Traditionally, a strategy is about ways, means, and ends. In operational planning mode, this means detailing the paths, tools, and outcomes, as in a logic model.
A real strategy provides more than this. It realizes a framework, not just for describing, but also dynamically coordinating ways, means, and ends, and providing guidance when the context of your effort changes-not dictating ways, means, and ends as a result of being somehow able to predict the future. After all, the larger world is always changing, and when it does, if we are faithful to our values and our hopes, we should support our change goals by shifting the relationship between ways, means, and ends.
Dynamic coordination means that you can change anything arising from your strategy at any time if the context changes (which it always will). Operational plans only allow you to tweak a way, a means, or an end, not fundamentally alter their relationships.
I’m going to take you through 2 genuine strategies. These two strategies were actually used in the real world, and they have all the complexity and depth that we should expect from our change strategies, even if the scope of our strategy will probably be much narrower than these. I will discuss one below and then describe the other in the next post.
The Allied Strategy in World War II:
You may have heard the phrase “unconditional surrender” as a description of the Allied strategy against the Axis in WWII. This was not just a slogan to mobilize citizen support. It was a strategy that grew out of the failure of the “negotiated settlement” that had ended WWI. In fact, the belief was that the settlement had led directly to the rise of fascism in Germany, and contributed to the rise of fascism in Japan and Italy.
Unconditional Surrender had profound consequences for both the prosecution of the war and its impact on all the participants. A reasonable guess would be that twice as many people died as would have if negotiation short of unconditional surrender had been a possibility. Ditto for infrastructure destruction, the number of people who acquired life-long disabilities, and the dramatic shrinking of social capacity that occurred in the Axis states. It is unclear whether the Holocaust would have been less humanly destructive, or even more so, had a settlement been allowed in, say, 1943. The industrialization of the US and the Soviet Union were dramatically accelerated by this strategy of Unconditional Surrender, leading directly to the Cold War. The race for nuclear weapons would have been slowed had the war ended in 1943. At the same time, it is possible that Germany would have acquired nuclear weapon technology had the war been foreshortened by a settlement.
My point is that this choice of a strategy has had real, concrete consequences down through post-war history to this day.
In the first edition of Planet Medicine, by Richard Grossinger, he describes a tribal method to begin the hunt for caribou which has a fascinating connection to our modern approaches to building and maintaining consensus in the creation and implementation of complex planning (and also complex change).
The short overview of the ritual is that the shoulder blade of a previously hunted caribou is heated over a fire until cracks appear in it. The bone is oriented to map the hunting territory of the tribe, and the cracks are interpreted as the paths of caribou herds. A tribal decision about where to start the hunt is made on the basis of the information on the bone.
The ritual practice, in one form or another, has been used by many different communities. The question that Grossinger raised was why the practice survived when there is no correlation between the map and the actual location of herds of caribou in the hunting territory.
He offered two kinds of possibilities:
The ritual solved the problem of how to get started quickly on the hunt when the actual location of the herds couldn’t be determined. In this environment of significant uncertainty, it was better for the tribe to start hunting right away and gradually discover where the herds were, rather than wasting time arguing among themselves about where to start. Certainly anyone who has been in a modern meeting trying to make a decision about a problem without enough information to do so will sympathize with this very effective way of producing consensus about doing something. Perhaps the most significant deficit of our modern approach to dealing with large uncertainties is the way social, political, and financial conflicts and their negotiated resolution eliminate any useful consideration of inherent uncertainty.
The second possibility was more subtle. Because the effect of the ritual was to have the tribe start their hunt in a more or less random place in their hunting territory, it was not possible for the caribou herds to evolve a defense against the hunting plans used by the tribe. The relationship between the tribe and its food source was kept stable by the ritual, adding a measure of constraint to a truly uncertain task.
Change advocacy as a social action framework is more like hunting caribou than it is like designing and building a nuclear power plant. There is significant uncertainty in creating and implementing change in Snowden’s “complex systems”, and when humans try to manifest that change, they will, by the very nature of what they are trying to do, engage in the social construction of some fiction to begin their change work. They will use shared values and their common commitment to outcomes consistent with those values to begin engaging with the target of their effort, learning as they go, evolving a plan of change through action, and, hopefully, achieving the change they desire by adapting their action to fit the constraints and ongoing evolution of the target.
