Notes on the practice of innovation and technology commercialization
A colleague at T2VC posed this question:
- We know that if everyone is an entrepreneur, a society will not function.
- We know that if everyone is a producer, a society will wither.
- Thus, is it possible to determine the exact proportion of innovators versus producers to maximize the productivity of an ecosystem?
This question is, I think, part of a broader one: in an innovation ecosystem is it possible to adjust all the various elements so that the ecosystem optimize its outcomes (measured in some way)? These elements may be universities, technology commercialization systems, public sector financing funds, and so forth – more about this later. You may remember that in the February 2013 Imperfect Works http://innovationrainforest.com/2013/02/06/imperfect-works/ blog in this series we talked about optimizing which means “make something as good as possible within a whole system” although each individual element may not be operating in the best way it can.
In their paper Chaos prevailing on every continent: Towards a new theory of decentralized decision-making in complex systems, http://www.temple.edu/lawschool/dpost/chaos/chaos.htm researchers David G. Post and David R. Johnson present a method of finding optimal configurations of elements in complex adaptive systems. The authors pose the Gardener’s Dilemma: how can a gardener find the best or at least a good configuration of a collection of plants whose overall “fitness” (for example total yield) is dependent upon the behavior of all the other plants? You may begin to see how the garden is an analogy for an innovation ecosystem. We shall see shortly if this analogy is helpful.
In this imaginary garden there are plants of different species. The gardener would like to obtain the most luxuriant overall growth. The gardener must decide for each individual plant: should it be pruned or not? How can the best combination of pruned and un-pruned plants be created that will produce the greatest yield for the whole garden?
As always, we need to make some assumptions, which for this garden are:
(1) The relationship between an individual plant’s pruned or unpruned state and its growth is different for each plant. For some plants growth will be increased by pruning, for other plants pruning will reduce their growth.
(2) Each individual plant’s growth can be affected by the growth of other plants, for example as one plant grows it might block sunlight reaching another plant that needs it. Kauffman calls these spillover effects although I prefer interactions.
Even with such simplifying assumptions it turns out that this problem, and many similar ones, are “computationally intractable” or in other words incapable of true solutions by any known analytical methods. A little thought may convince us why this is the case for our garden. Suppose there are only three plants each of which may be pruned (we will call this state 0) or unpruned (we will call this state 1). We can use the tree diagram below to help figure that 8 possible configurations exist, namely 2 x 2 x 2 = 23.
A little further arithmetic will show for 4 plants there are 16 possible configurations, namely 2 x 2 x 2 x 2 = 24 and so on. To generalize this pattern we can say that in any system with N elements, each of which can take one of S possible states, there are SN different system configurations. This SN can rapidly become a very large number. For 10 plants the number of configurations to be tried is already 1,024. Hard work for the gardener!
Is this gardening knowledge of any practical use for those of us developing optimal innovation ecosystems? If this this ecosystem analogy is computationally intractable, why am I wasting your time discussing it? Stay with me for just another moment and I will demonstrate its practical use – but first one final research result.
When a problem cannot be solved by mathematical analysis computer modeling may help, even greatly oversimplified models can be used as long as their simplifying assumptions are not forgotten when applying the results to the real world. Stuart Kauffman and his colleagues have developed a family of computer models and problem-solving algorithms for complex interconnected systems, known as “NK models,” for studying various forms of the Gardener’s Dilemma in, for example, evolutionary biology and cyberspace law. We don’t have space for details (which are in Johnson and Post’s publication) but essentially the method consists of modelling interactions between ecosystem elements. The elements in the garden ecosystem are sub-divided into any number of non-overlapping but interacting self-optimizing parts called patches, like a patchwork quilt.
To quote Post and Johnson “The result is a fairly remarkable one: It is by no means obvious that the highest aggregate fitness of the system will be achieved if it is broken into quilt patches, each of which tries to maximize its own fitness regardless of the effects on surrounding patches. Yet this is true. It can be a very good idea, if a problem is complex and full of conflicting constraints, to break it into patches, and let each patch try to optimize, such that all patches co-evolve with one another.”
A typical innovation ecosystem has elements (S) such as universities and research institutes with their technology transfer offices (TTOs), new business incubators and accelerators, some form of central support organization to assist TTOs with issues such as market intelligence and so forth, financing programs such as early-stage R&D grants and seed and venture funds, economic development organizations, science and technology park, a contract research organization, and sometimes miscellaneous organizations which were formed for different times but are still functioning. All these can certainly exist in many states so N will be much larger than 2 as in our garden.
