(P4): The OODA Loop

Complex Diagram of the OODA Loop. See description through link below diagram.

OODA Loop Diagram Long Description

The OODA loop (see picture above) was created by John Boyd to help explain why some fighter pilots were much better than others in aerial dogfights. His basic premise was that you could win if you made good decisions faster than the other pilot. This was oversimplified over time to mean just faster decisions, without the part about better decisions.

The most important part of the OODA loop for advocates is the “Orient” phase. Successful use of the Orient Phase requires not especially the observation of where the opponent is, but rather a deep understanding of how your opponent thinks about reality. What does your opponent value? What risks are paramount in their thinking? After all, you want to know where your opponent is going. As Wayne Gretzky said, “I skate to where the puck is going to be, not to where it has been”.

For example, in an advocacy negotiation over, say, a complex support for a student, the Orienting Framework of a typical ISD or special education administrator focuses on cost, required program resource commitment (including staff, skills, general availability), precedent (will hundreds of other students/families request the same service if we support this student?), and the political consequences of agreement to the requested support from other staff, other parts of the education system, and the general public.

While it might seem as though these concerns are matter-of-fact, they are not. Underlying all of them is the decision-making rationale AND fear-driven concern for personal and system liability if things go sideways for some reason. Because the fear of such liability is never entirely rational (we can’t know the future), the Orienting Framework is sensitive to surprise, regardless of its source.

The use of the OODA Loop as a tactic in disability rights advocacy is often about producing novel challenges to the system as it is now and as it thinks/feels now. Thus, a successful challenge to a system with a novel destabilization requires that you have a clear understanding of how your target thinks and feels.

These challenges don’t have to be radical or revolutionary. They must, however,  be initiatives that the system hasn’t run into before.

Often, there also need to be several destabilizations. A bad habit of naïve advocates is to create a destabilization (say, a complaint) and then sit around waiting for a response. Delay (because it requires nothing but avoiding action) is always available as a default response for the system you are challenging, and it is used as much as possible by that system.

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(P4): Record-Keeping

An ancient written text not in English

Record-keeping is the great unsung heuristic of effective advocates. It is unsung because it seems tedious and time-consuming and seldom drives change by itself.

In the bad old days, record-keeping was incredibly tedious. For example, imagine what it took to transcribe a recording of a meeting before digital frameworks were available to support such tasks.

For example:

  • Using a live transcribe app on a phone to record a meeting and generate a correctable text.
  • Spoken note recording.
  • Composite resource documents so that related information can be reviewed in one place or document by a simple email invitation.
  • Notification when emails are read and by who.
  • Easy encryption.
  • Easy sharing of info and events in an advocacy network through apps like Slack.
  • Social Media as an adjunct to advocacy work.

It is also far easier to collaborate and organize around advocacy information, initiatives, and events through separate personal, support groups, targets, and public venues.

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(P4): Tactical Heuristics

Typical Military Tactic Maneuvers: frontal attack; break through attempt; contra flanking including extended wing contra flanking and mobile force contra flanking; successful break through including stabilizing flanks and rear operation; Hammer and anvil including unyielding front anvil and mobile hammer; Flank counterattack; decoy and destruction; Break through with advance and Counterattack.

ngd-Excuse the typos in the image. I couldn’t find another image that communicated the same stuff….

Here are some guides to making tactical decisions when you are advocating. There are many more out there and you will discover them through your advocating experience as well as the experience of others:

  • Record-Keeping: Deep record keeping has always been an advantage that advocates could have over the systems. The data that advocates develop tend to be useful for advocacy-if you have a record of it. But we often don’t make use of it effectively. This is especially unforgivable with modern digital record-keeping tools easily available.
  • The OODA loop: The OODA loop is a famous model created for pilots involved in dogfights. But its uses go well beyond this original inspiration.
  • Multiple Advocacy Initiatives: Advocacy Targets often interpret multiple advocacy initiatives as far more threatening and anxiety-provoking than single initiatives, even when multiple initiatives require almost no additional effort.
  • Nucleation: Several low-profile similar advocacy initiatives can be used to produce system change without triggering significant counter-responses.
  • Cycles as exploitable weak constraints: Everything in a CAS operates in cycles. We typically don’t pay attention to this, even though there are real exploitable opportunities if we take the time to observe and learn.