We need to accept the uncertainty of what we do and the complex context within which that uncertainty lives if we expect to enhance our change effectiveness. In an environment of uncertainty, detailed pre-action planning wishes uncertainty away through the apparent detail and concreteness of the plan, creating the arrogance of the planners of Fukushima, who thought they could afford financial and political compromise in the creation of their fictional starting consensus. Better to learn while on the path of change, than to assume that everything important is already known.
These last two posts have looked at the role of uncertainty in justifying an open, one step at a time, evolutionary approach to change. I have tried to emphasize this difference because of the astounding dominance of detailed operational planning as a method to secure a predictable change in our global culture. As useful as such planning is in creating complicated artifacts (like nuclear power plants), the failure of typical operational planning to include the lessons of an evolving environment brings immense danger with it. There is no greater danger than the assumption that because our social focus should be on the complicated artifact we are trying to create, the only relevant considerations should be the negotiation necessary to secure the beginning of the project. Social, political, and financial conflicts become the only “problems to be solved”. Everything else is in the detail of the plan.
Because of the dominance of operational planning as a substitute for a real strategy, I am going to take one more crack at framing the differences between this global convention and evolutionary change action, by discussing one of the oldest distinctions in systems theory: The difference between Open and Closed systems.
Today, we use the word strategy to describe any planned action model to achieve outcomes. But historically strategy served a different purpose than simply describing an action plan.
A strategy is a change framework designed to deal with two realities:
The uncertainty of the future
The scarcity of available resources (including funding, staff, skill sets, networking, values, etc.).
An operational plan, on the other hand, by its nature is supposed to draw boundaries and eliminate uncertainty by detailing the specific action path to outcomes before the project starts, and by applying a budgetary limit for the achievement of those outcomes.
In this first post, I’m going to talk about the difference between Risk and Uncertainty, since uncertainty is part of the reason why we need a strategy.
Risk and Uncertainty are not the same, though we tend to think that they are synonyms.
Risk is statistically calculable-which is to say, we can assign a probability to the risk because we have some source of data that allows us to estimate how likely the event is. The proper response to this risk probability is to mitigate risk, to take steps that will allow us to react quickly to reduce the impact of the event that we are worried about.
We can’t assign a probability to real uncertainty though it is common for organizations to define uncertainty as risk, so it will be seen as manageable and requiring no particular change of current behavior. If we confuse uncertainty with risk, we will end up imagining events (since we are uncertain as to what will actually happen), assigning them a fictitious probability, and developing fictitious mitigation strategies.
The perfect example of this was the Fukushima nuclear plant meltdown. In the planning for building the power plant, the corporation’s design system had discussions of how to quantify the likelihood of an earthquake that would threaten the integrity of the plant’s operation. These discussions were only incidentally about the actual risk. They were mostly about the initial costs of building the plant to mitigate the results of an earthquake since that structural mitigation would dramatically affect the cost of building the plant and the potential profit from its operation.
You can easily imagine ongoing arguments between engineers and financial planners about the right investment in earthquake mitigation. What they settled on was a model that made the construction of the plant able to withstand an earthquake that was 10 times stronger than any earthquake in Japan’s history (that is, experiential history, written history, narrative history, since there was no way to access historical evidence of earthquakes back beyond these sources of data).
Of course, the actual earthquake was several orders of magnitude greater than any earthquake in that historical record and resulted in the complete loss of plant integrity and an ongoing destruction of the area around the plant and in earth’s oceans that will continue at some level for longer than anyone currently alive will be around to see it.
Obviously, the planners for this nuclear plant had no idea what the actual risk of this event was. They concocted a model of the “risk” that would allow them to build the plant, satisfy state regulators and provide predictable financial projections to the organization and its investors. The model was entirely wrong but was financially, socially, and politically useful.
While nuclear meltdown is seldom a possibility in our change efforts, we often follow exactly the same line of reasoning that the engineers and financial planners did for the Fukushima plant.
The answer to uncertainty is not an operational approach to a mitigating a fictitious risk, but the development of a real strategy.
Next Post, Part 2: Fictitious approaches to uncertainty as social glue.
I discussed in my last post the reality that most real ongoing advocacy involves dealing with target systems in the complex realm. This means that there won’t be straightforward expert based advocacy solutions that can be easily implemented through typical logic model plans. Instead, we must find our way a bit at a time, trying change ideas, seeing what they do, and building on the efforts that produce some successful change. The description of an advocacy effort directed toward a target is more like an evolutionary process than it is what we think of as planned change. Like any evolutionary process, both the advocacy effort and the target are changing and responding throughout. Advocacy in complex systems is a learning process, not a plan execution.
