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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.

Measure Preserving Transformations and Poincare Recurrence

1 · Prerequisites

2 · Summary

Finite measure forces almost every visit to a measurable set to be followed by infinitely many returns. The proof starts with disjoint inverse-image towers, so it applies even when the transformation is not invertible. On a second-countable space this gives neighborhood recurrence outside one explicitly defined null set.

The first-return map lives on the measurable core of points returning infinitely often. Its restricted measure is invariant; under ergodicity, Kac's formula gives integral return time one on a probability space, or conditional mean return time 1/μ(E). The excursion formula extends this calculation to integrable observables.

Circle rotations distinguish ergodicity from mixing. Irrational rotations are ergodic but fail even weak mixing, while integer-base maps are strongly mixing. A direct cylinder construction gives the fair-coin shift without using Tychonoff's theorem. Compact metric dynamics culminate in Krylov–Bogolyubov: explicit orbit averages have a subsequential invariant probability limit. The local positive-functional representation lemma supplies the measure needed by that argument.

Each result states its choice assumptions. The tower and orbit arguments make explicit finite or least-index constructions; the Lebesgue, completion, fair-coin measure and compact-probability results identify their uses of countable choice. The counterexamples keep strict invariance, invariance modulo null sets, topological density and measure ergodicity separate.

3 · Logical flowchart

4 · Definitions, theorems and proofs

LemmaStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

No-return sets have disjoint null preimage towers

Statement

Let (X,A,μ,T) be a measure-preserving system with μ(X)<. For EA put W=En1TnE. Then the measurable sets TnW, n0, are pairwise disjoint and all have measure zero.

Facts & Assumptions

[F1]

Nonnegative iterates preserve the original measure; only that choice-free clause is used. Compositions, iterates and completions preserve invariance.

[F2]

Countable additivity gives finite additivity by padding with empty sets. Measures on sigma-algebras.

[F3]

The measure of a measurable subset is bounded by that of its ambient set. Measures are monotone.

Proof

Given: Let (X,A,μ,T) be a measure-preserving system with μ(X)<. For EA put W=En1TnE. Then the measurable sets TnW, n0, are pairwise disjoint and all have measure zero.

1.1

Each Tn is measurable and preserves μ, so W and every TnW are measurable and μ(TnW)=μ(W). Here T0 is the identity.

F1given
2.1

For 0i<j, membership of x in both TiW and TjW would give TixWE and Tji(Tix)WE. This contradicts the absence of every positive return from W. Thus the tower sets are pairwise disjoint.

step 1.1given
3.1

For every positive integer N, finite additivity and monotonicity give Nμ(W)=μ(n=0N1TnW)μ(X). Since the right side is finite, a positive μ(W) would violate this bound for an integer N>μ(X)/μ(W). Hence μ(W)=0, and step 1.1 makes every tower level null. This also covers E= and μ(X)=0.

step 1.1step 2.1F2F3algebra
TheoremStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Poincare recurrence for finite measure-preserving systems

Statement

In a measure-preserving system (X,A,μ,T) with μ(X)<, for every EA almost every xE has TnxE for infinitely many positive integers n.

Facts & Assumptions

[F1]

The set with no positive return and every one of its inverse-image levels are measurable and null. No-return sets have disjoint null preimage towers.

[F2]

A countable union of measurable null sets is null. Finite and countable subadditivity of measures.

Proof

Given: In a measure-preserving system (X,A,μ,T) with μ(X)<, for every EA almost every xE has TnxE for infinitely many positive integers n.

1.1

Put W=En1TnE and N=m0TmW. Every set in this union is measurable and null, whence N is measurable and μ(N)=0.

F1F2
2.1

If xE has only finitely many positive return times, the finite nonempty set {m0:TmxE} has a largest member q (it contains zero). There is no positive return to E from Tqx, so TqxW and xN. Thus every xEN returns infinitely often. Conversely a point of ETqW has no visit after q, so the exceptional set is exactly EN and is measurable. The argument includes the case of no positive return by taking q=0.

step 1.1given
CorollaryStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Topological recurrence on second-countable spaces

Statement

Let X have a countable open basis contained in A, and let T preserve a finite measure μ on (X,A). Outside one measurable null set, every neighborhood of x is revisited infinitely often by its positive orbit. If the topology is induced by a metric d, there are strictly increasing positive integers nj with Tnjxx.

Facts & Assumptions

[F1]

Finite-measure recurrence applies to each measurable basis member. Poincare recurrence for finite measure-preserving systems.

[F2]

A basis refines each open neighborhood at its point. Second countability: an at most countable basis for the topology.

[F3]

The exceptional union over the countable basis is null. Finite and countable subadditivity of measures.

[F4]

Each nonempty set of eligible positive return times has a least member. The well-ordering principle.

Proof

Given: Let X have a countable open basis contained in A, and let T preserve a finite measure μ on (X,A). Outside one measurable null set, every neighborhood of x is revisited infinitely often by its positive orbit. If the topology is induced by a metric d, there are strictly increasing positive integers nj with Tnjxx.

1.1

For each basis member B define its exceptional set explicitly as NB=Bq0Tq(Bn1TnB). The recurrence proof shows that NB is measurable and null. Thus N=BBNB is measurable and null. This is a prescribed family, not a choice of null covers. A finite basis is handled by a finite union, and the empty space has no points to check.

