I met Dave Snowden about ten years ago on the Knowledge Management (KM) speaking circuit when he was with IBM working as a researcher with classic KM icons like Larry Prusack and others. Dave has always had a remarkable vision of alternative approaches to solving complex business problems. With his guiding wisdom, I began my personal learning journey to apply complexity science and narrative inquiry techniques to support our client’s innovation and growth practises.

To start our conversation in the right direction, below are some basic working definitions and other expert sources to help provide more context of this discussion - and in my next blog I will provide more concrete examples of innovation applications leveraging these approaches.

First, complexity evolves around the process of self-organization and a rejection of traditional notions of representation. From the earlier wisdom of John Holland we leaned how adaptation can be regarded as an engineering problem. His inventions started with the genetic algorithm and provided a systematic way to design and study complex adaptive systems with computer simulations. The research of the Santa Fe Institute and the University of Michigan’s Centre for the Study of Complex Systems have further clarified the working definitions so these models can be more readily understood by more practitioners.

First, in everyday conversation, we call a system “complex” if it has many components that interact in an interesting way. More formally, we consider a phenomenon in the social, life, physical or decision sciences a complex system if it has a significant number of the following characteristics:

  1. Agent-based: The basic building blocks are the characteristics and activities of the individual agents in the environment under study.
  2. Heterogeneous: These agents differ in important characteristics.
  3. Dynamic: These characteristics that change over time, as the agents adapt to their environment, learn from their experiences, or experience natural selection in the regeneration process. The dynamics that describe how the system changes over time are usually nonlinear, sometimes even chaotic. The system is rarely in any long run equilibrium.
  4. Feedback: These changes are often the result of feedback that the agents receive as a result of their activities.
  5. Organization: Agents are organized into groups or hierarchies. These organizations are often rather structured, and these structures influence how the underlying system evolves over time.
  6. Emergence: The overlying concerns in these models are the macro-level behaviours that emerge from the assumptions about the actions and interactions of the individual agents.

Understanding these characteristics can help business executives understand the dynamics of innovation processes where diffusion is so critical for a new product or service to be commercialized successfully.

One of the most interesting perspectives that Dave Snowden and his research networks have contributed is their understandanding of how language and stories richly communicate meaning and how rapid feedback loops in emergent explorations can help us see what we often cannot see.

For example, a recent project we did with Dave was with a global pharmaceutical company where in a one day workshop we were able to collect from thirty patients over 200 stories and index them to help us identify new pathways for potentially altering the market ecosystem dynamics to yield more successful brand image outcomes for the pharm company. We were also able to identify 4 new patient segments that two other global research firms had not been able to identify and millions had been invested and these techniques were able to share patterns never seen before. A complex puzzle becomes simpler with these techniques.

With the war for winning and retaining market share - companies that learn the art of applying complexity science techniques in their innovation practises will have a competitive edge. I have come to the conclusion that the techniques that Dave Snowden and now firms like us training in these methods is we are helping our clients see what others cannot easily see.

For companies to be able to identify patterns which cannot be seen from traditional market research approaches to gain insights on new products, services or identify a disruptive innovation advantage cannot be valued enough. With the compressed window for competitive advantage or lack of sustaining innovation market influence — seeing what others cannot easily see should be top of mind for every CEO or CMO who is serious about innovation and having a window for market differentiation..

Three recommended books to help you get started in learning more about complexity science and innovation techniques are: Emergence by Steven Johnson, Harnessing Complexity by Robert Axelrod and Michael Cohen, and Complexity and Post-Modernism by Paul Cilliers. If you would like us to send more information on complexity science management and receive a copy of our whitepaper research in this area, please contact us at info@helixcommerce.com.


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