Applying what we have learned, it is good practice to divide these ecosystems into patches, for example four possible patches could be:
Universities and research institutes with their technology transfer offices (TTOs)
Central support organization to assist TTOs
Early-stage R&D grants and seed and venture funds
New business incubators and accelerators
Contract research organization
Science and technology park
Economic development organizations
Miscellaneous organizations which were formed for different time
Having the elements trying to maximize their fitness within their patch improves trust and communications between them – and as a result – decreases transaction costs. Furthermore, this should break down the rigid hierarchical structure from which some ecosystems suffer. Other practical applications of the Gardener’s Dilemma can be found, such as explaining the familiar S-shaped curves of technology growth and disruption. Climbing around the fitness landscape searching for peaks and valleys will be discussed in future blogs.
Your response to all this may be “but this is obvious, I don’t need computer models.” In complex adaptive systems what appears to be obvious is not always so (occasionally disastrously not so). Personally I’m comforted by having a foundation theory.
Next time: Games of chance? Cause and effect in innovation ecosystems.
The setting: a New Year’s eve postprandial table. The diners:
Francis Bacon, 16 – 17th century philosopher and statesman, and inventor of the frozen chicken.
David Hume, 18th century philosopher and author of An Enquiry Concerning Human Understanding, and who believed that political discourse should always be polite (hmm…)
Cleisthenes, a politician from an antique land.
Dinner party host.
Host: “Francis, David – if I may be allowed to be informal in the presence of such empiricist greatness – we had a superb dinner and an enlightening debate about the scientific method, deductive reasoning, innovation, and Rainforests. Perhaps we can wrap up our conversation now?
We talked about the agriculture model of cultivated rationality with the goal of maximizing final output that I know resonates with you both. It’s a model that works for the mass production of plants, but blatantly fails when it comes to creating new ones. In this case, the better method is the Rainforest [http://www.therainforestbook.com/], which despite, or thanks to, its apparent non-rationality is the environment for innovation and the emergence and spread of new species. The Rainforest model also has the advantage of lowering transaction costs.”
Bacon: “What do you mean by transaction costs?”
Host: “Forgive me Francis, I forgot it was carelessness about cash transactions and the calumnies of your groupies that abruptly ended your political career.”
Hume: “As you know, I believe in deductive reasoning. How can we have such reasoning and demonstrate causal relationships in so patently a complex and, may I say, disorganized, model of innovation ecosystems?”
Bacon (who during the prior dinner conversation about innovation clearly believes Google to be the new 21st century ecclesiastical authority): “you may remember in my Essays I described time as the ‘great innovator’ but Google gave 115 million entries for definitions of innovation; it would seem the word ‘innovation’ has, in my opinion, been debased. However, the point I want to make is that innovation must include a time element”
Hume: “I have written that ‘all men of sound reason are disgusted with verbal disputes …it is found that the only remedy for this abuse must arise from clear definitions.’ Surely ‘innovation’ used to mean true breakthroughs, disruptions like the telephone, jet engine, internet, DNA structure, and so forth?”
Host: “David, innovation as such is a topic for another discussion. What I want to raise here is that it may not always be easy to grasp some of the Rainforest fundamentals so the purpose of these blogs is to apply Rainforest concepts to real issues, questions, and problems in innovation. In so doing we will use these principles to describe, through concrete examples, how to build innovation ecosystems in developed and developing countries.
Cleisthenes: “As an ancient fellow, I’m not sure what that these items are which Francis apparently knows about – although as a Greek I recognize tele and phone, so I assume telephone means someone shouting from a great distance. But, as a politician I don’t see so far how any of our discussions enable me to address the problems I have with delivering economic and job growth (we already have too many public intellectuals lolling around all day talking). These are my immediate problems: I’m not saying ‘I want to create an effective Rainforest ecosystem’ because I don’t yet see how this will help me. Just tell me how and I’ll go away and do it. Is that too much to ask?”
Host: That’s a fair point. Remember the reason I invited you to this New Year’s Eve dinner – and I’m grateful to you all for coming so far in time and space – is to seek your advice on future blogs in this series.
Let me survey what I believe to be the evidence for how to develop effective innovation ecosystems, summarize how this will be presented in more detail in future blogs, and get your comments.
Cleisthenes: “I’m sorry to be repetitive but I want to be sure I’m not misunderstood; this philosophical discussion is quite exciting – intellectually, but you still have not helped me. I don’t need to know about bounded rationality and such like we discussed over our meal, I want something that works. Before my government commits money to building Rainforest ecosystems, how will I know if we are on the right course without waiting for three, five years, or longer, by which time there will be a new government and new politicians to take the credit if success emerges, or blame the previous administration’s mistakes if failure; and of course the ‘expert’ consultants we used will be long gone to challenges new?”
Bacon: I realize that, unlike in my day, one cannot know about everything. My Google search showed me that innovation involves so many ideas from different disciplines, which have all been researched in depth. The readers of these blogs will not have time to study original sources. These blogs should bring results from research and application to the reader in a manageable form for practical use.
