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Change is hard, especially where structure creates imbalance in perspectives, power and impact. When years of good intentions yield underwhelming results, it is time to question the approach. In this four-part blog series, Scott Orsey explores the model used by scientists to measure health and well-being outcomes. He arrives at three conditions for change. Might these be the building blocks for the transformation we seek?
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In this series of blog posts, I have used a mathematical representation borrowed from statistical analysis to observe implications of our efforts to improve health and well-being outcomes for children. In the first article, I employed the Odds equation to show that our odds of success are driven, as assumed by our model, by a number of factors that are multiplied together. In the second article, I looked at the right hand side of the Odds equation to argue that, to be successful our work cannot be dominated by one sector – all sectors matter. In the third blog, I considered the left-hand side of the equation, which suggests that there is no one right lens from which to view the challenge and that those with power and influence must make room at the table for others – all sectors in. In this post, I will ask what is needed to apply this knowledge. What do we need to do to ensure that our collective work is successful?
To answer this question, I’ll attempt to integrate these thoughts, looking at the model as a whole to suggest a final important implication. Here again is our model:
Success is a tall order because coordinating such a wide range of actions and aligning goals is complex. If we try to centralize control, the information and management systems needed would be immense. Imagine tracking a half dozen factors and a half dozen outcomes in each of a half dozen sectors, all the while sharing perspectives, leadership, and actions. Overwhelming. In fact, typical social systems involve more factors, outcomes and sectors than this.
Efforts using such techniques as Collective Impact as researched and documented by the Stanford Social Innovation Review have had some success with cross-sector coordination. Collective Impact, they find, requires a common agenda/shared vision, a shared measurement system, mutually reinforcing activities, continuous communication and a backbone support organization. The best examples of Collective Impact’s application have brought organizations together around one or two desired outcomes, such as cleaning up a polluted river or improving educational achievement, to rally multiple stakeholders to take coordinated action in mutually reinforcing activities.
However, our model suggests that this sort of approach – rallying stakeholders around a single outcome (or a small set of outcomes) – has its limits. At the very least, it is challenging to define a common agenda and shared vision that will be viewed as equally important for many different sectors (or park benches from my previous blog post). The outcomes that matter to different sectors are different. In fact, the model formulation suggests that the more stakeholders you bring to the table, the more challenging this will become. Yet, as we’ve established, all sectors matter and we need all sectors in.
The Collective Impact approach makes a nod to this challenge, by acknowledging that the central infrastructure, or backbone organization, must facilitate communication and coordination and must not control the actions of the participants. However, if organizations cannot agree on the “shared vision,” then they will never be successful in improving their community. Or will they?
I’ve always enjoyed examples of biomimicry – where engineers borrow solutions from the natural world to tackle challenging problems. Well known examples include the hook and loop of Velcro or the use of surfaces that imitate sharkskin so submarines can slip through the water with ease.
By employing some creative biomimicry of our own, I wonder if we can propose an alternative solution to the “shared vision” problem.
It turns out that in nature, there are many examples where multiple organisms work synchronously (if not cooperatively) even though the individuals never sit down as a group to define a shared vision. Okay, there are no examples in nature where animals, other than humans, sit down to define anything, so perhaps it is more accurate to say there are examples where no two individuals have exactly the same vision of the world. Instead, the individuals are driven in their self-serving desire to survive and be safe. Flocking birds such as starlings come immediately to my mind. They travel together, yet no single bird takes the lead. When threatened, they move in unison to avoid, confuse and confound their predators.
The foundation of our understanding of flocking behavior was first proposed by a Russian biologist, Dmitrii Radakov, in the 1960’s. He observed that schooling fish can successfully avoid a predator even if only a few fish know where the would-be predator is coming from. Each fish simply needs to coordinate their movements with those of surrounding fish. In this way, the few fish “in the know” can guide a huge school by initiating movements that are then imitated by their neighbors. The imitation wave propagates through the school. The result is highly democratic behavior where any fish can start the chain.
Mr. Radakov’s observations were later codified to create an algorithm. Eventually, computer programs used these algorithms to model flocking and schooling behavior to great success in such films as Disney’s The Lion King and Finding Nemo. The algorithms apply three simple rules to each member of the group:
Later observation revealed that in the real world, animals have a little broader vision. They actually pay attention to not just their immediate neighbors, but to the action in their local neighborhoods.
What a nice analogy to our DoX Odds problem. We have observed that all sectors matter and that each sector has its own outlook on the world, and we know that all of those outlooks are observing the same system. We also know how hard it is to create a shared vision.
What if instead of investing so much effort in the shared vision, we simply did more of what the flocking starlings do: observe the actions of others and make movements based on our perspective? To facilitate, we would really only need to build the backbone to communicate two simple things: 1) what others are doing and 2) where others are going, especially those in the immediate neighborhood. This would provide sufficient information to the other participants to respond… stay close, but not too close, and go the same way.
What’s needed to make this work? In my view, there is one glaring barrier to success. In a word, “safety.” Our culture is built on the Darwinistic foundation of capitalism. Threats abound, and organizations build offenses and defenses. Eat or risk being eaten. The risk of sharing what you are up to is great.
“Shared vision” may not be so important, if it’s even feasible. Safety, (or maybe trust), is the true barrier to mutualistic behavior.
>Read Part 1, Part 2 and Part 3 of this series.
This final observation completes my journey to derive implications from our Odds equation model. In taking this journey, I have explored several implications that derive from the statistical model and our efforts to create successful health and well-being outcomes. In summary, I’ve
If we admit that we need others, ensure others have equal standing and guarantee that everyone feels safe, we will accelerate the transformation to a world where every child achieves their optimal health and well-being outcomes.
Author’s Note: My deepest gratitude to Amy Hunter, PhD, MPH, who reviewed this series, challenged my thinking and offered feedback and insight.