Introduce synergy estimation

[etaroza]

In addition, we could measure the "synergy" between any two concepts, suggesting how much they complement each other
e,g,, recently Elon Musk announced that "Research" is just another way of saying "Engineering" at X, but if we could show that these two concepts complement each other to say "70%", i.e. there is 70% chance that one without another will drop to its exaggeration, then this may have sparked some better decisions?

(Synergy in my mind is closer to the "strength" of control statements:
"A+ without T+ yield A-")

Synergy Strength estimation:
/2-eye-opener/New Prompts 2025-08/
C_1 Complementarity Index.md - estimates Complementarity between T and A (K)
C_2 Control Statement Strength.md - estimates Strengths of control statements (C1 and C2)
These two could be used either as independent parameters or joint "Intrinsic Synergy" (IS):
IS = SQRT (K x SQRT (C1 x C2)) - we would need to see in practice
We could also invent "Feasible Synergy" (FS) = IS x (F1 x F2)^0.4, but this is more questionable, as e.g. Man and Woman are highly synergetic, but Feasibilities of turning negative man into positive woman and negative woman into positive man could be quite low
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We also have:
Polarity Index (PI) - how opposed or aligned T and A are
(I think that PI will correlate with K, but GPT suggests using it as follows:
If PI < 0.30 and K ≥ 0.60 → likely false complementarity (near-duplicates; consider merging).
If PI > 0.85 and K ≤ 0.40 → likely destructive opposition (conflict without synergy).
Keep PI out of the math; keep it in your reviewer guidance.)
Positive Resonance ( UR scale) - how clear, resonant, and unifying S+ statement is
(GPT suggests using it for estimating "Adoption Energy")
Negative resonance / Parasitic Drift Risk (PDR) - how likely S- is to enable exploitation while appearing legitimate

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I have added one more prompt:
C_3 Decision Assistant.md
It analyses all other parameters and outputs recommendations
Iput:
Complementarity (K): [0–1] – How much they need each other
C₁ (T isolation risk): [0–1] – Does T+ without A+ degrade?
C₂ (A isolation risk): [0–1] – Does A+ without T+ degrade?
(optional) Polarity Index (PI): [0–1] – How opposed they are
(optional) Context: [Relevant domain or situation — interpretive background only]
Output example:
T = Research, A = Engineering
K = 0.58, C₁ = 0.72, C₂ = 0.65, PI = 0.38
Relational Type: Dependent partnership (moderate complementarity, low opposition)
Recommended Structure: Link asymmetrically — keep distinct but integrated under Engineering leadership.
Governance Guidance: Engineering leads delivery; Research maintains knowledge-creation loops to prevent narrow utility.
Asymmetry: Research depends slightly more on Engineering (C₁ > C₂).
Quantitative Insight: Metrics show partial interdependence, not redundancy. A full merger would erode innovation feedback; asymmetric integration preserves synergy.