Host: If I understand your advice then in future blogs we should show how in Rainforest complex ecosystems we should, inter alia: solve problems; make decisions; express causality where possible; build innovation capacity; create roadmaps; show leadership; communicate.
Bacon: Don’t forget time. Time means telling an evolving story. Time as an element implies narrative. I believe in the efficacy of narrative to present what I have called elsewhere a ‘globe of precepts.’
Hume: Convince me that deductive reasoning and causality are not discarded in the complex systems of which you speak. As I have noted elsewhere, ‘we must endeavor to render all our principles as universal as possible, by tracing up our experiments to the upmost, and explaining all effects from the simplest and fewest causes.’
Cleisthenes: I concur with Hume’s last statement; by the way David, you sound like a Austrian economist. Keep it practical, easy to understand, and applicable to finding solutions to specific problems.
Host: Splendid! We will follow your sage guidance. Let’s reconvene next New Year’s Eve and review progress. Perhaps I’ll also invite Wittgenstein to talk about complex systems and reducing statements at a higher level to statements at a lower level, and maybe invite …..
Next time: Solving problems and growing gardens in the Rainforest.
Notes on the practice of innovation and technology commercialization
“I suspect that the fate of all complex adapting systems in the biosphere – from single cells to economies – is to evolve to a natural state between order and chaos, a grand compromise between structure and surprise.” Stuart Kauffman, At Home in the Universe: The Search for the Laws of Self Organization and Complexity (1995).
In this blog we continue, from the last blog Fury and Adrenaline http://innovationrainforest.com/2013/11/20/fury-and-adrenaline/ looking into the basic analytical infrastructure which underpins commercialization activities and the development of supportive ecosystems – with an emphasis on what is known about complex systems. I believe that more rigor leads to better explanation and prediction (the mark of a good theory is that it must not just explain but also predict) and thence to improved application by practitioners.
It was a warm cloudless summer morning in Central Asia as the glum looking group slid slowly but resolutely into the conference room to be appraised of new government initiatives to support R&D and technology commercialization. Several in the front rows sat, arms severely folded, questioning – sans words – the veracity of my colleague and I, and, by extension, that of the government’s sincerity.
A “presentation” was clearly not going to impress. Let’s have a debate instead I thought – with not entirely flawless logic – had not Frederick Engels, co-revolutionary with Karl Marx whose philosophy had once dominated this land, believed in the negation of the negation to deliver the future? A noisy dispute followed between audience and presenter (me, intervening only when the volume exceeded a decibel level sufficient to attract those in adjoining offices) and among audience members. One scientist became especially upset but was restrained by his colleagues from walking out in high dudgeon.
However, in the course of the bruising arguments which followed something exciting emerged out of the session’s flotsam; new thinking and agreements among the previously hostile audience which had taken the opportunity to vent against the government and their foreign consultants, and then moved on to a constructive deliberation.
The last blog introduced the idea of dis-equilibrium state (also called a far from equilibrium state); 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. Quite an accurate description of the Central Asian event.
Complexity science shows that when systems are in a dis-equilibrium state, small actions and events, “perturbations” in the system, can be amplified through a positive feedback cycle of self-reinforcement. This effect has been predicted and observed in evolutionary biology and also in studies of leadership in groups of people.
To show the theory behind all this we need to introduce the concept of a phase space (also called a state space). The term was originally use for substances which can exist in several phases or states. For example, water can exist in solid (ice), liquid, or vapor (steam) phases. The formal definition is “a multidimensional space in which each axis corresponds to one of the coordinates required to specify the state of a physical system, all the coordinates being thus represented so that a point in the space corresponds to a state of the system.” Note that, but don’t be too alarmed, we are using “space” as a mathematical term not as in everyday usage.
As an example, when you are riding your bicycle the physical space you inhabit is the familiar 3-dimensional space. However, your phase space is a 2-dimension one whose axes are position and velocity (remember velocity = speed and direction, such as 5 miles/hour due north). In economic systems, the phase space variables could the inflation rate, the interest rate, the national debt, and the unemployment rate, for example.
The Rainforest Canvas, created by T2VC, is a set of questions to help map an innovation ecosystem in a region by looking at factors such as: Leaders, Stakeholders, Frameworks, Resources, Activities, Engagement, Role Models, Infrastructure, Culture, and Communications. (The Rainforest: The Secret to Building the Next Silicon Valley http://www.therainforestbook.com/ ). In the Rainforest view of the world the system variables are these headings in the Rainforest Canvas. Parameters or constraints are the sub-questions under each heading. Changes in these parameters move the ecosystem to a new point in its phase space. As we can imagine, but cannot draw, a phase space diagram with these 10 variables let’s just consider three ecosystem variables a, b, and c, represented along the horizontal and vertical axes in the three dimensional phase space diagram (below).