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Part 4: Advocacy Heuristics

A complex image. See Text from Image and Notes link below the image.

Text from Image and Notes

What is a Heuristic?

As a tool for disability rights advocacy, a heuristic is a framework of meaning that provides a way of developing an advocacy tactic, strategy, or organization. At its simplest, a heuristic is a rule-of-thumb, that allows us to make decisions about what to do more easily. Mostly heuristics are used to develop tactics, but they can be used at any level of decision-making and for any advocacy purpose. Heuristics represent a distillation of someone’s experience and reflection on what worked in the past.

Even a very capable heuristic guarantees nothing. Heuristics have their own built-in bias and using them automatically prevents you from noticing that bias. Remember that heuristics are initiators of reflection, discussion, and collaboration to reach a decision for action that respects the current reality and the current context, not ways to save time and thought.

So, remember that you, too, come to advocacy work with an existing set of heuristics and their biases. Capable advocacy should always be an opportunity to question, explore, and reframe the automatic responses we all have living in our world of failed social justice.

I would also note that it is common for advocacy organizations to use heuristics more and more automatically as they age.

The image is from Scott Page who has done a lot of work in the usefulness and challenges of diversity as a framework for problem-solving. His work is noted in the text through the link under the image.

The complement of this view of diversity and advocacy (kind of the other side of the coin) is detecting weak constraints in the problem by deliberately avoiding the homogenization that arises in groups. This way of respecting the lived experience of persons is called distributed ethnography (DE). DE is a complement because diversity in groups helps with both problem-solving and detection of weak constraints if approached properly. These ideas are explored more completely through links in the “Text from Image and Notes”.

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(P3): System Aging and Our Organizations

A model of how corporations age. See long description link below the image for details

Long Description of Image

There are patterns in the aging of our advocacy organizations. Because these patterns arise out of Complex Adaptive Systems (CAS), they are not mechanically or programmatically determined. They arise out of the interacting possibility spaces created by the governing constraints that allow the creation of the CAS in the first place. These governing constraints are Mission, Reproduction, and Hierarchy.

So, there is no rigid development pattern in advocacy organizations. Instead, there are a series of choices about enabling relationships in the complex possibility space that create the actual pattern of the organization that arises. What follows are some observations I’ve made about these patterns during the last half-century of my personal development as an advocate and the many organizations to which I have belonged.

Early Patterns

When an advocacy or social support organization is first created, it usually prizes Mission over Reproduction and Hierarchy. Partly this is because small new organizations don’t have much money, the people who are in the organization generally got into the work they do because of the way they value that Mission and the non-mission skills they have are relatively unformed compared to their understanding of the importance of the Mission. The effect of this reality is, in many ways, to set up the organization for a difficult transition that accompanies the successful growth and expansion of Mission impact.

Transition to the Two Missions Framework

There is a transformation of the organization as it tries to create the infrastructure that is necessary to sustain the work. Creating this infrastructure can be thought of as creating a new governing constraint called Reproduction. This Reproduction infrastructure includes a Board, improved methods for getting program income. a system of accounting and monitoring the use of the funds, community relationships, etc. It is typical that building this infrastructure produces mistakes. Boards crash and burn, the bookkeeper that was handling the limited funds is discovered to have embezzled some of the limited money, lack of HR experience produces very poor management decisions about the people who work at the organization, etc. The punishment (however that plays out) of these errors either destroys the organization or shifts it to a model of Two Missions (Mission as Purpose and Mission as Reproduction). If the punishments are severe enough, but the organization survives, there is a tendency for the surviving managers to value the financial/social (Reproduction) Mission over the original purpose. This causes an organization-specific development (i.e., aging) process focused on managing the relationship and value of both Mission and Reproduction.

