Alphabeta Math
ExampleConstruction: AI-generatedVerification: AI-generatedPipeline-generatedjudge pass (gpt-6-luna)
How statement and proof provenance work

The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.

  • Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
  • AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
  • AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.

These labels describe origin, not correctness: citations and verification chips remain separate evidence.

Equal mean trajectories do not determine a master-equation state

Example

On states {0,1,2}, compare QA=0 with

QB=(0100−20010),

both with initial vector e1=(0,1,0)T. For QA the law stays e1. For QB it is

pB(t)=(1−e−2t2,e−2t,1−e−2t2)T.

Both mean trajectories equal 1, so both obey the same deterministic mean equation m′(t)=0, m(0)=1. For every t>0 the probability vectors differ. Thus an equation for the mean alone does not solve or determine the full master-equation state.

Facts & Assumptions

Given: The finite state set {0,1,2}, initial vector e1, and generators QA,QB displayed in the Example.

[F1]

Finite column generator: a finite column generator has nonnegative off-diagonal entries and every source column sums to zero.

[F2]

Finite master equation and uniformization formula: the full probability vector is the unique solution of p′=Qp from its initial vector.

Verification

technique · solve both finite master equations and compare their first moments
1.1givenF1F2algebra

QA=0 is a finite column generator, and the constant vector pA(t)=e1 solves pA′=QApA with the stated initial value. By [F2] it is the unique law, and its mean is 0⋅0+1⋅1+2⋅0=1.

1.2givenF1F2algebra

The off-diagonal entries of QB are nonnegative and its columns sum to zero, so [F1] applies. Writing pB=(p0,p1,p2)T, its master equation is p0′=p1, p1′=−2p1, p2′=p1 with initial values (0,1,0). Solving gives p1=e−2t and p0=p2=(1−e−2t)/2; direct differentiation verifies these formulas and their initial data, and [F2] makes this the unique law.

2.1step 1.1step 1.2algebra∎

The mean under pB(t) is 0⋅p0+1⋅p1+2⋅p2=e−2t+1−e−2t=1, the same as the mean under pA(t), while pB has positive mass at 0 for t>0 and pA has none. Thus the same mean trajectory corresponds to two different full distributions.

Depends on

Used by

Nothing in the library uses this result yet.

Dependency tree · two levels

10 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.

Sources