Path 1 represents for example a big jump to a new business model, or a new set of resources, or possibly some disruptive innovation, whereas Path 2 represents continuous transformation: maybe a gradual culture change, or breaking down a problem or opportunity into manageable pieces and sequentially tackling each one. Diagram adapted from J. Goldstein et al. A Complexity Science Model of Social Innovation in Social Enterprise. Journal of Social Entrepreneurship, Vol 1, Mo 1, p.109, March 2010.
We will further build on this diagram in future blogs.
Once a complex system, of individuals in this case, is pushed to a far from equilibrium state, the more its leaders and members surface conflict and create controversy, the more likely that the system will generate novel opportunities and solutions. The more that leaders and members encourage rich interactions, the more likely that amplifying actions will be present in the system.
On the subject of leadership, my polyhistor colleague, Henry Doss, has written in his Forbes blog about organization’s which mistakenly focus on training leaders to lead people, rather than training leaders to build and lead systems: Why Your Innovation Leadership Training Will Fail http://www.forbes.com/sites/henrydoss/2013/06/06/why-your-innovation-leadership-training-will-fail/ Henry introduced The Innovation Syllogism:
Innovation is a product of culture (not individuals).
Culture is an emergent factor of systems (not individuals).
Therefore, systems drive innovation (not individuals).
“If the logic and assumptions of this syllogism hold, then you may find that the most critical aspect of building an innovative organization – systems – is absent from your training and development planning.” We will discuss emergence as a phenomenon in complex adaptive systems in a future blog.
Oh yes, what happened in the Central Asian meeting? Standing applause at the end, and the person who seemed most pleased with what emerged – you guessed it – was the scientist who had been the most voluble!
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.
Notes on the practice of innovation and technology commercialization
Aristotle (384-322) in his Nicomachean Ethics, Book III, in writing about a person’s voluntary and involuntary acts, distinguishes between (i) who is acting, (ii) what is the act, (iii) the circumstances of the act, (iv) the instrument or tool, (v) the aim of the action, and (vi) the manner of doing the act; for example quickly or slowly.
As we shall see, it turns out Aristotle had rather good advice for applying the reusable knowledge tools introduced in my last blog Create early, use often: Lego™ blocks, learning objects, and ecosystems. Part 1 http://innovationrainforest.com/2013/09/19/create-early-use-often-lego-blocks-learning-objects-and-ecosystems-part-1/ in which I speculated about the appealing simplicity of building technology commercialization programs, and, more broadly, supportive ecosystems, by plugging Lego™ blocks of learning into each other.
Well, can we? Let’s see how far we can go. If you’ve read my other blogs you’ll remember that “context” is a perennial theme. We meet it once again here.
Actually, the Lego™ block idea has been discussed extensively by educators and found to be an imperfect analogy. The problem is that the metaphor’s assumption suggests any learning object should be combinable with any other learning object; this is not always the case as we shall see – because of our old acquaintance: context.
It’s not that you cannot reuse these learning objects (we will call them ‘tools’ from now on) because contexts are never the same, it’s that context must be fully understood. We can think of every tool as being embedded in its own contextual cloud. For example, the tool to decide whether to license a technology or create a spin-off company which was introduced in an earlier blog Solving the Right Problem: Part 1 http://innovationrainforest.com/2013/03/24/solving-the-right-problem-part-1/ has a rather small context cloud; it is largely context independent. By contrast, a tool for use in developing intermediaries Network holes and how to plug them http://innovationrainforest.com/2013/07/31/network-holes-and-how-to-plug-them/ has a large context cloud; it is highly context dependent. This is mostly because “… no two knowledge intermediaries are the same; their work is entirely context specific, which means that, while it is possible to draw general lessons as to how they [a user] could chose to act, it is impossible to develop a standard set of rules as to how they should act.” Jones, Harry, et al (2012) Knowledge Policy and Power in International Development: A Practical Guide. The Policy Press. Chapter Five: Facilitation knowledge interaction, p 123. The authors also caution that (p. 135) … “it will not be possible to anticipate how the information will be used [by those seeking solutions] and its likely effects.”
I have not been able to come up with any tool which is entirely context free.
We next need to introduce the concept of ‘contextual qualifiers’ which are those pieces of knowledge that allow a user to assess whether a given policy or practice, implemented elsewhere, is truly relevant or applicable to the user’s environment. Conditional qualifiers are statements, which refer to knowledge Lego™ blocks (documents, videos, etc.) which ‘qualify’ the knowledge presented as being dependent on certain conditions. In more detail:
- Contextual qualifiers are those knowledge fragments (also called “facets”) that allow a user to assess whether a given policy or practice, implemented elsewhere, is truly relevant or applicable to the user’s environment.