Long Term Paths for Two Missions Organizations

Once an organization has transitioned to the realities of the Two Missions, there are many paths that the organization can follow as it struggles to manage the relationship between the sometimes complementary, sometimes conflicting demands of these two governing constraints.  It is common to try to use Hierarchy to manage these challenges.

As a Governing Constraint, Hierarchy never exists separate from the Two Missions. But, management beliefs about hierarchy themselves constitute a governing constraint that defines the possibility space for the management view of the quality of solutions to organization problems.

Management view of the level of control necessary to solve management problems is often set in concrete, narrowing the range of “acceptable” ways to solve problems, which in turn guarantees poor problem-solving. Under ordinary circumstances, some public failure of the organization (embezzlement, reputation failure, or similar organizational system issue) must occur, and it is not unusual for the existing governance structures, like the Board and the senior managers to turn over before there is any major change in the Governing constraint of Hierarchy.

The usual choice to resolve this problem is to increase the control offered through Hierarchy. This choice is made out of fear, not because it genuinely offers integration of Mission and Reproduction, and increasing control often starts the organization down a path of technocratic zombism, where the original Mission no longer has any meaning.

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(P3): The Ways of System Aging

The Panarchy Cycle; alpha-phase: reorganization; K-phase: Conservation & Stabilization; Omega phase: Release (Creative Destruction); r-phase: Exploitation & Growth; Poverty Trap (between alpha-phase and r-phase) is Insufficient Resources for starting New Growth; Rigidity Trap (between K-Phase and Omega Phase) is Holding on to old systems.

All CAS age (even us, or maybe especially us!). But seeing aging in a CAS is tough. There is no perfect way to describe such aging, but it is important to grasp the general contours if we are to make use of aging in our advocacy efforts.

Aging applies as much to advocacy organizations as it does to those organizations that are the object of our advocacy efforts. The Panarchy Cycle is as good a model for system aging as I have found, and it has the advantage of “face” usefulness. It is important to remember in what follows, however, that we can do things to change the path of aging in a CAS. If I develop arthritis in my hip and it gets bad enough, I might have a hip replacement surgery. If that surgery is successful, the quality of my life can take a huge leap. But, despite the improvement in my day-to-day activities, I am still aging.

The Panarchy Cycle is usually described as four repeating steps:

1.Reorganization

2.Exploitation and Growth

3.Conservation and Stabilization

4.Release or Creative Destruction

A commonly used example of these steps is a forest system after a large-scale fire:

1. The “empty” landscape after the fire becomes populated by weeds and other fast-growing plants and small animals and micro-organisms.

2. As the landscape becomes denser with life, fast growth is gradually replaced by plants and animals that can store resources and more easily alter the forest to fit their needs.

3. Eventually, the forest becomes stable and many of its possibilities for novelty are locked up in resources controlled by subsystems of large tree species, symbiotic relationships, organizing of resource flows like water, animal families and reproduction, and so on.

4. The CAS organization becomes increasingly brittle and subject to easier breakdown.

The two big drivers of the development of enabling relationships in the CAS are the “poverty trap” in the early development of the system, when it is tough to use resources because they must be changed (the enabling relationships must be created) by those organisms that participate in early development, and the “rigidity trap”, when most resources are already tied up in some subsystem, and organisms have set patterns for their use and reproduction. Rigidity is defended and becomes brittle and opens the forest to disturbances that cause some level of cascading breakdown in the system’s ability to adapt to further disturbance.

It is often difficult for us to accept this kind of aging cycle in our own organizations or those we target for advocacy because it seems as though the problems we experience would be easy to fix if we just go ahead and fix them. This apparent ease of problem-solving is based on our false idea that the organization is a machine or a computer.

The isolated problem is often easy to fix. But fixing the problem also changes the CAS in long term ways that are hard to see, by destabilizing some enabling relationships and generally making enabling relationships harder to create. This is the unavoidable burden of unintended consequences.

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(P3): The Ways of Governing Constraints

A female soldier doing orthopedic rehabilitation under PT supervision.