F1F3
2.1

If xN and U is a neighborhood of x, choose an open set V with xVU and a basis member B with xBV. Since xNB, infinitely many positive iterates enter B, and hence U.

step 1.1F2
3.1

In the metric case set n0=0 and let nj be the least integer exceeding nj1 for which d(Tnjx,x)<1/j, for j1. Infinitely many visits to the ball make this set nonempty. Least-element recursion supplies the sequence without countable choice; njj and the displayed bound proves convergence.

step 2.1F4algebra
DefinitionDefinition: AI-adaptedProof: Not applicablejudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

First-return times and induced transformations

Definition

Let (X,A,μ,T) preserve finite measure and let EA satisfy μ(E)>0. For xE define the first-return time

rE(x)=min{n1:TnxE},min=.

A nonempty set of return times has a least element by The well-ordering principle. Define the infinitely returning core

E=EN1nNTnE.

This is the set-limsup convention of Limit superior and limit inferior of a sequence of sets. It is measurable by Sigma-algebras are closed under countable intersections, differences, symmetric differences, and set limits, and Poincare recurrence for finite measure-preserving systems gives μ(EE)=0. Define the induced transformation and normalized measure by

TE:EE,TE(x)=TrE(x)x,μE(B)=μ(B)μ(E),BAE.

Here the trace is as in The trace of a sigma-algebra on a subset. Since E is measurable, the trace equals {BA:BE}: each intersection with E is measurable and each such B equals BE. Its closure under relative complement and countable union follows directly from these same operations in A; no sequence of ambient representatives needs to be chosen. This supplies the measurable-subset instance of The trace of a sigma-algebra is a sigma-algebra on the traced subset locally.

For xE, rE(x) is finite, and the infinitely many visits after rE(x) are exactly positive visits of TE(x), so TE(x)E. Restricting countable additivity of μ and dividing by the positive finite number μ(E) makes μE a measure; its total mass is μ(E)/μ(E)=1. The value is allowed for rE on EE, but is never used as an iterate in TE. No choice axiom enters this construction.

PropositionStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

First-return time and induced map are measurable

Statement

With E,rE,E,TE as in the first-return definition, rE:E{1,2,}{} is measurable for the full power-set sigma-algebra on this countable target, and TE is measurable for AE.

Facts & Assumptions

[F1]

The core is measurable and T_E is an everywhere defined self-map of it. First-return times and induced transformations.

[F2]

Every finite iterate is measurable on the original sigma-algebra. Compositions, iterates and completions preserve invariance.

[F3]

Trace sets are intersections with the domain. The trace of a sigma-algebra on a subset.

Proof

Given: With E,rE,E,TE as in the first-return definition, rE:E{1,2,}{} is measurable for the full power-set sigma-algebra on this countable target, and TE is measurable for AE.

1.1

For n1, the fiber Hn={xE:rE(x)=n}=ETnE1j<nTj(XE) is measurable. At n=1 the empty intersection is X. The fiber at infinity is En1Hn. Every inverse image of a subset of the countable target is a countable union of these fibers, proving measurability of rE.

F1F2
2.1

For BAE the measurability of E implies BA. The identity TE1B=n1(EHnTnB) expresses its inverse image as a measurable subset of E, hence a trace set. Empty B gives an empty union of pieces; full B gives all of the core.

step 1.1F1F2F3
TheoremStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Induced transformations preserve restricted finite measure

Statement

For a finite measure-preserving system and a measurable E with μ(E)>0, its induced transformation TE on E preserves both μE and μE. Neither invertibility nor ergodicity is required.

Facts & Assumptions

[F1]

Return fibers and induced inverse images are measurable. First-return time and induced map are measurable.

[F2]

The recurrent core is conull in E, and T_E is its self-map. First-return times and induced transformations.

[F3]

Pullback by T preserves the original measure. Compositions, iterates and completions preserve invariance.

[F4]

Additivity splits measurable sets into disjoint pieces. Measures on sigma-algebras.

[F5]

Increasing unions of partial first-return sets have the supremum of their measures. Continuity from below for measures.

Proof

Given: For a finite measure-preserving system and a measurable E with μ(E)>0, its induced transformation TE on E preserves both μE and μE. Neither invertibility nor ergodicity is required.

1.1

Fix a trace set BE; it is ambient measurable. Write Hn=ETnBj=1n1TjEc and RN=TNBj=0N1TjEc. Pulling B back once and splitting at E gives μ(B)=μ(H1)+μ(R1). Pulling RN back and splitting at E gives μ(RN)=μ(HN+1)+μ(RN+1). Thus for each N1, μ(B)=n=1Nμ(Hn)+μ(RN).

F1F2F3F4
2.1

The H_n are disjoint first-return pieces. Each differs from HnE by a subset of the measurable null set EE; these differences are themselves measurable. Consequently the finite-sum identity implies μ(n=1N(HnE))μ(B). Passing to the increasing union gives μ(TE1B)μ(B).

step 1.1F1F2F4F5
3.1

Apply the same inequality to C=EB. Since TE maps its entire domain to itself, TE1C=ETE1B. All measures here are finite, so μ(E)μ(TE1B)μ(E)μ(B) gives the reverse inequality. Equality follows for every B; division by 0<μ(E)< proves invariance of μE.

step 2.1F2F4algebra
PropositionStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Inducing an ergodic system gives an ergodic system

Statement

If T is an ergodic probability-preserving transformation and μ(E)>0, then TE on E is ergodic for μE.

