Brenda Zimmerman is concerned about why some Collective Impact initiatives are falling short of expectations. She believes that part of the problem is that leaders are “Snapping Back” to old habits and inappropriate modes of management and not recognizing the kind of ecological resilience needed to address complex issues. In her keynote presentation to the Collective Impact Summit convened by Tamarack in Toronto 6-10 October, she talked about how to understand change in complex social systems, the type of resilience that is needed to address this, and how to mitigate the risks of “Snap Back”.
“Managers would rather live with a problem they can’t solve than with a solution they can’t fully understand or control.” - Eric Bonobeau, researcher in swarm intelligence and CEO of Icosystems
Brenda’s research focuses on organization and system level change and her writing draws on insights from complexity science to make sense of changes in health care systems, hospitals, public policy, and social innovation. John Kania and others have credited Zimmerman with helping to understand the critical role that emergence plays in creating innovation in Collective Impact initiatives. Contrary to much of current management thinking which views organizations through the metaphor of “a well oiled machine”, complexity science is built on the metaphor of “a living organism” or an “ecosystem”, and advocates an ecological approach to resilience which uses adaptation and deep change through “creative destruction” to allow the system to continually learn and adapt. It is this perspective that has become so important in understanding how Collective Impact approaches can potentially deal with complex issues where traditional approaches of management and planning have failed.
“Stop trying to change reality by attempting to eliminate complexity.” - David Whyte
What we need to understand about complexity
Zimmerman starts by explaining what we need to know about complexity by describing three types of problems:
- Simple (Known) – Eg Making Soup from a recipe
- Complicated (Knowable) – Eg Sending a Rocket to the Moon
- Complex (Unknowable) – Eg Raising a Child
Because social systems are inherently complex, we need to understand that they require an approach that is very different from the kind of technical and engineering expertise that is required to send a rocket to the moon. Complex problems require an appreciation of uniqueness, interaction, being adaptable, and staying tuned in to what is happening.
Brenda uses the fanciful example of “throwing a bird” as opposed to throwing a rock. At some point the bird wakes up and starts to exert its own influence on its trajectory. You need to create an “attractor pattern” in order to influence its ultimate path and destination, such as setting out food, and the same approach holds true in social situations, where the behaviour of the system can be largely explained by understanding “attractors” and relationships and coordination among parts can be more important than the parts themselves.
Again, Zimmerman draws on lessons from biology to explain how this works: Living systems follow simple relationship “rules” or “minimum specifications” that allow for complex adaptability, such as in the example of birds flocking where they are able to respond to stimuli 50 times faster. Only three rules define flocking – maintain a minimum distance, respond to those around you, head towards the center - but they give enough of a sense of direction without over-specifying and allow birds to respond much more efficiently as a group to both threats and opportunities than they can as individuals. The difference in approach can be clearly seen by juxtaposing with the characteristics of a traditional planning checklist:
- Lay out clear task
- Optimal process known apriori
- Steps to optimal process known and articulated
Minimum Specs/Simple Rules
- Need to adapt quickly to changing circumstances
- Set boundaries of acceptable behaviour
- Leave room for flexibility or creativity
As a result, instead of the “Buy-In” to pre-conceived plans sought in traditional “Checklist” engineering-style approaches, “Minimum Specs / Simple Rules” use attractors to create coherence and emergent outcomes are encouraged and shaped by creating “Ownership” in an ecological approach to resilience.
Preventing Snap Back
Zimmerman illustrated the problem of “Snap Back” in four real-life examples of Collective Impact initiatives (provided by John Kania) that were failing because leaders were devolving back to traditional management approaches that were inappropriate. Typical causes of failure included:
- Thinking that a leader knows best, causing many stakeholders to disengage
- Trying to import “Best Practices” and impose them on the group with no experimentation
- Valuing “Content Expertise” over “Context Expertise”, ignoring the wisdom of the group
- Pushing too hard to drive solutions and measuring only efficiency through dollars and not effectiveness
How can we mitigate the risks of Snap Back?
Brenda suggests that we need to understand and embrace the paradox of complexity if we are to avoid “snap back” to old habits and ineffective practices. “Complexity encourages us to create optimism, to create energy, to recognize progress which is not necessarily visible from the traditional management perspectives”. Here are four practical principles to make Collective Impact initiatives more durable:
- Relationships are Key - Choose your “Audience of Significance” that you will look to for validation
- Pay Attention to Engagement - Ensure resources are available for listening and engaging on an on-going basis. Protect space in your calendar and reward others that take the time to do this. Listening, engaging, and pattern recognition must be supported forever – not just at the beginning.
- Be Strategic Thinkers, not merely Strategic Planners - Reinforce strategic processes that recognize the iterative nature of profound strategic thinking, and always look for the small differences that could create a tipping point.
- Don’t Confuse Quick Wins with Quick Fixes - Success is not a destination in complexity. Make resources available for safe-fail experiments and value context expertise as much as content expertise.
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