Governing Constraints create possibility spaces by eliminating large swaths of possibility that are irrelevant to the meaning of the governing constraint. When there is more than one governing constraint in an organization, the governing constraints create a complex possibility space that permits much more complex possibility choices.  When the CAS is working the way it is supposed to, the multiple governing constraints also allow the populating of the space by more complex enabling relationships. In turn, this drives a more complex CAS development process and a more complex aging process.

But the interaction of multiple governing constraints is, well, complex. Multiple constraints can undermine or enable each other, and the choices made by participants in an organization can produce extremely different organizational behavior. The three governing constraints of any advocacy organization or the usual organizations that are subject to advocacy (Mission, Reproduction, and Hierarchy) frame but do not determine that behavioral path.

The primary result of the Hierarchy Constraint is a massive reduction in the possibilities of enabling relationships. The best example of this is the aphorism about bureaucracies, “Anything not required is forbidden”. Hierarchy is a societal choice, not an objective law of physics. It hinges on the belief that the universe is mechanical, and that restricting choice improves the causal power of the mechanical links that supposedly guarantee outcomes.

The staff will be flogged until morale improves, etc.

Hierarchy can be based on logical relationships or power relationships. Logical hierarchies are not used to run organizations. The use of a power hierarchy always reduces the possibilities in the space created by Mission and Reproduction.

The Mission, at least in new systems, is the best statement of what the system can create. The relationship between Mission and Reproduction is complicated by the way that a complex adaptive system ages.

If a system ignores Reproduction to maximize it’s Mission, the system will run out of resources to realize the Mission. If the system maximizes reproduction at the expense of the Mission, then the system will become like a zombie, focused exclusively on more resources, with no regard for Mission outcomes.

Because human systems are made up of humans, it is always possible to change the relationship between Mission, Reproduction, and Hierarchy, creating a new complex possibility space. But it becomes more difficult to change these relationships as the system ages, and the likelihood of some kind of system collapse (not required to be total) increases.

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(P3): Advocacy Organizations (Good Times and Hard Lessons)

An infinity sign colored like a rainbow

In our time, advocacy is organized around networks of advocacy organizations. This networking through organizations was a natural result of both the problems and successes of individual advocacy and the ongoing struggle for disability civil rights.  Advocacy organizing brings with it its own strengths and weaknesses, and it won’t surprise any reader of this blog that I view this understanding of advocacy organizations through the lens of Complex Adaptive Systems (CAS).

Any advocacy organization (or for that matter, any system we might focus on for advocacy) has at least three Governing Constraints:

  • The organization Mission (why it exists)
  • The organization plan for Reproduction (how it keeps the doors open)
  • Its framework of Hierarchy (how it controls)

To understand why advocacy organizations have their ups and downs, and how advocacy organizations age, you must understand how these Governing Constraints both cooperate and collaborate for the organization’s work over the course of time. Each of the Constraints creates its own possibility space, and the actual trajectory of the organization is a complex interweaving of collaborative and competitive choices in real-time.

The various parts of the organization’s infrastructure (Board, financing system, staff morale, network relationships both positive and negative, who is defined as a threat or competitor, etc.) also reflect this multi-constraint dynamic.

In modern organizations, even non-work time can reflect this dynamic to a varying extent.

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(P3): Meta-Systems Advocacy- Part 2

Famous cartoon of different stakeholder views of designing a tree swing. None of them except the customers original vision of a simple tree swing works as the customer intended

Long Description of Cartoon

More about the Differences Between the Two Kinds of Plans

Causal Models of change, like logic models, tie causal links between the steps from the beginning of the plan to the outcome. But, there is inherent uncertainty in change plans targeted at any CAS, which is inconsistent with the requirements for developing an operational plan like a logic model. We pretend that the problem and the plan are mechanically causal, eliding over the actual complexity. The effect of this is to weaken the so-called causal links and contract the potential outcomes of the plan.