Facts & Assumptions

[F1]

The induced transformation preserves its probability measure. Induced transformations preserve restricted finite measure.

[F2]

Positive sets in an ergodic probability system have conull forward entrance sets. Positive sets sweep out ergodic probability systems.

[F3]

An everywhere invariant finite real measurable function is a.e. constant, and this property characterizes ergodicity. Equivalent invariant-set and invariant-function criteria for ergodicity.

Proof

Given: If T is an ergodic probability-preserving transformation and μ(E)>0, then TE on E is ergodic for μE.

1.1

Let U=n0TnE. It is measurable and conull by positive-set sweep-out. It is strictly invariant: if Tx eventually enters the core then x does; if x eventually enters at a positive time then Tx does, while if x is already in the core, its next positive return belongs to the core. Thus T1U=U. On U let q(x)=min{n0:TnxE}. The fibers {q=n}=TnEj<nTjE are measurable.

F1F2
2.1

Take any finite real measurable f on the core with fTE=f everywhere. Define F(x)=f(Tq(x)x) for xU and F(x)=0 otherwise. The measurable q-fibers make F measurable. If xUE, then q(Tx)=q(x)1, hence F(Tx)=F(x). If xE, then q(Tx)=rE(x)1: before the first return there is no E visit, and the first return is in the core. Therefore F(Tx)=f(TEx)=f(x)=F(x). On XU, both x and Tx are outside U and F is zero.

step 1.1F1
3.1

Ergodicity of T and the everywhere invariant-function criterion give a constant c with F=c almost everywhere. Since F=f on the core, f=c for μE-almost every point there. The same criterion applied to the probability-preserving T_E now proves its ergodicity. Working with everywhere invariant functions avoids choosing representatives for almost-everywhere invariance.

step 2.1F1F3
TheoremStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Kac return-time formula without invertibility

Statement

If T preserves an ergodic probability measure and E is measurable with μ(E)>0, then ErEdμ=1. Equivalently, ErEdμE=1/μ(E). The formula does not require invertibility.

Facts & Assumptions

[F1]

Return-time tails are measurable. First-return time and induced map are measurable.

[F2]

Avoiding a positive set forever is a null event. Positive sets sweep out ergodic probability systems.

[F3]

Apply integral invariance to measurable indicators. Integral invariance under measure-preserving maps.

[F4]

Increasing finite tail sums converge in integral. Monotone convergence for the integral.

[F5]

Decreasing avoidance sets have limiting measure equal to their intersection. Continuity from above when one set has finite measure.

Proof

Given: If T preserves an ergodic probability measure and E is measurable with μ(E)>0, then ErEdμ=1. Equivalently, ErEdμE=1/μ(E). The formula does not require invertibility.

1.1

Put C0=X and Cj=i=0j1TiEc for j1. Since T1Cj=i=1jTiEc, its disjoint split at E consists of E{rE>j} and Cj+1. Indicator invariance and finite additivity therefore give μ(E{rE>j})=μ(Cj)μ(Cj+1) for j0. In particular at j=0 this is μ(E)=1μ(Ec).

F1F3
2.1

For N1, the simple function j=0N11E{rE>j} equals min(rE,N) on E and zero elsewhere. Its integral telescopes to 1μ(CN). The sets C_N decrease to points avoiding E at all nonnegative times, a null set by sweep-out. Since μ(C0)=1, continuity from above yields μ(CN)0.

step 1.1F2F5
3.1

The tail sums increase pointwise to rE1E, including infinite return times. Monotone convergence gives ErEdμ=1. The complement of the infinitely returning core in E is measurable null, so its nonnegative integral is zero, even where rE=. Restriction and normalization therefore give ErEdμE=1/μ(E); multiplying by the positive denominator reverses the equivalence.

step 2.1F1F4
TheoremStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Kac integral formula for excursions

Statement

In an ergodic probability-preserving system, let μ(E)>0. For nonnegative measurable f:X[0,], define SEf(x)=j=0rE(x)1f(Tjx) on E, using the infinite nonnegative sum when rE(x)=. Then ESEfdμ=Xfdμ. For integrable real or complex f the excursion is absolutely summable a.e. on E; setting it to zero on its measurable null failure set gives the same identity.

Facts & Assumptions

[F1]

In an ergodic probability system, almost every point visits every fixed positive-measure set at some positive time. Positive sets sweep out ergodic probability systems.

[F2]

Pullback preserves nonnegative and integrable integrals. Integral invariance under measure-preserving maps.

[F3]

Return-time tails and finite iterates are measurable. First-return time and induced map are measurable.

[F4]

Nonnegative truncations and excursion partial sums pass through integrals. Monotone convergence for the integral.

[F5]

After absolute integrability is established, real/complex component integrals combine linearly. The Lebesgue integral is linear on L1(μ).

[F6]

The measures of decreasing measurable sets of finite measure converge to the measure of their intersection. Continuity from above when one set has finite measure.

