Emergent Physics from Probability-Field Dynamics: Derivation of the Born Rule, General Relativity, and a Programmatic Path to the Standard Model from Constraint-Based Observation
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Description
We present a unified framework in which the core structures of modern physics emerge from
a single underlying mechanism: irreversible constraint formation (“observation”) acting on an
initially unstructured space of possibility. The theory is specified by a master pruning operator
that suppresses histories accumulating high constraint density and by a local constraint field
whose growth is sourced by field intensity and smoothed by diffusion. Within this setting we
show: (1) the Born rule emerges dynamically as the normalized long-time survival measure
of branch components under coherence-dependent pruning, rather than as an independent
axiom; (2) gravity arises as an effective description of gradients in accumulated constraint
density, reproducing the Newtonian limit and admitting a generally covariant formulation closely
paralleling Einstein’s field equations; and (3) the Standard Model is naturally interpreted as
a low-energy universality class reached after successive pruning and ordering transitions, with
gauge symmetry as emergent redundancy of persistent constraint-defect sectors, chirality and
anomaly freedom as pruning-consistency conditions, and electroweak symmetry breaking as a
constraint-induced ordering phenomenon whose effective order parameter reproduces Higgs-sector
phenomenology. We summarize numerical evidence from dissipative lattice simulations showing
robust self-organization into stable topological defects across multiple resolutions and outline
discriminating predictions and falsifiers. The framework requires no substrate beyond constrained
possibility itself; quantum probabilities, spacetime geometry, and particle interactions appear as
regime-dependent summaries of the same constraint-driven dynamics
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Derivation_of_the_Born_Rule__General_Relativity__and_a_Programmatic_Path_to_the_Standard_Model_from_Constraint_Based_Observation.pdf
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