Complexity – it’s not simple: Solving the right problem Part 3

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.

2 Comments on “Complexity – it’s not simple: Solving the right problem Part 3”

  1. Henry Doss says:

    Your Wittgenstein is showing! I like the way you are dissecting the “complexity of complexity” into simple components — or at least more easily understood components. The “fallibility of fallible indicators” is nicely captured, particularly the exponential decrease in reliability, relative to increased complexity. As always, it comes back to picking the right question, as answers are easier than questions. Or, said differently, being mindful of your choice of constraints (questions). A nice piece!

  2. […] the March, April, and May 2013 blogs in this series we speculated about, not just complexity, but solving problems in complex […]

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