Minimizing Self Inherent Limitations In Decision Making
By: Mi'Kail Eli'Yah
Tags: Life Hacking, Management, Life, Strategy, Life Lessons

Paradigm Shift Oracles

One of the common questions I kept revisiting are:

1. How to create deeper and wider understanding?

2. How to become hyperaware?

3. How do we hunt for black swans and paradoxes hidden in systems, and how to manage the unknowns operationally, particular those that will incur a hefty penalty if they creep up on us later?

4. How can we ensure optimality *¹? *¹ 如何画龙点睛, 而不画蛇添足 (rú hé huà lóng diǎn jīng, ér bù huà shé tiān zú): How do we draw the dragon and add the eyes, and not draw the snake and add the legs? If we don’t complete the eyes of the dragon, the dragon will not come alive, and if we add the legs to the snake, we may actually bungle the finality that was supposedly already complete.

Using The Double Blind Oracle

The double blind oracle is the most reliable model to date compared to other methods observed to date, and it usually converges to a point which I termed as — an optimal point of current known collective knowledge or awareness. It also puts the decision maker outside the equation.

From all the collected data, not omitting any, remove all the variables and/or scramble them to create subjective contextual blinding confusion and diffusion, and yet not degrading the objective context (Stay with this, and I will explain why this is important with a demonstrative use case). All adjectives and agenda or preferential directing adjectives are also removed or where technically descriptive ensure that it is neutralized to be objective not normative based on regional or human disposition.

Experimental example:

In a case of international conflict of interests, especially involving armed conflicts, news are extracted from both sides of the reporting. The nouns (countries, names, equipment types, etc) are switched and in some cases blinded or obscured to move it out of the context of the known story. The rigged news are sent to both sides and requested for feedback. In the controlled case where the original news are sent, each sides will "coincidentally" support unequivocally their own sides with partisan interests. However, when the rigged version of the news are provided, each side took the objective facets which they deem to be fair. When the original news is sent later, both sides display emotional outrage. However, when asked how the facts can changed just because it has to have their "preferred labels", their reasoning again degrades in accordance. When this was shown to another group who have vested interest in either side, and yet uninvolved in the survey, it brought to an awareness and admitted from a neutral point of view that pride and prejudice are clearly the cause of cognitive dissonance. This observation not only retains neutrality in judgement removing pre-conditioned pride, prejudice and preferences, it also sieves to demonstrate the selected candidates which have the most neutral views upon admitting the problem and demonstrates the will and willingness to attain that faculty of rigor reasoning.

Let the trusted blind assessors process them and come back to you with their conclusions. Next, go through a second run after sometime, and alter the variable again, but get another group of assessors to rewrite the same report in different settings. The data formatting group does re-phasing of both questions and data organization presented. Switch between the data formatting group and data assessment group, so that you can get a wider variety in both.

Carefully pick the less usual candidates as black cell assessors (this is the selected group of assessors which after several years of refinement that are observed to be rigorously logical and un-biased), especially those not related to the situation in the most remote and isolated means possible, and check the convergence to the optimal point.

"A quarrel is quickly settled when deserted by one party; there is no battle unless there be two." - Lucius Annaeus Seneca (circa. 4 BC — AD 65)

Sincere Perspicacity

In reality, it is not easy to differentiate between facts and fakes of the real world. There are many things we have to work to understand, unfortunately, most people are presumptive to conclude to what they want to believe and naively conclude on reporting sells on sensual bias, instead of being objectively curious, and augment their awareness.

There are so many things to catch up and yet we humans know so little, and it should frighten us to see so many people around us being so sure of themselves knowing so paltry while the few of us are sincerely laboring on sincere exploration and learnings. Laboring the truth is resource taxing, it’s often easier to tell a convenient lie instead.

To have quality growth in the right directions, we need quality people around us. Indeed, we are the average of the people around us. Invite yourself to a table of scientists, and / or stay with the topic long enough, you will be glad in time that you will no longer be as nescient to some really queer subjects.

Attesting Tendability and Survivability of The Decision

As stated in Strategic Crisis Management, the principle of Hormesis is needed to ensure that the decision is sound. We would need our harshest, yet most rigorous and brutally honest critics, to aid us in hunting for the blindspots and black swans scenarios to the argument. Therein, we should never pass any verdict categorically but intermediately conclude with caveats in the case of impending exceptions. However, ensure that the critics are ad logos, not ad hominem in the rigor. We need to ensure we are engaging the right people in productive discussions, and we need to identify the characteristics of productive discussion. The likely candidates would be part of the black cell assessors group. The strong caveat to this is to ensure that the trusted group does not fall into cloistered herd mentality.

While mining for the truth, also beware of the fallacy of relating correlation to casualty. This is also compounded or caused directly by regression to the mean (discovered in 1886 by Sir Francis Galton) where normally distributed systems, large deviations from the average will tend to return to that average with an increasing number of observations. This often leads us to falsely attribute cause-effect relationships. Hence, we may want the trusted groups to be diverse to inspect multifaceted on the matter.

Human associative memory looks for convenient explanations than regression to the mean. Human minds are strongly biased towards manufacturing causal connections. A way to correct for regression to the mean is to use control groups.

Caveat: Correlation is not Casualty

…. to be continued … productive fact and perspective mining with swarm intelligence …