- Contextual qualifiers are statements, which refer to knowledge sources (documents, videos, etc.) which qualify the knowledge presented as being dependent on certain conditions.
- Contextual qualifiers acknowledge that “one size doesn’t fit all” and that a tool is needed that helps knowledge users to better appreciate the influence of contextual factors.
- Contextual qualifiers facilitate the presentation and comprehension of context when locating potentially relevant resources.
- Reusable knowledge products may include embedded contextual qualifiers.
- Users should be able to contribute contextual qualifiers such as “how to use” or “when to use,” or “when not to use” based on their experience.
Contextual qualifiers are important not only when selecting, designing and implementing actions – what we might call ‘formulating solutions’ – but also in diagnosing where the failures and bottlenecks lie – what we might call ‘identifying problems’.
Identifying problems brings us back full circle to knowledge reuse in innovation and commercialization. Studies on knowledge reuse for innovation from NASA’s Jet Propulsion Lab at Caltech cited in Knowledge reuse – the process of knowledge reuse in radical innovation situations http://knowledgeputeri.wordpress.com/articles/knowledge-reuse-the-process-of-knowledge-reuse-in-radical-innovation-situations/ found that users were motivated to reuse others’ ideas if:
- They are confronted with an insurmountable problem with their current knowledge and resources.
- They re-conceptualized the problem and approach to require an ambitious new perspective.
- They believed that they can find useful existing ideas elsewhere.
Are not these common technology commercialization challenges, and again, more broadly, challenges of developing supportive innovation ecosystems? Additional motivating factors found in the studies included:
- Work processes that optimize exposure to diverse knowledge sources.
- Use of extensive personal knowledge bases with both weak and strong ties (for more about weak and strong ties see my blog: The ties that bind us: networks, strong links, weak links, and expanding our knowledge http://innovationrainforest.com/2013/06/30/the-ties-that-bind-us-networks-strong-links-weak-links-and-expanding-our-knowledge/)
- Culture within the project which encourages reuse.
- Availability of flexible ways to assess credibility of potentially reusable knowledge.
- Ability to scan for fit.
- Ability to quickly determine malleability of reusable knowledge.
Or, as some time earlier, Aristotle might have expressed the stages:
(i) Person seeking a solution to a problem, (ii) applying the solution, (iii) context, (iv) tool, (v) re-conceptualizing the problem, (vi) short-term or long-term impacts.
Next time: Response Ability: Learning from agile manufacturing.
Notes on the practice of innovation and technology commercialization
“Mankind are so much the same, in all times and places, that history informs us of nothing new or strange in this particular. Its chief use is only to discover the constant and universal principles of human nature.”
David Hume (1711–76). An Enquiry Concerning Human Understanding.
A handful of years ago a colleague of mine from the World Bank and I were having coffee in Washington DC with visitor from South Africa. She was listing the skills and tools that she believed South Africa needed to improve technology transfer and commercialization, especially from universities. At that time I had never worked anywhere in Africa, but I was stunned by the realization that some 90% of the items in our visitor’s list of wants and needs were identical to those of the countries in Eastern and Central Europe where I had more experience. Since then this commonality of needs has been verified by working in many other countries from Colombia to Kazakhstan.
In this blog I argue that there is unnecessary and frequent reinvention in creating technology commercialization systems, especially in developing countries, resulting in unnecessarily high transaction costs and less than optimum efficiency. Reusable knowledge tools, analogous to more general reusable knowledge or learning objects, can reduce reinvention of known processes, lower transaction costs, and increase technology commercialization efficiency. This is important because more and more developing countries are attempting to build ecosystems around technological innovation. To be clear, when I use the terms ‘learning object’ I mean a digital resource that can be reused to facilitate learning. In the application discussed here it’s helpful to think of a learning object for what it does (an agent) rather than what it is (its properties).
Wait a moment. Am I going against what I was preaching in an earlier blog about context and cutting and pasting solutions without paying attention to context? To be honest, maybe – a little. In Solving the Right Problem: part 1, March 24, 2013. http://innovationrainforest.com/2013/03/24/solving-the-right-problem-part-1/ I stated “Solving the right problem is all about context. A problem comes embedded in its own context; apparently similar problems in different contexts may have very different solutions. Likewise, solutions have their own contexts.” In the first part of that blog I looked look at one way of identifying a problem which may also bring out its context and suggest possible solutions. In Part 2 of the blog, Fallibility and the Making of Good Decisions, problem solving and decision making in Rainforest ecosystems was discussed. http://innovationrainforest.com/2013/04/30/fallibility-and-the-making-of-good-decisions-solving-the-right-problem-part-2/ Let’s see what, if anything, has changed.
