion on learning from agile manufacturing (see my October blog Create early, use often: Lego™ blocks, learning objects, and ecosystems. Part 2 http://innovationrainforest.com/2013/10/13/create-early-use-often-lego-blocks-learning-objects-and-ecosystems-part-2/ in order to talk about where my blogs are heading since I wrote the first one last February.
The point I’m attempting to make in these pages is that, at least in my experience, most analyses of technology commercialization are rather surface and have little depth or theoretical underpinnings. By comparison, economics, engineering, and law, each have an analytical base. Maybe this lack of depth is because it’s only been some 30 years since Paul Romer recognized technology as an endogenous growth factor. Of course, we are all aware of problems caused by physicists “descending” on economics and in some cases giving the practice an unjustifiable mathematical rigor based on unsustainable assumptions. I speak as a physicist myself – not an economist. I also speak not as an academic or researcher but as a practitioner seeking insights into practice.
In my work I find that technology commercialization methodologies for developing countries are frequently being unnecessarily re-invented, and reusable knowledge is not sufficiently shared (the topic of my last two blogs). My hypothesis is that this is because the basic analytical infrastructure which underpins commercialization activities in disparate regions of the world is not well understood, and that which is understood is not efficiently available to practitioners.
Through these blogs, In a small way, I’m trying to create a more complete basis together with tools, based on theory and practical experience, for use by practitioners.
Previous blogs in this series have discussed, in addition to reusable knowledge objects, the role of networks and links, decision making and problem solving, transaction costs, imperfect optimization, and complexity. It is this latter topic – complexity – which will be the rationale for the next few blogs which will attempt to at least approach an analytical foundation. We shall begin to see how these ideas relate to issues in The Rainforest: The Secret to Building the Next Silicon Valley http://www.therainforestbook.com/ by T2VC’s founders Greg Horowitt and Victor Hwang and how the book’s principles can be applied.
We will try to collect results from researchers and practitioners applicable to technology commercialization and, more broadly, creating the architecture of economic development ecosystems. This will not be a simple task as thousands of research papers, blogs, and articles are published each year on this subject.
At two ends of the application spectrum, Jean Boulton talks about the world as a complex system So the world is a complex system – what should aid agencies do differently? http://www.oxfamblogs.org/fp2p/?p=9645 whereas Ian McCarthy applies ideas of complex adaptive systems to new product development New Product Development as a Complex Adaptive System http://itdepends4.blogspot.ca/2012/09/new-product-development-as-complex.html?goback=.gde_76119_member_194199540
We live in a complex and non-linear ecosystem usually in far from equilibrium situations so I begin by briefly explaining these terms, in the present context, along with related concepts.
There are many definitions of a complex system (which reflects the fact that we have an incomplete understanding of such systems). An informal definition is a large network of relatively simple components with no central control, in which emergent complex behavior is exhibited. We will return to “emergence” in a later blog. The term “adaptive” is often appended to complex systems and means a system which is capable of learning.
This definition can be extended by saying that a complex system is a system which has heterogeneous smaller parts, each carrying out some specialized function, not necessarily exclusively, which then interact in such a way as to give integrated responses. In a complex system, as opposed to a complicated one, the function of the whole in a complex system cannot always be guessed from the function of its parts, and the reassembly of the parts does not always give back this function. This extended definition is based on work by David Snowden, which we shall discuss in future blogs.
The notion of nonlinearity is important here: the whole is more than the sum of the parts. Innovation is considered to be such a system which also exhibits another property of nonlinearity, namely, where the same input may not always yield the same output. This means that to understand a complex system we have to study the system as a whole; different from the “reductionist” methodology of decomposing systems into their individual components to see how they work.
Some results from research in complex systems we might say are common sense (which is another way of expressing our personal experience) such as the theory behind the emergence of new ideas from a group of people arguing with each other. Other results may be counter-intuitive, such as how small changes in initial conditions may produce large effects later on.
And Turner’s Shipwreck painting? His magnificent brush strokes show a Far from equilibrium state. That is, one in which it is definitely not business-as-usual and events are occurring which push a system into a highly dynamic and unstable state. Much more about this concept later.
Next time: If all of life is a dispute (according to Nietzsche), let’s argue – a case example of a far from equilibrium state of affairs in technology commercialization.