Insight: Mechanics of "Knowledge Out of Nowhere" and the Verification Algorithm

Where does insight—the so-called "knowledge out of nowhere"—come from?

Let us consider the state of the DM's (decision maker's) cognitive system when working on a dead-end problem. All standard and bold hypotheses have already been tried, the result is zero, and the problem has reached an impasse.

The brain continues to continuously ponder the problem, but it cannot generate a new trajectory. The reason is that the exact same neural ensembles are being activated. From constant excitation, these connections strengthen, yet they produce no solution.

[Dead-end Problem] ──► Continuous Neural Network Activation ──► Neurotransmitter Depletion in Axons
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[Alternative Neural Network] ──(!impulse overshoot)─────────► [INSIGHT / Epiphany]

As the same neural circuit is intensively exploited, neurotransmitter reserves are depleted in the axons of its constituent cells. At the moment of critical deficit, an impulsive "!impulse overshoot" occurs: excitation is dumped onto a neighboring, previously uninvolved neural network rich in neurotransmitters.

A person perceives this moment as a sudden insight or epiphany. On a physiological level, this is not a "miracle," but a random integration of the dead-end problem with a previously isolated array of experience from a neighboring neural network.

Insight Validation: Verification Protocol

Obtaining an insight is a necessary condition for escaping a systemic impasse. Tools from TRIZ, t!r (Theory of !resonances), and Agile analysis are used to stimulate and facilitate this process.

However, any epiphany generated in the DM's brain falls into one of three categories:

• True;

• False;

• Requiring refinement.

Under conditions of high uncertainty, the only criterion of truth is a physical experiment.

[Obtaining Insight] ──► [Agile Experiment / Sprint] ──► [Result Evaluation]
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                       ┌──────────────────────────────────────┼──────────────────────────────────────┐
                       ▼                                                                                                           ▼                                                                                                          ▼
             [True: Implementation]                                                                             [False: Discard]                                                                    [Refinement: New Iteration]

Agile Validation Algorithm:

1. Hypothesis Formation: The epiphany is translated into a testable assumption.

2. Rapid Experiment: Conducting a local physical test (preferably within short Agile sprints).

3. Recording the Outcome: Based on the test results, the insight is either immediately implemented, sent for refinement, or permanently discarded.

Repeating this cycle over a set number of iterations is guaranteed to lead the system out of the deadlock: the goal is either achieved or recognized as physically impossible and abandoned with minimal loss of resources.

Epiphany Priority and the Phenomenon of aLLM Intensification

In the DM's cognitive system, internal insight takes unconditional priority over purely technical methodologies, since the biophysical mechanism of its generation more frequently produces relevant and accurate solutions.

Load Effect of TRIZ and aLLM Integration:

With the active use of TRIZ or generative AI systems (aLLM), the generation of insights and counterintuitive ideas becomes almost automated. However, simultaneously, the experimental load increases exponentially.

Even if some options can be screened out beforehand using digital modeling, conducting physical tests still requires a budget of time and resources.

Managing a !cascade of insights in the Theory of !resonances boils down to strictly limiting the resources allocated for testing each individual hypothesis, while maintaining a high speed of iterations.