I always have fun playing with my granddaughter’s Lego™ blocks, and the Lego™ block analogy is used frequently whenever knowledge is being collected and assembled from disparate resources. Mary Adams and Michael Oleksak in their 2010 book Intangible Capital: Putting knowledge to work in the 21st century organization speak about using Lego™ blocks to build models of a knowledge factory such as Google search or a medical device company (see the video “You can grow like Google” http://www.youtube.com/watch?v=brBwWqiSg8g)
Wouldn’t it be great if we could build technology commercialization programs, and more broadly supportive ecosystems, by plugging Lego™ blocks of learning into each other? There is an appealing simplicity. In the second part of this blog we will use some examples to see how far we might go, discuss the limitations of the Lego™ block analogy, and suggest that a reluctance to apply reusable knowledge tools to problems arises from a misunderstanding of the role of context.
We will also introduce the concept of ‘contextual qualifiers’ which are those pieces of knowledge that allow a user to assess whether a given policy or practice, implemented elsewhere, is truly relevant or applicable to the user’s environment. Conditional qualifiers are statements, which refer to knowledge Lego™ blocks (documents, videos, etc.) which ‘qualify’ the knowledge presented as being dependent on certain conditions.
As I was preparing this blog a Harvard Business Review article was published: Consulting on the Cusp of Innovation by Clayton Christensen, Dina Wang, and Derek van Bever (HBR October 2013, pp. 106 – 114) which discusses how incumbent consulting firms are being eroded by technology and other forces. The authors note that “only a limited number of consulting jobs can be productized but that will change as consultants develop new intellectual property. New IP leads to new toolkits and frameworks, which in turn lead to further automation and technology products.” In this new business model, consultants may not always re-invent solutions; a move away from work where value depends primarily on “consultants’ judgment rather than repeatable processes.” The authors call this “value-adding process business” in which “processes are usually repeatable and controllable.”
I’m guilty of raising several issues and leaving them hanging. Next time these will be pulled together and some conclusions drawn about the feasibility of reusable knowledge tools in technology commercialization.
Notes on the practice of innovation and technology commercialization.
“There’s a hole in the bucket, dear Liza, dear Liza…” A children’s song with obscure origins in 18th century Germany.
My last blog discussed strong and weak links in networks. We should now talk about how to connect networks and what is meant by a network ‘hole.’
Networks, like the bucket in the song, may suffer from holes. In buckets holes need to be plugged; in networks, holes are bridged. In social networks they are called ‘structural holes’ formally defined by Ronald S. Burt, a leading researcher in networks and structural holes at the University of Chicago, as ‘when two separate clusters possess non-redundant [that is, complementary] information, there is said to be a structural hole between them.’ This suggests that a broker or boundary spanner – such as an entrepreneur – who bridges the hole could gain competitive advantage by engaging in information arbitrage. Connecting networks of entrepreneurs with their own strong and weak links by bridging structural holes may provide access to valuable new ideas, alternative opinion and practice, early access to new opinion, and an ability to move ideas between groups where there is an advantage in doing so.
Previously we noted evidence from Innocentive http://www.innocentive.com that as the number of unique scientific interests in the overall submitter population increased, the higher the probability that a scientific or engineering problem was successfully solved through Innocentive’s crowdsourcing method. Bridging structural holes between disconnected networks brought a diversity of potential scientific approaches to a problem and was a significant predictor of problem solving success. Innocentive and ideas coming from other industries than the seekers http://www.innocentive.com/blog/2008/07/25/5-questions-with-dr-karim-lakhani/
However, let’s control our enthusiasm because in a moment we will see that there may also be a downside.
Brokerage is about coordinating people between whom it will be valuable to trust, but where there is an inherent degree of risk. An earlier blog [http://innovationrainforest.com/2013/03/24/solving-the-right-problem-part-1/] discussed the importance of understanding context; the effectiveness, or otherwise, of brokerage is also highly dependent on context.
Much more can be found in Professor Burt’s paper Structural Holes and Good Ideas
From what was discussed in my last blog we learned that within a closed network, with many strong links, no behavior goes unnoticed. Problems may be identified early and at low cost so that non-performing people can be removed from the network. In the early 1990’s, while working in Russia on technology commercialization issues I was surprised that the organization for which I was consulting employed several people who were related to the boss. Nepotism? No, said the boss; it’s an incentive for them not to screw up and have it immediately known by their relatives and be the cause of family shame.
Mentoring for early stage companies in venture accelerators relies of having robust knowledge of the business to apply the fail-fast fail-often principle, but this will be the topic of a future blog.
A series of new studies by Dr. Yuval Kalish of Tel Aviv University suggests that, in some cases, networking can do more harm than good. “If you’re at the intersection of two previously unconnected niches of a network, you’re occupying what I call a ‘structural hole,’” says Dr. Kalish. Filling that space can lead to prestige, opportunities and power ― or it may have quite the opposite effect.
