(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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Author: disabilitynorm

hubby2jill, 2dogs, advocate45+yrs, change strategist, trainer, geezer, pa2Loree, gndpa2Nevin

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