“Uncertainties Design” (UD)

Significant implications arise when we redefine the fundamental nature of data for use across society:

The metrology and framing of the data to be gathered [1] will become vitally important. Making it important that we use “measurement uncertainties” to design ‘data harvesting’ plans and user instructions.[2]

This suggests that there is a need to:

(i) Develop an entirely new educational & scholarly UD discipline;
(ii) Develop UD Big Data computing certification programs;
(iii) Develop UD as a basic skill set in pre-college schooling.[3]

Uncertainties Design or UD will touch the entirety of Data Pre-Harvest Planning.

It will be used to design Uncertainty Measures for a wide range of subjects including: Accounting, Finance, Tax Law, Investing, Trading, Governance, Long-Term Climate Strategies, Ecosystems Stewardship, Weather, Energy, Food, Water, Sustainable Cities, and Economics Forecasting.


[1] In February 2019, researchers reported the results of a study which concludes that “students would probably exercise better judgement, say the researchers, if they knew more about measurement uncertainties and had a framework for determining when a difference is significant—things that are often left out of the curriculum.” cf. http://physicsbuzz.physicscentral.com/2019/02/more-data-can-lead-to-worse-decisions.html?m=1 and https://journals.aps.org/prper/abstract/10.1103/PhysRevPhysEducRes.15.010103;

[2] 06 February 2019 at 17:21 pm by email: Proposing “Uncertainties Design” to the researchers of Note [1] at Humboldt-Universität zu Berlin and Hofstra University: and to George Verghese (PhD, Curtin University).

[3] ‘ “Given what’s at stake, the researchers recommend that teachers make time to include these concepts in science classrooms and beyond.” ‘ “Since data, and judging the quality of this data, is becoming so prominent in our everyday lives, teachers in all subjects should try to incorporate this into their classes,” they write.” cf. Ibid., http://physicsbuzz.

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Might Friston’s “Free Energy” Markov Blankets = Liquid Membraning?

Karl Friston is looking to move forward describing the ‘free energy principle’, for example using the Friston/Ramstead/Babcock paper describing “all life in terms of Markov Blankets.” https://www.wired.com/story/karl-friston-free-energy-principle-artificial-intelligence/ and https://www.sciencedirect.com/science/article/pii/S1571064517301409

Another term for Markov Blankets might be “Liquid Membraning”. I use Liquid Membraning in problem-solving; the cognitive process appears to be akin to stitching up data-points into Markov Blankets. “Cloud-Boxing” is my other term. The suggestion here is to shift away from the rigid/fixed/ossifying nature of the term “blankets” to a flow-state framework that evokes particulates and osmoticity? Could the term instead be Markov “fluidics,” “flow-states”, “nets”, “clouds”, and/or “meshes”?

“Liquid Membraning”* is the deep enveloping that evanescently correlates datasets during the gloaming phase of a sort; stitching up cloud-points out of constellations of clues; out of a search to find the deep root cause of a challenge/problem/issue/threat [ie. to go back to the deep root first principle]. This produces a key that when turned unlocks the entire problem. Computationally you could call it super-swarming. It has been suggested that creatives approach a task ‘more intuitively’, not analytically. I disagree; these skills could be characterized as hyperfast analytical tasking. Think of super-swarming as standing in a cloud. Where the cloud is a set of data points. Among these are the discrete clues that I notice. Among these are the patterns and there a route of questions to find, to follow, down to the root clue/key that unlocks everything. Another way to look at this is to say I see all the trees in the forest, and then notice the clue paths and patterns that lead me back to the one different tree.

* definition circa 25 Nov 2018

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