Typically, we don’t just do this because of the obligation to create a mechanically causal plan for our proposal submission. We also do it because we try to make the plan match the perspective of the proposal reviewers about what constitutes a plan that is both creative and “realistic”. After all, if our plan is seen as too ambitious or too open-ended for the available money or the purpose of the RFP, it will be rejected.

But, if we were to stick very closely to the plan as we drew it up for the proposal, we would have great difficulty achieving our valued outcomes. So we fudge our proposal to meet the expectations of the reviewers while trying to keep our fidelity to the valued outcome we want to achieve.

This is a hard thing to do, and we tend to pull back on the impact of our outcomes to meet the realities of the funding possibilities available to us while reframing the outcomes and the steps as marketing memes in the RFP-required causal network.

A “plan” that respects the realities of a CAS is more like a plan for discovering the infrastructure of a room in the dark. You build a model of the room through experiments and exploration, not the traditional model of a plan. If you use a traditional model, you will miss important information about the room. If you experiment and evolve your plan based on what you discover, you can reach an understanding of the room, instead of imposing an inadequate meaning on the room using the traditional logic model approach.

Planning for change in a CAS requires constant engagement, not the “roll out the plan like dropping a rock off a cliff” approach that is almost universal in modern service/support/advocacy organizations.

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(P3): Meta-Systems Advocacy-Part 1

A map of Meta-impacts of systems- Evidence-informed effective policymaking: Policy rules promote evidence us and transparency; Sustained relationships, mutual trust, aligned medium and high level beliefs; deliberative processes systematic, collaborative. Political Forces: Advocacy for inclusion of broad groups and skilled chairing; Advocacy for establishing and maintaining relationships across the policy community; Advocacy for evidence use enhancing meta policy structure and processes.

How would we create a Complex Adaptive System whose purpose was to produce individually customized and emergent supports for individuals over the course of their lifetime?

First, we need to have a clearer grasp of the difference between mechanical causal outcome systems, and Complex Adaptive Systems (CAS). The planning systems we all use now were designed from a framework of mechanical causality.

Mechanical Plans

Governance of a Mechanical Plan (say, a logic model for a grant proposal) is through a valued outcome. The outcome serves to organize the process of its achievement. The plan is a series of parallel linked steps that mechanically lead one from the other until the outcome is achieved. The adequacy of the plan is evaluated in the concreteness and causal (read measurable) links of the steps to the achievement. The point of the plan is that it works like a machine/computer if it is implemented properly. If it doesn’t work, the plan is treated like a broken machine/computer; we look for a broken part/process and replace it without changing the rest of the plan.

CAS Plans

Governance of a CAS Plan is through an outcome, as well, but the outcome is more like an intention, and the issues of measurability that arise in a mechanical plan are turned on their head. An intention is a governing constraint that does not dictate the steps required to achieve it. Instead, the governing constraint creates a space of possibilities within which we expect to find the realization or creation of the outcome/intention. Such an open approach is much more in line with how we all actually achieve an intention to do something we have never done before.

Imagine a pair of fraternal twins, both about 6 months old. A new ball is placed the same distance from each of them. Maybe the ball has flashing lights or bright colors on it, and each of the infants forms the intention of getting to the ball, grabbing it, and playing with it.

One of the infants carefully reaches out with a hand and carefully moves bit by bit to get closer to the ball until getting close enough to grab it. The other infant rolls about energetically until getting close enough to grab it.  Both strategies for reaching and grabbing the ball are part of the possibility space that the original intention creates.

There are many others. For example, the infant might communicate to a parent to bring the ball closer, or ignore the ball out of frustration, etc. Which strategy is picked is about the specifics of the CAS; the infant’s temperament, where the infant is in relation to the ball, the environment in general, other things that are around, and so on.

The strategy is not mechanically determined, but more felt through by trying approaches and muddling towards a solution. The second time the approach is tried, it is easier to reach the outcome and gets increasingly easier with practice.

This CAS-based way of thinking also has the advantage of being progressively customized to the dynamic actions of the infant. It matches the reality of the uniquess of the infant, and this uniqueness is reflected in the organization of the specific child’s brain.

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