Seven Complexity Implications for Multi-Stakeholder Networks

Learning to work with complexity is absolutely essential for people working with multi-stakeholder networks.  It’s key to effective leadership, network development, impact measurement, communications, and change strategies.  A multi-stakeholder change network developing all these for a complicated rather than complex system is bound to fail…or at least fall far short of its objectives.   

When co-leading a workshop with The Change Alliance in Nairobi a few weeks ago, colleague Jim Woodhill brought forward again David Snowden’s cynfin (pronounced kun-ev’in) framework that presents the distinctive essence of complexity in an easily-understood way. 

Snowden starts with two major contextual factors that determine the appropriate strategic framework for an initiative.  The factor of level of abstraction is related to  trust – trust in whether people share the same values and goals, whether they believe they have the competency to do what they say they want to do, and whether they actually have worked together enough to knowing the meaning of each others’ use of words.  Where trust is high, people can handle relatively high levels of abstraction.   

A second factor is culture. Snowden distinguishes between a techno- or physical-oriented situation that can be minutely described and controlled, with a learning context where there are many unknowns and knowledge is emergent. 

This produces four different operating environments that require four different ways of strategizing and four different logics for any initiative like a network.  In the Visible Order domain, bureaucratic and highly structured approaches such as those associated with government application of laws and rules, and traditional business production lines are appropriate.  Strong control rules account for the possibility of low trust and a high number of transactions.

In the Hidden Order domain professional skills become much more important, since many more scientific judgment calls are made against an array of options.  This is associated with complicated situations, where there can be a large number of interacting variables but they can be understood through reviews of experiments, and controlled.  Think of sending a person to the moon.  This is the realm of scientific management.  This requires high abstraction and trust in technical abilities. 

Rather than techno-focused, the complex setting is people-focused. Rather than joining around hard science knowledge and standards, the high abstraction comes with people collaborating voluntarily around shared values and concerns.  However, the collective work is usually very broadly defined in terms of objectives – in fact, the work involves actually clarifying the objectives (eg:  what does sustainability really look like?).  People are continually learning about each other and how they can collaboratively realize aspirations.  Cause and effect cannot be separated, because they are intimately intertwined. 

The fourth action domain is chaotic.  In this situation, there is inability to learn or predict because the situation, people and issues are all changing so rapidly.  Patterns do not exist and do not emerge through interactions.  Crisis management is needed.

Seven Implications

1.     One implication is that multi-stakeholder change networks like Global Action Networks (GANs) by their very nature tend to operate in the complex domain.  They operate with high diversity in participants, in terms of culture, language and objectives.  They are addressing “stuck” topics and B-HAG (big hairy audacious goals).  They cannot look to history as a guide.  Their decision model is probe-sense-respond. 

2.     This emphasizes the importance of GANs developing sophisticated learning processes.  Traditional history-based research takes a second seat of importance.  GANs must support action learning projects to test and “emerge” potential answers to collective challenges.  They need to be good at understanding worst practice and adapting good experience in one part of a network, to work in another…as opposed to linear “scaling up” best practices that require a highly predictable environment to be useful. 

3.     Operating in the domain of complexity also emphasizes the importance of not thinking of “a strategic plan”, but of “strategic planning” as an on-going process. Rather than operating with detailed projects, a GAN should identify some broad goals and continually adjust as new opportunities and new learning arise.  “In a complex domain we manage (in order to) recognize, disrupt, reinforce and seed the emergence of patterns;  we allow the interactions to create coherence and meaning,” says Snowden. 

4.     Effective structures tend to take the form of well-connected networks, without a dominant center.  Empowerment for self-organizing is important. 

5.     And complexity once again emphasizes the value of leadership that is leaderful.  More on these implications next week.

6.     For communications, complexity stresses the importance of creating virtual platforms for discussions where people can probe, and sensing what is emerging to formulate appropriate responses – and this de-emphasizes the role of communications as “telling people”.  Supporting development of informal communities is key.

7.     Probably the most problematic implication for operating in the complexity domain is with impact measurement and evaluation.  Linear tools like log frames are appropriate for the techno worlds, but not for the learning domains.  They suppress the ability to respond to emerging knowledge and opportunities.  Moreover, they are predicated upon identifiable cause-effect relationships.  Although learning-based evaluation systems like outcome mapping are beginning to develop, we lag in tools and in understanding of those who are demanding evaluations. 

Significant development issues are associated with this Cynefin model.  For example:  does a GAN aim to shift the issue it is working on, into the realm of scientific management?  Is that possible? 

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