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The practitioner’s landscape by Glenda H. Eoyang

Posted by  Shawn Callahan —January 10, 2005
Filed in Collaboration

Papers which provide practical advice on the design of complexity-based interventions for organisational issues are rare. E:CO (a new complexity journal) is attending to this shortcoming by including a practitioner’s section in its journal. In the latest volume, Glenda Eoyang presents her practitioner’s landscape based on more than 15 years of applying complexity science to organisational development and management practice.
The practitioner’s landscape is a matrix for categorising complexity-based tools and techniques. Its two dimensions are: 1) the conspicuousness of the issue of interest, which Eoyang calls the ‘phenomena’; and 2) the type of complexity-based techniques and tools in relation to its level of abstractness.


Phenomena—issue of interest
The dimension labelled ‘phenomena’ has three categories:

 

    • surface structures which represent issues which are evident to anyone in the organisation such as interpersonal conflict, lagging sales and client dissatisfaction;

 

    • evident deep structures which initially reveal themselves as a sense of disquiet or an uneasy feeling by people in the organisation that something is wrong but they are unable to detect the cause. With exploration, however, these patterns can be revealed and upon discovery make sense to the organisation;

 

    • subtle deep structures where neither the instincts nor first order investigations yield explainable patterns. Here Eoyang (2004: 56) suggests more analysis is required: “The complexity of these situations transcends the capacity of one level of complexity tool and demands more subtle and/or complicated methods and models.”

Tool type
There are four tool types starting from the least abstract (practice) and moving to the most abstract (mathematics):

 

    • practice is the act of implementing an intervention and discovering the outcome without predicting, in fine detail, the outcome at the outset;

 

    • descriptive metaphors use the rich language of complexity science, such as ‘attractors’, the butterfly effect and fitness landscapes, to help people see problems from new perspectives. These metaphors are simply ‘descriptive’ as they don’t attempt to accurately describe the human process in motion using complexity science.

 

    • dynamic metaphors focus on the similarities between the underlying dynamics of the human system and other non-linear dynamical systems. It is interesting to note that like other authors writing on the application of complexity science to management practice (notably Ralph Stacey) Eoyang primarily views complexity science as a metaphor for human systems.

 

    • mathematics which represents the many different mathematical models, such as agent based modelling, which simulate complex systems.

Eoyang wrote this paper specifically for practitioners to help them identify a range of different types of complex issues they might face in an organisation and assist them in selecting an appropriate tool.
Most importantly it provides a useful language for discussing organisational issues in a new way which will create quite different conversations. Solution designers will be asking questions to discern the depth and conspicuousness of the structures in operation and in doing so a greater awareness of the complex nature of many issues will be developed by decision makers. Hopefully this will lead to organisations investigating and applying techniques other than those which assume a rational, linear world.

This paper, however, subtly suggests that answers will come with the right amount and depth of analysis, applying the right technique. Here we must show caution. Complex systems are unpredictable, especially in the long-term. In many cases, the only option available to us is to act and see what happens. Eoyang describes this approach in the ‘practice’ toolset yet seems to contradict the principle in the ‘mathematics’ category where models are thought to be able to discover the subtle structures mostly hidden in the complex human system. Mathematical models are exception of discovering counter-intuitive phenomena which help understand a system but should only be viewed as simulations rather than predictors.
While it is important to introduce new and vibrant language in order to conceive new solutions based on complexity science, complexity based management practice will continue to be held back if we over complicate what we say. For example, rather than say, “These descriptions are based on apparent isomorphisms between chaotic or complex adaptive patterns in physical systems and emergent behaviour in human systems.”, say the descriptions are based on apparent similarities. Simple language will help the discipline being labelled a fad by the hard-nosed, battle-weary, managers who we must convince of its usefulness.
It will be interesting whether practitioners embrace this framework and begin to populate the matrix with examples of techniques and tools. It seems to me that many of the Cynefin techniques fit in the ‘practice’ and ‘descriptive metaphor’ categories and probably have been mostly applied to the ‘subtle deep structures’ issues such as understanding tax payer behaviour, occupational health and safety and the role of trust.
I believe this will be a useful framework which I will use to discuss issues with my clients.

Eoyang, G.H. 2004. “The practitioner’s landscape.” E:CO 6(1-2):55-60.

About  Shawn Callahan

Shawn, author of Putting Stories to Work, is one of the world's leading business storytelling consultants. He helps executive teams find and tell the story of their strategy. When he is not working on strategy communication, Shawn is helping leaders find and tell business stories to engage, to influence and to inspire. Shawn works with Global 1000 companies including Shell, IBM, SAP, Bayer, Microsoft & Danone. Connect with Shawn on:

Comments

  1. I’m very pleased that you find the article useful. Your retelling of the story is excellent. I do hope you and others will use the landscape and continue to help it evolve in the future.
    Your two concerns are valid.
    As to the language that serves to cloud rather than clarify: I try to make things clear, and each draft and rewrite burns away more and more of the big words and confusing syntax. Apparently this one needed another round. Hearing others retell the points in their own language also helps distinguish what is central and relevant from what is just noise. That is one of the reasons why your blog piece is helpful to me. Thanks.
    The other distinction is more central and needs to be restated many times. The reason to study the complex patterns in a human system CANNOT BE to predict and/or control its behavior. Because of the complex interdependencies, no one can predict the systemic outcomes of an informal action or a formal intervention. The purpose of the practitioners’ landscape is not to predict and control on a system-wide scale.
    On the other hand, the purpose is also not merely an academic description or impotent understanding of the emerging dynamics.
    The purpose is to inform action in the moment. The more I understand about the emergent and patterned dynamics of a human system, the more intentional I can be in imagining multiple options for action, choosing one, and learning from its effects.
    I believe this is equally true regardless of how subtle or explicit the patterns and regardless of the level of abstraction of the analysis tools. Whenever we slip into the expectation that a human system will respond with machine-like cause and effect precision, we’ve left the world of complexity behind, whatever tools and metaphors we use to talk about our work.
    Thanks again and please let me know how you use the landscape and how you might help us refine it.
    Glenda

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