Invariants
The rules governing execution in ULM-PD Engine.
Invariants are immutable laws
They don’t persuade, They don’t motivate, They don’t care who believes them They simply define what can and cannot occur, and what happens when thresholds are crossed. Systems that align behaviour to invariants can degrade gracefully. Systems that align behaviour to narrative tend to overshoot, then collapse.and they come in three types
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Universal invariants
Examples include the speed of light, spacetime, gravity, and the laws of physics. -
Domain-agnostic invariants
These apply across many domains, for example:-
systemic_autophagy
This invariant applies when a system’s extractive or optimisation efficiency is sufficient enough to eliminate the agent population or substrate it depends on for continued existence unless the current behaviour changes. System viability is proportional to agent presence; when agents are driven to extinction, the system’s own viability collapses. This invariant captures terminal self-destruction caused by unbounded efficiency, not external shock or scarcity. -
constraint_governed_state_transition
If a system’s state variables change, the subsequent state is determined by prior state and governing constraints. Environmental or energetic changes propagate as lawful state transitions across all substrates, producing predictable changes in system properties once thresholds are crossed. -
transdomain_debt_pathogen
Applies when symbolic or nominal constraints (e.g. debt, performance targets, abstract obligations) induce measurable degradation in biological, psychological, or ecological substrates. -
harm_benefit_polarity
Applies when evaluating the effect of any interaction, condition, policy, or intervention on a system under load. An effect is classified by its polarity: beneficial when it increases corrective, repair, or adaptive capacity relative to imposed load; harmful when it decreases such capacity; neutral when it produces no persistent change. System health is the accumulated result of these polarity effects over time. Apparent stability produced by continual harm–benefit cancellation without surplus corrective capacity constitutes pathological equilibrium rather than viability.
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systemic_autophagy
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Domain-specific invariants
These apply only within a single domain. They have polarity and thresholds, but do not generalise across substrates. Their role is to define when execution is meaningful within that domain only.-
Aerobic–anaerobic threshold breach (physiology):
When oxygen demand exceeds delivery in muscle tissue, metabolism shifts to anaerobic pathways, increasing fatigue and reducing sustainable output. -
Synaptic pruning window closure (neurodevelopment):
Neural structures must be reinforced or pruned within specific developmental windows. Missed windows produce irreversible functional deficits. -
Wing loading exceedance (aerodynamics):
If mass per wing area exceeds a critical threshold, lift cannot counteract gravity at feasible speeds, resulting in stall or inability to take off. -
Renal filtration saturation (biology):
When toxin load exceeds kidney filtration capacity, waste accumulates systemically, producing organ failure. -
Cache line thrashing (computer architecture):
When memory access patterns exceed cache coherence capacity, performance collapses non-linearly due to repeated eviction and reload. -
Photosynthetic light saturation (plant biology):
Beyond a certain photon flux, photosynthetic efficiency declines due to photoinhibition and cellular damage.
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Aerobic–anaerobic threshold breach (physiology):
Invariant-first rule
If a mathematical or physical state is valid, it must be representable. Failure to represent a valid state is a system error, not a refusal condition.
Formal systems are not natural laws
Formal systems are built from man-made rules used to describe, organise, and coordinate behaviour. They include systems such as mathematics, logic, programming languages, accounting systems, legal frameworks, money, gender roles, and status.
These systems rely on defined symbols and active execution. Without people or machines enforcing their rules, they do nothing.
Invariants are different. They exist regardless of language, agreement, or belief. They cannot be negotiated, suspended, or redefined.
Formal systems can model aspects of reality, but they are not reality itself. When a formal rule conflicts with a natural constraint, the natural constraint always wins.
Many large-scale human systems operate as if their rules were natural laws. They are not. They persist only because behaviour is coordinated around them.
Invariants exist to prevent this confusion. They enforce the difference between what can be symbolically declared and what can actually occur.
Boundaries are data
Terminal states such as identity elements, limits, fixed points, and finite exhaustion are returned explicitly as data.
Refusal semantics
Refusal occurs only when an invariant is undefined or violated. No other meaning is attached to refusal.
Examples of invariants
Division by zero (impossibility, not ambiguity)
Division requires a non-zero divisor.
The statement “division by zero is undefined” describes a limitation of notation, not the truth of the invariant.
The invariant truth is simpler: it is impossible to divide something by nothing.
There exists no mathematical state satisfying
a = b × 0
for any non-zero a.
Because no valid state exists, execution halts.
This is not a boundary condition and not a missing definition. It is the absence of any lawful result.
Examples of domain-specific invariants:
- Mathematics: You cannot divide something by nothing. There is no number that satisfies the operation, so no result exists.
- Mathematics: You cannot multiply by nothing. Computer systems instead use zero, which is not nothing but a symbolic value. Multiplication by zero is a formal rule, not a real interaction with nothing.
- Linear algebra: You cannot invert a matrix that has no inverse. If information is lost, it cannot be recovered by inversion.
- Calculus: A derivative does not exist at a point where a function jumps or breaks. There is no single rate of change at that point.
- Probability: A probability cannot be negative and cannot be greater than one. Values outside this range are not uncertain — they are invalid.
- Geometry: You cannot form a triangle if all three points lie on the same line. The shape cannot exist.
In all cases, failure is not caused by missing information or ambiguity. It occurs because no lawful result exists under the rules of that domain.
Exactness and approximation
Exact representations are used by default. Approximation is explicit, opt-in, and visible in the execution surface.
Tool pluralism
Internal execution may involve multiple computational substrates. Invariant compliance is the only exposed guarantee.
Determinism
Given identical inputs and declared invariants, ULM-PD Engine produces identical output.
Scope and responsibility
Only explicitly declared capabilities are supported. ULM-PD Engine enforces internal correctness but does not replace professional judgement.