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Relational Resolution Theory (RRT) & Relational Substrate Theory (RST)
Axioms of the theories:
Format: The independent, dynamic physical substrate. Information: An arbitrary point of observation defined by the observer. Translation: The irreversible mechanism of change. Every change costs energy. This makes identity a dissipative process, meaning: maintaining identity is an active process. Proper Time: Local FPS (the refresh interval of any system's relations). Time is not a background. Relational distance: the number of translation steps for one system to relate to another.
Extending Landauer's principle principle to this relational ontology means, that any information change (creating, erasing) a bit, comes with a cost. This cost can be quantified by adding a minium required power utilising a relational extension of Landauer's limit:
Phi_min = (k_B * T * ln 2) * Sum [ (d_i * sigma_i) / tau_i ]
Where sigma is the informational resolution, d is the relational distance, and tau is the update interval.
To generalise this thermodynamic ROI approach, we can define the Signal-to-Noise ratio as eta = Omega / N, where Omega is the functional workload (Signal) and N is the thermal noise floor. Because any physical substrate has a finite total resource budget (W), Signal and Noise must compete via a Resource Triangle:
W^n = Omega^n + N^n
From this, the thermodynamic efficiency (or Fidelity (mu)) of maintaining a function against the noise emerges naturally:
mu = Omega / W = eta / (1 + eta^n)^(1/n)
This leads to the generalised equation for any persisting structural information (I):
I = Omega * mu
This equation builds the basis of reality.
Reality (as perceivable by us) can be modelled with this equation as a resource-constrained signal maintenance on an active substrate (the format).
This graph shows how this fidelity curve (mu) governs phase transitions across statistical mechanics (free energy), complexity theory (P vs NP), and network connectivity.
Inherent constraints of the format
Irreversibility: Every translation is a one-way operation.
Energy Tax: Every event requires energy. Nothing changes for free.
Causality: Information cannot skip steps in the format.
Entropy: Every translation produces entropy. Dissipated energy cannot be recovered.
Locality: Proper time and temperature are local. No two systems share an identical bill.
Relational Friction: Connecting to distant information increases cost with the number of translation steps.
Inherent Principles of Continued Existence
Everything Changes. To exist is to continuously maintain yourself. Even "staying the same" is work.
Change is Gradual. Every change builds on the last, one step at a time.
Everything Builds on What Came Before. Identity is continuity, not sameness.
Reality Has a Reach. You can only work with what you can perceive and process.
Every Path is Unique. No moment is repeated. Difference is inevitable.
Existing Has a Cost. The more you maintain, the higher the price. Letting go of detail is how things endure.
Full repo incl. python calculators, but excluding SPARC and other telescope raw data.
Relational Resolution Theory (RRT) & Relational Substrate Theory (RST)
Axioms of the theories:
Format: The independent, dynamic physical substrate.
Information: An arbitrary point of observation defined by the observer.
Translation: The irreversible mechanism of change. Every change costs energy. This makes identity a dissipative process, meaning: maintaining identity is an active process.
Proper Time: Local FPS (the refresh interval of any system's relations). Time is not a background.
Relational distance: the number of translation steps for one system to relate to another.
Extending Landauer's principle principle to this relational ontology means, that any information change (creating, erasing) a bit, comes with a cost. This cost can be quantified by adding a minium required power utilising a relational extension of Landauer's limit:
Phi_min = (k_B * T * ln 2) * Sum [ (d_i * sigma_i) / tau_i ]
Where sigma is the informational resolution, d is the relational distance, and tau is the update interval.
To generalise this thermodynamic ROI approach, we can define the Signal-to-Noise ratio as eta = Omega / N, where Omega is the functional workload (Signal) and N is the thermal noise floor. Because any physical substrate has a finite total resource budget (W), Signal and Noise must compete via a Resource Triangle:
W^n = Omega^n + N^n
From this, the thermodynamic efficiency (or Fidelity (mu)) of maintaining a function against the noise emerges naturally:
mu = Omega / W = eta / (1 + eta^n)^(1/n)
This leads to the generalised equation for any persisting structural information (I):
I = Omega * mu
This equation builds the basis of reality.
Reality (as perceivable by us) can be modelled with this equation as a resource-constrained signal maintenance on an active substrate (the format).
This graph shows how this fidelity curve (mu) governs phase transitions across statistical mechanics (free energy), complexity theory (P vs NP), and network connectivity.
Inherent constraints of the format
Inherent Principles of Continued Existence
Full repo incl. python calculators, but excluding SPARC and other telescope raw data.