Proof

Given: In an ergodic probability-preserving system, let μ(E)>0. For nonnegative measurable f:X[0,], define SEf(x)=j=0rE(x)1f(Tjx) on E, using the infinite nonnegative sum when rE(x)=. Then ESEfdμ=Xfdμ. For integrable real or complex f the excursion is absolutely summable a.e. on E; setting it to zero on its measurable null failure set gives the same identity.

1.1

First let 0fM<. Put AN=j=1NTjEc. Splitting the integral at E and pulling the complement part back gives f=Ef+fT1A1. More generally pulling back the part of fTN1AN outside E gives the next remainder, while its part on E is EfTN1{rE>N}. Thus for N>=1 the exact finite identity is f=Ej=0N1fTj1{rE>j}+fTN1AN. All products are defined piecewise as zero off their indicated sets.

F2F3
2.1

The sets AN decrease, and their intersection is the complement of j1TjE. This intersection is null by [F1]. Since the ambient probability measure is finite, [F6] gives μ(AN)0. The remainder is bounded between zero and Mμ(AN) and hence vanishes. The excursion partial sums on E increase pointwise to S_Ef; monotone convergence proves the identity for bounded nonnegative f.

step 1.1F1F4F6
3.1

For arbitrary nonnegative f use fm=min(f,m). These increase pointwise to f, and SEfmSEf: one inequality is termwise, and the other follows by first restricting to any finite number of excursion terms and then increasing m. Monotone convergence on X and E proves the equality, including infinite values.

step 2.1F4
4.1

For integrable real or complex f, apply step 3.1 to f: ESEf=f<. Thus SEf is finite a.e. (otherwise its integral is at least K times the positive measure of its infinity set for every K). On this conull set the excursion is absolutely summable. Apply step 3.1 to the positive and negative parts of the real and imaginary components, each bounded by f. Their excursion integrals are finite, so linearity yields the asserted identity without subtracting infinities. Null-set values have no effect.

step 3.1F5
DefinitionDefinition: AI-adaptedProof: Not applicablejudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

The circle, rotations and the doubling map

Definition

The circle is T=[0,1) with distance d(x,y)=min(xy,1xy)=minkZxyk. The minimum is attained, since 1<xy<1. It is nonnegative and symmetric, and it vanishes precisely when x=y. For minimizing integers k,l, d(x,z)xz(k+l)xyk+yzl=d(x,y)+d(y,z), proving the triangle inequality.

Write {u}=uu[0,1) for fractional part. The rotation of angle αR and the doubling map are

Rα(x)={x+α},D(x)={2x}.

They satisfy d(Rαx,Rαy)=d(x,y) and d(Dx,Dy)2d(x,y) by the integer-minimum formula, so are continuous in the circle metric. This topology differs from the ordinary interval topology at zero: points tending to 1 from below tend to 0 on the circle.

Open circle balls of radius less than 1/2 are single ordinary intervals or two intervals meeting the cut at 0 and 1. By Both Q and RQ are dense in R, and every nonempty open subset of R is uncountable, Q is countably infinite and A product of two at most countable sets is at most countable, balls with rational centers and positive rational radii form a countable base: given a ball about x, take a rational center close enough to x and a smaller rational radius whose ball contains x and stays inside the original ball. Thus circle-open sets are countable unions of ordinary Borel sets. Conversely ordinary interval-open subsets of (0,1) are circle-open, and the singleton {0} is circle-closed. Every relatively open subset of [0,1) is therefore circle-Borel. The two Borel sigma-algebras agree.

For measures we assume countable choice The Axiom of Countable Choice (ACω). Let λ be the restriction of Lebesgue measurable sets, the family L(Rn), and the restricted set function λn to [0,1), either on Borel sets or on Lebesgue measurable sets. The measure and volume clauses of Assuming countable choice, L(Rn) is a sigma-algebra containing every elementary set and λn is a complete measure extending elementary volume give its total mass one: singletons have measure zero by covering them with intervals of arbitrarily small length, so changing an interval endpoint does not change length. By L(Rn) is exactly the completion of the restriction of λn to the Borel sets, the latter version is the completion of the former (intersect its Borel representatives and null covers with [0,1)). Countable choice is used for these measure constructions only; the circle metric and maps above are choice-free.

PropositionStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Circle rotations preserve Lebesgue measure

Statement

Assume countable choice. For every real α, Rα is an invertible measure-preserving transformation for both Borel Lebesgue probability on the circle and its completion.

Facts & Assumptions

[F1]

The circle has Borel probability and R_alpha is continuous; the measure construction uses countable choice. The circle, rotations and the doubling map.

[F2]

Translation preserves Lebesgue measurable sets and their measures. Lebesgue outer measure, Lebesgue measurability and Lebesgue measure are unchanged by translation.

[F3]

A generating pi-system containing the finite-mass whole space tests preservation. Measure preservation can be checked on a generating pi-system.

[F4]

A Borel preserving map also preserves the completion under countable choice. Compositions, iterates and completions preserve invariance.

Proof

Given: Assume countable choice. For every real α, Rα is an invertible measure-preserving transformation for both Borel Lebesgue probability on the circle and its completion.