“While it’s been reported that people who occupy these structural holes become more successful, some structural holes may be ‘social potholes’ that can harm you and your business,” he warns. For example, someone who cut across formal hierarchical organizational boundaries within a company may be resented.
Finally, networks are useless unless “transactions” occur among the networked parties. Communication is a transaction – an exchange of knowledge – and is also context dependent. The six degrees of separation theory that everyone is six or fewer steps away, by way of introduction, from any other person in the world is well known, but not much practical use unless at each stage a request gets transmitted. I figure I’m two links from the leader of one the world’s most powerful countries, but I very much doubt if any advice I send him would be received, and the transaction would fail.
Once when studying two linked group of researchers from a research center and a corporation in two different countries, my colleagues noticed an occasion when communications (transactions) between both groups were acting effectively, but then stopped. It was a mystery. Why? Answer: ‘culture’ prevented further transactions. The culture of the country in which the research center was located was that it was not good to transmit bad news. So, when the R&D ran into problems – completely normal and to be expected – they decided that giving no news to their corporate partners was better than giving what was perceived as bad news was the better action. Of course, from the company viewpoint it was essential for the company to know if there were problems so that the two partners could work together to correct them and proceed.
The remainder of the children’s song describes an increasingly frustrating tale of attempts to plug the bucket’s hole. Network brokers balancing cultures, transaction effectiveness and costs, trust, and so forth, may have a similar experience.
Next time: Notes on the practice of innovation and technology commercialization blog will be on vacation during August, and will return in September. Blog topics for the rest of this year will include how to create effective support ecosystems for technology transfer and commercialization in developing countries, agile commercialization systems, slow systems, and re-usable learning objects for technology transfer and commercialization.
Notes on the practice of innovation and technology commercialization.
I did not expect these ideas on solving the right problem to expand into three blogs, so let’s wrap up this topic for now. We talked about identifying problems and also discussed fallible indicators which support decision making. Lastly, some tentative hypotheses about indicators and decision making in complex environments (spaces) such as Rainforests were introduced.
Indicators related to technology commercialization outcomes will always have different levels of fallibility. The Association of University Technology Managers (AUTM) annual survey of US and Canadian universities and teaching hospitals focuses on a relatively small indicator set. I list these here so you can assess them for fallibility. Also note that one indicator may in turn depend on other indicators.
• number of staff employed in technology transfer offices
• research expenditure
• legal expenditure and reimbursement
• patent related activity
• start-up activity
• licenses and options
• license income
For many developing countries, or indeed developed ones, these indicators are not sufficient for capturing contract research, skills development, indirect benefits, or other outcome-related metrics. Remember, there is a difference between outputs and outcomes. Indicators are the critical step between identifying a problem (discussed in Part 1 of this blog) and determining possible practical solutions in the appropriate problem context.
Not recognizing this frequently leads to ‘jump to’ solutions also described in Part 1 of this blog. This may happen because our personal networks are not sufficiently open to new thinking; hypotheses 1 and 3 from this blog Part 2. In his 1984 book Decisions and Revisions: Philosophical essays on knowledge and value, Isaac Levi – now John Dewey Professor of Philosophy Emeritus at Columbia University – described seeking to identify potential answers to questions as a strategy to expand our personal body of knowledge (“knowledge corpus”). However, Prof Levy notes that all knowledge expansion bears the risk of importing error. Refusing to expand our body of knowledge of course incurs no risk whatsoever.
While reading Levy’s explanations recently I came across the paul4innovating three-part blog of May 1, 2013, which I recommend reading, The Innovation Bunker – Our Cognitive Traps about minimizing cognitive traps which states:
“I suspect we are all cognitively trapped most of the time. We are all more ‘hard-wired’ than we would care to admit too. That cognitive bias that ‘permits’ us to make constant errors of judgement, ignore often the advice around us and certainly gloss over the knowledge provided or staring us in the face. Innovation does need us to break out of these cognitive biases if we want to really develop something very different, more transformational.” I interpret Professor Levy as suggesting that another cognitive trap is our reluctance to expand our body of knowledge in case we introduce error.
These ideas also re-appeared in a recent paper in Research Technology Management, a journal for R&D managers: Reframing the Search Space for Radical Innovation. Searching to identify ideas for radical innovation, companies may frame their searches within the frame of existing markets or well understood applications. But, limiting peripheral vision in this way may not recognize discontinuous innovation shifts occurring. The paper notes however, as did Levy, that reframing an existing cognitive frame presents a significant risk.
