Games of chance? Cause and effect in innovation ecosystems Part 2

Notes on the practice of innovation and technology commercialization

“That is why, according to Viktor Mayer-Schönberger and Kenneth Cukier’s book, Big Data, ‘causality won’t be discarded, but it is being knocked off its pedestal as the primary fountain of meaning’. But a theory-free analysis of mere correlations is inevitably fragile.”
Big data: are we making a big mistake? Financial Times, March 28, 2014

“You have on each table cardboard, drinking straws, glue, string, balloons, paper cups, and other bits and pieces. Use these materials to build a model your local innovation ecosystems.” These were the instructions given to multiple groups of five or six from among those of us who participated in the recent Global Innovation Summit in San Jose California. An undisclosed prize, based on unexplained criteria (a slice of innovation humor?) was to be given to the winner. This modeling game was a lot of fun, provided insight to some, and also raised the question of what kinds of models might represent innovation ecosystems?

We usually build system models to simplify the world around us in order to better understand it – and hope that in such simplified models we have included the important features of the actual system. For example, it is not possible to include what may be large numbers of possible causes producing observed outcomes.


A model may be a physical structure, as in the picture above of one team’s product from the Global Innovation Summit, or in the form of mathematical equations (in some cases this may have been the way an actual system was designed), computer simulations, or even a set of stories. Using narrative to understand the dynamics of innovation ecosystems will be explored in a future blog in this series.

One difficulty is that the more we expand and generalize models to take into account wider circumstances the more unmanageable they become and usually we have to make additional simplifying assumptions or model small subsets of a system.

At this point it’s important to make a distinction between complex and complicated systems. Complex (adaptive) systems are what we have been discussing in the past few blogs. We noted, for example, that in such systems the same inputs may not always yield the same outputs and the whole is more than the sum of its parts. Complicated systems may be broken down into smaller and smaller constituent parts (superposition principle); the whole is the sum of its parts and behaviour is completely predictable. An economy is complex. An modern passenger aircraft is complicated. Both systems are composed of a system elements connected in a system structure. Both kinds of system perform specific system functions in its system environment. Both systems may have a permeable system boundary allowing inputs from, and outputs to, the external environment.

The difference is that complicated systems can be fully modeled whereas complex systems are inherently resistant to modeling.

My colleague Henry Doss, is his series of Forbes blogs on leadership, put the issue well “We live and work in a world that wants specificity and predictability, but we live and work in systems that defy predictability…  A strong leader of complex systems knows this truth about systems, and understands that oftentimes judgment, intuition and commitment are more important than measurements, projections and predictions.  Knowing that systems are resistant to predictive models, and are rich in unforeseen, often positive, outcomes is a powerful foundation for effective leadership.  And it’s an awareness that will make for more informed and nuanced decision-making.” Does Synergy Really Mean Anything?

Do difficulties relating effects to causes mean that we cannot model complex adaptive ecosystems? In fact no, even without predictive capabilities progress can be achieved. Sharon Zivkovik at the University of Adelaide, Australia, in her article Addressing Society’s Most Pressing Problems by Combining the Heroic and Collective Forms of Social Entrepreneurship notes “According to complex adaptive systems theory, under certain conditions interactions between independent agents produce system-level order as agents interact and learn from each other, change their behavior, and adapt and evolve to increase their robustness. Empirical research has shown large complex systems such as communities require enabling conditions to be created in order to maintain the coordination required for emergence self-organization and adaptive capability.”

These communities may be said to be engaged in ‘collective entrepreneurship’ by integrating knowledge and resources from different, and sometimes diverse, parts of the ecosystem, capitalizing on properties of far-from-equilibrium complex adaptive systems such as self-organization, and using the resulting resources to address difficult problems.

Dr. Zivkovik further notes “The aim of interventions at the point of self-organization is to enable community system members and their resources to recombined into new patterns of interaction and working arrangements that improve the functioning and performance of the community system and displace the old way of thinking.”

It is this recombining into new patterns of interaction or moving from one ‘basin of stability’ to another in a Rainforest innovation ecosystem that allow us to build, if not complete ecosystem models, then at least some predictability around these stable regions. I use the word “moving” and this indicates what’s missing in our discussion so far; a model must be dynamic and include flows such as those of knowledge, and capital in the innovation ecosystem. These are relatively new ideas in the context of innovation ecosystems and present considerable challenges to the modeler. However, it does seem that we should be able to model such systems beyond string and Styrofoam™.

The first part of this Blog is at: Games of chance? Cause and effect in innovation ecosystems Part 1

Next month: A review of the 14 blogs so far in this series, their connections, and what I hope we have learned.

3 Comments on “Games of chance? Cause and effect in innovation ecosystems Part 2”

  1. […] differences may stalemate any efforts to diminish ongoing conflict and strife. Read more here.Games of Chance? Cause and Effect in Innovation Ecosystems Part 2Alistair Brett, Technology Commercialization Advisor for T2 Venture Capital, from The Innovation […]

  2. […] as we described in April’s blog Games of chance? Cause and effect in innovation ecosystems Part 2… which reported the work of Sharon Zivkovik on social entrepreneurship. Prof. Zivkovik reports on […]

  3. […] example of heavy reliance on cause-and-effect logic as best-practice. For more on causality see April 2014 blog in this […]

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