1.1

Let a={α}. For any Borel B[0,1), Rα1B=((B[a,1))a)((B[0,a))+(1a)). The two pieces lie respectively in [0,1a) and [1a,1) and are disjoint. Translation invariance and additivity give λ(Rα1B)=λ(B[a,1))+λ(B[0,a))=λ(B). For a=0 the second piece is empty.

F1F2
2.1

In particular this proves the inverse-image identity on the pi-system of half-open intervals and the whole circle. These generate the circle Borel sigma-algebra: ordinary open intervals are countable unions of half-open subintervals, and the Borel sigma-algebras agree by the circle definition. The whole-space measure is one, so the generating criterion applies (or directly use the identity for every Borel B in step 1.1). The completion clause extends preservation to all completed sets. The countable-choice use is exactly the earlier Lebesgue and completion construction.

step 1.1F1F3F4
3.1

Fractional-part arithmetic gives RαRαx=x=RαRαx. The same argument applies to α, so the inverse is measurable for either sigma-algebra. Thus invertibility here includes measurability of the inverse.

step 2.1F1algebra
LemmaStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Irrational circle orbits are dense

Statement

For irrational α, the subgroup {{nα}:nZ} is dense in the circle. For every ε>0 and integer M0 there is an integer n>M with d({nα},0)<ε. These assertions are choice-free.

Facts & Assumptions

[F1]

Circle distance is distance to the nearest integer. The circle, rotations and the doubling map.

[F2]

N+1 points in N intervals contain a pair in one interval. The pigeonhole principle on N.

Proof

Given: For irrational α, the subgroup {{nα}:nZ} is dense in the circle. For every ε>0 and integer M0 there is an integer n>M with d({nα},0)<ε. These assertions are choice-free.

1.1

For an integer N2, place {jα}, 0jN, in the N half-open intervals [k/N,(k+1)/N). Two indices i<j lie in one interval. Hence for q=j-i, 1qN and 0<d({qα},0)<1/N; positivity follows from irrationality. Change q to -q if needed to obtain a subgroup point β(0,1/N).

F1F2
2.1

The subgroup contains 0,β,2β,,mβ, where m=1/β; the last term is interpreted modulo one if necessary. Consecutive gaps are beta and the remaining gap to 1 is at most beta. For every x in [0,1), taking k=x/β gives 0xkβ<β. Since N can be arbitrarily large, every circle neighborhood meets the subgroup.

step 1.1F1algebra
3.1

For fixed M>=1 let δ=min1qMd({qα},0)>0. Take N with 1/N<min(δ,ε) and use the positive q from step 1.1 before changing its sign. That q exceeds M and has distance less than epsilon. For M=0 any q supplied there works after taking 1/N<ε. Only finite minima and finite pigeonhole choices occur.

step 1.1F1algebra
TheoremStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Circle rotation is ergodic for Lebesgue measure exactly at irrational angles

Statement

Assume countable choice. The circle rotation Rα is ergodic for Borel Lebesgue probability and for its completion if and only if α is irrational.

Facts & Assumptions

[F1]

All positive and negative powers of a rotation preserve either measure. Circle rotations preserve Lebesgue measure.

[F2]

Integer powers of an irrational rotation move any center arbitrarily close to another. Irrational circle orbits are dense.

[F3]

Under countable choice, a positive measurable set has density-one points. Lebesgue density theorem.

[F4]

The strict invariant-set criterion is equivalent to the mod-null criterion. Equivalent invariant-set and invariant-function criteria for ergodicity.

Proof

Given: Assume countable choice. The circle rotation Rα is ergodic for Borel Lebesgue probability and for its completion if and only if α is irrational.

1.1

If α=p/q with integers p and q>=1, the set A=k=0q1[k/q,k/q+1/(2q)) is Borel, has measure 1/2, and is permuted by R_alpha. Thus Rα1A=A and the rotation is not ergodic for either measure. This includes integral alpha with q=1.

F1F4
1.2

Now let alpha be irrational and let A be a strictly invariant measurable set. Suppose both A and its complement have positive measure. Choose density-one points a in A and c in its complement, different from the cut point zero. For sufficiently small 0<r<1/10, the circle balls I=B(a,r), J=B(c,r) have length 2r and satisfy λ(IA)<r/5 and λ(JA)<r/5. The ordinary density theorem applies because these small balls at the chosen points do not cross the cut.

F3F4
2.1

Choose an integer m with d(Rαma,c)<r/10. Strict invariance and invertibility give RαmA=A, and the rotated interval I has a portion outside A of measure less than r/5. Its intersection with J has length greater than 2rr/10=19r/10: both are radius-r arcs whose centers are less than r/10 apart. Subtracting the two exceptional portions, this intersection would contain points in both A and its complement on a set of measure at least 19r/102r/5=3r/2>0, impossible. Thus every strict invariant A has measure zero or one, proving ergodicity. Together with step 1.1 this proves both directions.

step 1.2F1F2F4algebra
DefinitionDefinition: AI-adaptedProof: Not applicablejudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Integer-base maps and b-adic circle intervals

Definition

On the circle of The circle, rotations and the doubling map, for an integer b2 put Db(x)={bx}. For every n0 its level-n b-adic intervals are

In,k=[k/bn,(k+1)/bn),0k<bn.