Difficulties in identifying the right problem, understanding the degree of fallibility of an indicator, and cognitive traps are all characteristics of those complex ecosystems which support innovation. These are the ecosystems in which we will increasingly live and work. Attempting to avoid such troubles by retreating into more ordered domains will only reduce our access to new ideas and thus reduce innovative activity.
References to philosophers such as Levy, whose writings are somewhat dense with the language of symbolic logic, may seem disconnected from practical problems of technology commercialization and innovation ecosystems. I have tried to show that it is valuable to expand our knowledge of these domains, and that doing so increases our ability to make better practical decisions.
We can expect problem solving for innovation and technology commercialization in Rainforests to throw up challenges; our hypothesis H5 – that the fallibility of multiple fallible indicators is increased in complex spaces (such as Rainforests) indeed holds.
In the next blog I will try and show how these challenges become less intimidating and how new and energizing good practices can be developed, once the characteristics of such complex spaces are understood.
Next time: The ties that bind us: networks, strong links, weak links, and expanding our knowledge.
Management guru Peter Drucker noted that “making good decisions is a critical skill at all levels.” In mid April 1912, a decision was made based on an observation which proved to be fatally flawed. For ships sailing the North Atlantic routes at night in conditions where icebergs could be expected it was common practice to detect presence of an iceberg from the white foam splashing against the base of its dark bulk so that a decision could be made to steer the ship port or starboard to avoid a collision. Under the prevailing conditions of an ocean smooth as glass the lookouts on the Titanic saw no such indicator, and the rest is history. A more mundane example of an indicator is when we look for the presence of dark clouds in the sky to see if it’s going to rain and whether to make the decision to take a umbrella when we venture out. As we know from experience it may not rain; this indicator is unreliable or “fallible.”
We constantly, and unconsciously, make decisions based on multiple fallible indicators. Ideally, indicators should be clearly defined, reproducible, understandable, and unambiguous. As we have just seen, these features are not always possible.
Indicators are the critical step between identifying a problem (discussed in Part 1 of this blog) and determining a possible practical solution in the appropriate problem context. Not recognizing this frequently leads to ‘jump to’ solutions also described in Part 1 of this blog.
As Kenneth R. Hammond points out in his fascinating 2007 book Beyond Rationality: The Search for Wisdom in a Troubled Time:
“Because indicators vary considerably in their fallibility, from complete fallibility to perfect infallibility, whether the fallibility is due to random or deliberate factors, it is essential that we be able to measure their degree of fallibility so that we can discriminate among them. These measures simply indicate, in one form or another, how often an observable indicator is associated with an observable fact, condition, or event.”
In selecting a possible policy solution to a technology commercialization problem we may use multiple indicators to select one or more possible solutions. These might be (1) off-the-shelf solutions, modified according to the problem’s context, (2) re packaged existing solutions, or (3) new solutions formed from theory or practice.
In many technology commercialization applications we also wish to know how well solutions will scale up for widespread applications. There is a paradox in how we approach scaling of innovation. In theory we test an innovation in order to determine whether it works and has potential for scaling up, but in practice the decision to move toward scaling up must often be made on the basis of inadequate information, producing fallible indicators, or indicators of unknown reliability, and also before all contextual conditions are in place (context was discussed in Part 1 of this blog).
Next month in the final part of this blog we will finally reach the promised investigation – after this necessary detour – of how problem solving in less structured Rainforest ecosystems may differ from problem solving in more structured environments. What Rainforest elements impact on problem solving? Is identifying problems and possible solutions easier or harder in the Rainforest?
In the meantime let’s wax philosophical and state some hypotheses to chew over and test next time. “Wait” I hear you say “what’s philosophy got to do with technology commercialization?” I hope to demonstrate that the answer is “a lot.” These hypotheses are H1 to H5 namely:
H1. The fallibility of multiple fallible indicators is reduced in spaces where the characteristics are a balance of strong and weak links, with a sufficient number of weak links for stability but still enable access to divergent opinions and experiences (we will discuss weak and strong links in a future Blog).
H2. The fallibility of multiple fallible indicators is reduced in spaces where the characteristics are low transaction costs and high trust levels.
H3. The fallibility of multiple fallible indicators is reduced in spaces where the characteristics are efficient boundary spanning organizations.
Note what connects H2 and H3 is not just reduce transaction costs but transaction value (see The Rainforest book for a discussion)
H4. The fallibility of multiple fallible indicators is reduced in spaces where the characteristics are ordered domain focused on efficiency (such as Plantations), where the whole is the sum of the parts, and where optimizing the parts optimizes the whole.
H5. The fallibility of multiple fallible indicators is increased in complex spaces (such as Rainforests) where the characteristics are such that small actions may change the nature of the system. As a result to optimize the whole system, sub-optimal behavior of each of the components needs to be allowed. See my February Blog Imperfect Works.