Games of chance? Cause and effect in innovation ecosystems Part 1Posted: March 11, 2014
Notes on the practice of innovation and technology commercialization
As cousin Zeb spreads his money on the table, ready to play poker with Cuthbert J. Twillie (played in the movie by W.C. Fields) he excitedly asks, “Is this a game of chance?”
“Not the way I play it, no,” comes Twillie’s reply.
My Little Chickadee (1940) movie starring W.C. Fields and Mae West.
Is poverty a cause of crime, did my forgetting to change the oil in my car cause the engine to seize, was the lack of funding for patenting inventions in my university the cause of low technology commercialization compared to peer institutions, what was the cause of sudden rise in value of my company stock? Chance, probability, cause and effect are so embedded in our daily lives that may give scant thought to the mechanisms of causality – what cause produces what effect, either immediately or at a later time. In this and the next blog we shall show that time is the critical feature of causality in both “plantation” and “rainforest” innovation ecosystems (The Rainforest: The Secret to Building the Next Silicon Valley http://www.therainforestbook.com/ ).
If, in building ecosystems, we propose interventions that adjust the ecosystem’s sub-systems and, especially their connections, we need to know what the effect of these actions are likely to be. For example, in one case, improving communications and shared goals among universities, incubators and accelerators, resulted in improved efficiency and ‘fitness’ of the innovation landscape (see February 2014 blog The Gardener’s Dilemma https://innovationrainforest.com/2014/02/08/the-gardeners-dilemma-2/ for the concept of fitness).
Philosophers have been debating cause and effect for millennia. Aristotle identified four basic causes and stated that “we do not have knowledge of a thing until we have grasped its why, that is to say, its cause.” David Hume, whom we met in the January blog, made his readers think about whether we are justified in using inductive reasoning to understand events. Ludwig Wittgenstein’s, quoted in the March 2013 blog in this series, with his usual ability to both enlighten and confuse, dropped in the idea that “outside logic everything is accidental.”
Causality would seem to imply that we can create simple models such as event A causes B and in turn action B may be the cause of an effect C. For example the diagram below depicts a causal model relating price and demand, for which algebraic equations can be written. Q is the quantity of household demand for a product, P is the unit price, I is household income, W is the wage rate for producing the product, and U1, U2 are unmodeled error factors effecting quantity and price.
From Causality by Judea Pearl, Cambridge
University Press, 2000.
But wait a moment. In previous blogs we discussed how innovation ecosystems are non-linear complex adaptive systems where the same inputs don’t always produce the same outputs, where the behavior of a system is not the sum of its individual parts, where there are disruptions and emergence, and where effects occur in far-from-equilibrium states. Surely then, complex adaptive systems make a mockery of simple causation? So, what should we do to get a hold of cause and effect in complex adaptive innovation ecosystems? If we cannot, then we have lost our way completely. It is to these questions we shall now turn our attention.
Writers in several disciplines including biology, physics, economics, and sociology continue to add to theories and applications. However, it’s clear that there is much still to be explained. The remainder of this blog and the next will survey and summarize what is known about solving practical problems of cause and effect in complex adaptive innovation ecosystems. Much of what is discussed next is taken from the thought provoking work of David Byrne and Emma Uprichard.
Deterministic equations cannot be written for complex systems as they can for the linear system diagramed above. Other means are needed. A valuable concept is the ‘causal narrative’ – descriptions or cases, which may contain both text and numbers, that help to explain why some event happened in a complex system and how the state of the system came about. Such narratives are reconstructions of events similar to case examples and studies familiar from education, although here we are typically talking about short narratives and maybe reusable knowledge facets as introduced in the October 2013 blog in this series Create early, use often: Lego™ blocks, learning objects, and ecosystems. Part 2 https://innovationrainforest.com/2013/10/13/create-early-use-often-lego-blocks-learning-objects-and-ecosystems-part-2/.
In practice preparation of a roadmap for an innovation ecosystem demands an understanding of causality. A roadmap is a trajectory over time which might show what actions or projects are recommended and when they should begin and when they should be completed. In this situation causality means causality by comparison – comparison with the trajectories of systems which have similarities, or in the language of complexity those which are ‘near neighbors.’ Knowledge of what happened to produce an existing state can enable choices to be made of which actions – causes – can produce future expected results – effects. The late Fritz Ringer, professor emeritus of history at the University of Pittsburgh, described this as “the kind of causal analysis that will explain why the course of historical development ultimately led to the explanandum* in question, rather than to some other outcome.”
In this introduction to cause and effect in complex systems I have a feeling of having laid myself open to charges of either over simplifying issues or making them vague. Part 2 will attempt to correct either situation and look at the practical consequences of trajectories in complex innovative ecosystems and all their component parts. W.C. Fields was correct, it’s not a game of chance.
* “explanandum” is not a familiar word to many of us, but quite a handy one. An explanandum is a phenomenon that needs to be explained, and its explanans is the explanation of that phenomenon.
Next time: Cause and effect in innovation ecosystems Part 2