These intervals partition [0,1); at level zero the only interval is the whole circle. On the branch I1,j the map is Db(x)=bxj. Iterating fractional-part arithmetic gives Dbn(x)={bnx}, and on In,k this is bnxk. Each branch maps bijectively onto [0,1) with inverse y(y+k)/bn. Half-open endpoints ensure that every x belongs to exactly one branch, including x=0. Multiplication by the integer b respects congruence modulo one, and the distance formula gives d(Dbx,Dby)bd(x,y). Thus these are continuous circle maps. The previously defined doubling map is exactly D=D2. None of these algebraic or metric facts uses a measure or a choice axiom.

PropositionStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Integer-base circle maps preserve Lebesgue measure

Statement

Assume countable choice. For every integer b2, the circle map Db is continuous, surjective and non-injective, and preserves Borel Lebesgue probability and its completion.

Facts & Assumptions

[F1]

The b affine branches and Lipschitz bound are explicit. Integer-base maps and b-adic circle intervals.

[F2]

The finite measure generator test applies with the whole space included. Measure preservation can be checked on a generating pi-system.

[F3]

Countable choice gives preservation on the completion. Compositions, iterates and completions preserve invariance.

Proof

Given: Assume countable choice. For every integer b2, the circle map Db is continuous, surjective and non-injective, and preserves Borel Lebesgue probability and its completion.

1.1

For 0a<c1, Db1[a,c)=j=0b1[(a+j)/b,(c+j)/b), with disjoint pieces of length (ca)/b. Thus their total measure is c-a. The empty interval has empty inverse image. The map is Borel measurable by the Lipschitz bound from its definition.

F1F4
2.1

The half-open intervals together with the empty set form a pi-system containing [0,1) and generating the circle Borel sets, as in the circle definition. Its whole-space mass is one, so the finite generator theorem gives Borel preservation; the completion theorem gives completed preservation. Countable choice is inherited by the Lebesgue, dilation and completion suppliers and is assumed here.

step 1.1F1F2F3
3.1

For every y in [0,1), the explicit preimage y/b lies in [0,1) and maps to y, proving surjectivity. The distinct points 0 and 1/b both map to 0, proving non-injectivity. Continuity is the already established inequality d(Dbx,Dby)bd(x,y).

step 2.1F1
TheoremStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Every integer-base circle map is strongly mixing

Statement

Assume countable choice. For every integer b2 and every pair of Borel or completed Lebesgue measurable circle sets A,B, λ(ADbnB)λ(A)λ(B). Thus Db is strongly mixing for either measure.

Facts & Assumptions

[F1]

D_b preserves either probability measure. Integer-base circle maps preserve Lebesgue measure.

[F2]

Strong mixing follows from correlations on a generating pi-system containing X. Mixing is checkable on a generating pi-system.

[F3]

Every completed set differs from a Borel set within a Borel null set. The completion domain and proposed completed set function of a measure space.

Proof

Given: Assume countable choice. For every integer b2 and every pair of Borel or completed Lebesgue measurable circle sets A,B, λ(ADbnB)λ(A)λ(B). Thus Db is strongly mixing for either measure.

1.1

The b-adic intervals at all depths, together with the empty set, form a pi-system: two such intervals are nested or disjoint, and depth zero is X. They generate the circle Borel sets. Indeed every ordinary open interval in (0,1) is the union of the b-adic cells whose closures lie within it; the cell containing any specified interior point has arbitrarily small diameter. The endpoint zero is the intersection of [0,br) over r, so relative interval-open sets are generated as well. Conversely each cell is Borel. The countable family of cells permits these unions in the generated sigma-algebra.

F1
2.1

Let I=Ir,k and J=Is,l. For nr, the interval I contains exactly bnr depth-n cells. On each such cell, the inverse image under Dbn of J is a half-open interval of length b(n+s). Hence λ(IDbnJ)=bnrb(n+s)=brbs=λ(I)λ(J). The same equality holds when either set is empty. The generator theorem proves Borel strong mixing.

step 1.1F1F2algebra
3.1

For completed A,B take Borel A_0,B_0 and Borel null sets Z_A,Z_B containing their respective symmetric differences. For every n, (ADbnB)(A0DbnB0)ZADbnZB, a completed null set by preservation. Thus the correlation and both marginal measures agree with those for A_0,B_0. The Borel limit proves the completed limit. Countable choice is inherited from the measure and completion suppliers; only finitely many representatives are selected here.

step 2.1F1F3
PropositionStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Doubling preserves Lebesgue measure

Statement

Assume countable choice. The doubling map D(x)={2x} is a continuous, surjective, non-injective transformation preserving Borel Lebesgue probability on the circle and its completion.

Facts & Assumptions

[F1]

The preservation theorem applies to every integer b>=2. Integer-base circle maps preserve Lebesgue measure.

[F2]

The stable doubling map has the fractional-part formula. The circle, rotations and the doubling map.

Proof

Given: Assume countable choice. The doubling map D(x)={2x} is a continuous, surjective, non-injective transformation preserving Borel Lebesgue probability on the circle and its completion.

1.1

By the definitions, D=D2. Since 2 is an allowed integer base, the base-map theorem proves preservation on both sigma-algebras and continuity. Its countable-choice measure and completion hypothesis is the assumption here.