I worked for Michigan Protection and Advocacy starting in 1981, and roughly half of my direct advocacy work was representing students and their families in special education issues. It was very interesting work, and the evolution of MPAS special education advocacy illustrates advocacy in complex systems.
When I first began advocating on behalf of students and families, it was very easy to win the negotiations with schools. Mostly, this was because the school districts considered special education a small part of their overall responsibilities, whereas we saw special education advocacy as one core service of what we did as advocates. We knew the laws and rules better than the schools, we had more practice at things like special education complaints, and we were expected to be well prepared when we went into the education planning meetings on behalf of a specific student, unlike the schools who were running (sometimes) hundreds of education planning meetings in a few weeks. So, we won most of the negotiations, even if they went to formal hearings. Also, the families we represented were very grateful, and we liked that. Finally, it was easy and quick for us to solve the problem ourselves without taking the time to teach the families how to advocate on their own behalf in future.
This worked well for the a few years. But at first slowly and then much more quickly, a number of things began to happen.
The first was that local school districts and ISDs began to hire attorneys for specific cases, and eventually, keep the attorneys on retainer. Especially when districts had attorneys on retainer, they used them in lots of situations, since they were paying them anyway. This meant that schools stopped making simple rules errors, and the cases themselves became more complex. In turn, this meant that advocacy required more research, more time, more layers of appeal, more meetings, and fewer victories.
The second thing that happened was that families told their friends in the special education community and we had a sizeable uptick in the number of requests for advocacy support. Also, since we had inadvertently encouraged dependence, we got the same families back year after year for representation in their education planning meetings. This produced a sharp spike each year in such requests, and, after a few years, we no longer had the time or resources to respond well to all the requests.
Which is to say, that we ran into a resource limit as a direct result of our success. This limit was not just some lack of funding. A great deal of skill is required to properly negotiate such cases successfully, and there simply weren’t enough experienced special education advocates to handle the demand. In fact, the Michigan Department Of Education estimated that 10% of the special education planning meetings each year were contentious (i.e., needed an advocate), and that amounted to ~20,000 or so meetings a year. Since a single advocate could only handle about 100 cases a year, that meant we need 200 full time advocates. That was never going to happen.
The organization had to respond to this evolutionary demand from the environment, and there were basically 3 alternatives. These 3 alternatives are roughly the same for any advocacy organization that faces a resource limit (an evolutionary challenge) in implementing their mission.
Keep Current Practice: Most organizations struggle to maintain what they see as the most important parts of their change advocacy, and to try to alter the forces in the environment that are requiring this strategic reconsideration. This approach almost never works. Even when it does, it is typically for a relatively short period. Instead, the organization becomes less capable over time, bureaucratizing its workflows in order to gain control over the demand. One of the telltale signs of this is a sharp rise in what the British refer to as “failure demand”-calls, letters and other contacts from clients asking for updates or responses where the response is that nothing has changed. Failure demand sucks up time that the organization already doesn’t have because of the resource squeeze.
Modify the Current Model: Various versions of constraining the actual response are made across mission related demand so that the organization can control demand. In the case I experienced, the core responses of individual advocacy and legal responses were kept more or less intact, but the percentage of cases considered for these two responses was reduced through the use of an issue priority system that was updated each year. These contacts were responded to by an organized Information and Referral process. I&R became the primary response for roughly 80-90% of the contacts, and the number of demand contacts has plateaued at many thousands.
New Way of Doing Business: The one discussed during the transition I experienced was to move to a community organizing model, in which organization staff would work as organizers training and providing technical assistance to families in local school districts, building up a more or less permanent cadre of special education advocates around the state.
As you can imagine, there are many potential variations of these three canonical responses to resource scarcity. There isn’t a “best” response. There is only the response that the advocacy organization chooses, which maps out the organization’s future for many years, and lays out a path of skill and capability (learning) that the organization will track until another evolutionary challenge comes along.
This is the way that dealing with complex advocacy always works. It has little in common with the pretty picture of a logic model implementation. It isn’t that plans are of no use. Rather the plans are executed inside this evolutionary environment in which the advocacy effort and the target both adapt.
Next Post: Some other ways of looking at advocacy in complex systems