F1F2
2.1

Explicitly D1[a,c)=[a/2,c/2)[(a+1)/2,(c+1)/2) for 0a<c1; the two pieces have total length c-a. For each y, y/2 is a preimage, while D(0)=D(1/2)=0 exhibits failure of injectivity. These branch identities also show why preservation concerns inverse images.

step 1.1F2
TheoremStatement: AI-adaptedProof: AI-adaptedaudited 2026-09-13Open item page →

Doubling is ergodic for Lebesgue measure

Statement

Assume countable choice. Doubling is ergodic for Borel Lebesgue probability and its completion.

Facts & Assumptions

[F1]

Doubling preserves each of the two measures. Doubling preserves Lebesgue measure.

[F2]

It suffices that strictly invariant measurable sets have measure zero or one. Equivalent invariant-set and invariant-function criteria for ergodicity.

[F3]

A positive Lebesgue measurable set has an interior density-one point. Lebesgue density theorem.

[F4]

A nonzero affine branch scales measure by its slope, with translation handled by its separate supplier. For a nonzero real c, dilation by c multiplies Lebesgue outer measure by cn, and reflection in the origin preserves it.

Proof

Given: Assume countable choice. Doubling is ergodic for Borel Lebesgue probability and its completion.

1.1

Let A be strictly invariant: D1A=A. Then (Dn)1A=A for every n. On In,k=[k/2n,(k+1)/2n) the map Dn is x2nxk, so AIn,k=(A+k)/2n. Translation and dilation give λ(AIn,k)=2nλ(A)=λ(In,k)λ(A).

F1F4F5
2.1

If λ(A)>0, choose a density-one point xA(0,1). Let I_n be its unique half-open depth-n cell and put h_n=2^{-n}. This cell lies in the centered interval (xhn,x+hn) apart from possibly an endpoint of measure zero. Thus λ(InA)/hn2λ((xhn,x+hn)A)/(2hn)0. But step 1.1 makes the left side identically 1λ(A), so λ(A)=1. Every strict invariant set is therefore null or conull, and the invariant-set criterion proves ergodicity. Countable choice is used in the measure, scaling and density suppliers.

step 1.1F2F3F4F5
PropositionStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Doubling is strongly mixing for Lebesgue measure

Statement

Assume countable choice. Doubling is strongly mixing for Borel Lebesgue probability and for its completion: λ(ADnB)λ(A)λ(B) for every pair of measurable circle sets.

Facts & Assumptions

[F1]

The strong-mixing theorem covers b=2 and both measure domains. Every integer-base circle map is strongly mixing.

[F2]

Doubling is the base-two fractional-part map. The circle, rotations and the doubling map.

Proof

Given: Assume countable choice. Doubling is strongly mixing for Borel Lebesgue probability and for its completion: λ(ADnB)λ(A)λ(B) for every pair of measurable circle sets.

1.1

The definition gives D=D_2. Taking b=2 in the integer-base theorem, with exactly its countable-choice assumption, yields the stated correlation limit for every Borel pair and every completed pair.

F1F2
2.1

For clarity, if A and B are dyadic intervals of depths r and s, the proof gives the exact value 2nr2(n+s)=2rs=λ(A)λ(B) for every n>=r. This includes the depth-zero whole circle; an empty test set gives zero. Thus the specialization retains the explicit dyadic correlation calculation as well as the general measurable-set conclusion.

step 1.1F1
DefinitionDefinition: AI-adaptedProof: Not applicablejudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Binary-sequence cylinders and fair-coin content

Definition

Put Ω={0,1}N0, the set of functions from nonnegative integers to {0,1}. For a finite set FN0 and a function a:F{0,1} the cylinder [a]F consists of x with xj=a(j) for every j in F. The empty prescription gives all of Omega. Every cylinder is nonempty by filling unspecified coordinates with zero.

The cylinder algebra C is the set of finite unions of cylinders, including the empty union. It is an algebra in the sense of Algebras of subsets: any finite list of prescriptions can be refined to their finite coordinate union G; the 2G complete prescriptions on G are disjoint nonempty atoms partitioning Omega, and union, intersection and complement of unions of these atoms again are such unions.

Give each G-atom mass 2G. If A is a union of m distinct G-atoms, define its fair-coin content by p0(A)=m2G. This is independent of G and its representation. Enlarging G to H splits each atom into exactly 2HG atoms, leaving its mass unchanged. Two representations agree after refining to their coordinate union; because all refined atoms are nonempty, the same subset A selects precisely the same atoms in both. Common refinement also proves finite additivity on disjoint sets. In particular p0()=0, p0(Ω)=1, and p0([a]F)=2F. All constructions here involve finite coordinate sets and are choice-free.

LemmaStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Binary-sequence space is compact without Tychonoff

Statement

On Ω={0,1}N0 put d(x,x)=0 and d(x,y)=2min{j:xjyj} for xy. This is a metric whose topology is generated by cylinders; it is compact in ZF.

Facts & Assumptions

[F1]

Finite prescriptions define nonempty cylinders and each prefix has two disjoint children. Binary-sequence cylinders and fair-coin content.

[F2]

Compactness means every open cover has a finite subcover. Open cover, subcover, compact metric space, and compact subset of a metric space.

Proof

Given: On Ω={0,1}N0 put d(x,x)=0 and d(x,y)=2min{j:xjyj} for xy. This is a metric whose topology is generated by cylinders; it is compact in ZF.

1.1

Positive definiteness and symmetry follow immediately from the first disagreement. If x,y agree before index r and y,z agree before index s, then x,z agree before min(r,s). Thus d(x,z)max(d(x,y),d(y,z))d(x,y)+d(y,z), with equal pairs included. A prefix cylinder fixing indices 0 through m-1 is the open ball of radius 2(m1) about any of its points when m>=1. Arbitrary finite-coordinate cylinders are finite unions of sufficiently long prefixes. Conversely prefixes of arbitrarily small diameter fit in every ball. Hence cylinders generate exactly the metric topology and are clopen.

F1
2.1

Fix an open cover with no finite subcover. The empty prefix cylinder has no finite subcover. If a prefix cylinder has no finite subcover, at least one of its two children also lacks one; otherwise the union of the two finite subcovers would cover it. Choose the zero child if it lacks a finite subcover, and the one child otherwise. This is a uniquely specified recursion, producing a binary sequence x whose every prefix cylinder lacks a finite subcover.

step 1.1F1F2
3.1

Some member U of the cover contains x. Since U is open, it contains a prefix cylinder about x. That cylinder has the one-element subcover {U}, contradicting its construction. Therefore no open cover without a finite subcover exists, proving compactness. The recursion makes no arbitrary infinite choices; neither Tychonoff nor dependent choice is used.

step 1.1step 2.1F2
LemmaStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Fair-coin cylinder content is a premeasure

Statement

The fair-coin content p0 on the cylinder algebra C is a finite premeasure, with p0(Ω)=1. This assertion is choice-free.

Facts & Assumptions

[F1]

Common refinement proves finite additivity and total mass one. Binary-sequence cylinders and fair-coin content.

[F2]

Omega is compact and cylinders are clopen. Binary-sequence space is compact without Tychonoff.

[F3]

Only disjoint countable unions remaining in the algebra must be additive. Premeasures on algebras of sets.

Proof

Given: The fair-coin content p0 on the cylinder algebra C is a finite premeasure, with p0(Ω)=1. This assertion is choice-free.

1.1

Let A=n0An with disjoint AnC and AC. All these sets are clopen, being finite unions of clopen cylinders. The family consisting of ΩA and all A_n is an open cover of Omega. Compactness gives finitely many members covering Omega and hence finitely many A_n covering A. Using their least indices if the same member repeats gives a finite index set F with A=nFAn. This uses ambient compactness only of Omega itself, not an unstated compact-subset criterion.

F1F2
2.1

For every n outside F, disjointness gives AnAjFAj=. Thus all other terms have zero content, and finite additivity gives p0(A)=nFp0(An)=n0p0(An). If A is empty every A_n is empty and the same identity is zero equals zero. Along with p0()=0 and p0(Ω)=1, this is precisely a finite premeasure.

step 1.1F1F3
TheoremStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Fair-coin measure on binary sequences

Statement

Assume countable choice. There is a unique Borel probability p on binary sequence space such that a cylinder prescribing k distinct coordinates has mass 2k. Its completion is a complete probability measure on the completion of the Borel sigma-algebra.

Facts & Assumptions

[F1]

The cylinder-algebra content is a finite premeasure. Fair-coin cylinder content is a premeasure.

[F2]

Under countable choice the premeasure extends to its generated sigma-algebra. Assuming countable choice, a premeasure extends through its induced outer measure.

[F4]

Under countable choice the Borel probability has a complete extension on its completion domain. Assuming countable choice, every measure space has a unique complete extension to its completion.

Proof

Given: Assume countable choice. There is a unique Borel probability p on binary sequence space such that a cylinder prescribing k distinct coordinates has mass 2k. Its completion is a complete probability measure on the completion of the Borel sigma-algebra.

1.1

The prefix cylinders form a countable base: length m has exactly 2m possible prefixes, enumerated by the binary integers from 0 to 2m1, and the pairs of length and index admit a diagonal enumeration. Every cylinder is open, and every open set is the union of the subfamily of prefix cylinders contained in it. It follows that the sigma-algebra generated by the cylinder algebra is exactly the metric Borel sigma-algebra.

F1
2.1

Apply the extension theorem to the premeasure p_0. It gives a Borel measure p agreeing with every cylinder mass and with p(Ω)=p0(Ω)=1. Any other measure with these cylinder masses agrees on finite disjoint unions by finite additivity, hence on the entire algebra; finite-premeasure uniqueness makes it equal to p.

step 1.1F1F2F3
3.1

The completion theorem gives a complete probability extending p, since the whole-space mass remains one. Countable choice is used by the cited extension and completion constructions; the preceding finite-algebra and compactness arguments themselves were choice-free. This constructs this particular binary measure, not a general infinite product measure.

step 2.1F4
TheoremStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

The fair-coin one-sided shift preserves measure and is mixing

Statement

Assume countable choice. The one-sided left shift σ:ΩΩ, (σx)j=xj+1, preserves the fair-coin probability and its completion, is strongly mixing for both, and hence is ergodic.

Facts & Assumptions

[F1]

A cylinder prescribing k coordinates has mass 2^-k. Fair-coin measure on binary sequences.

[F2]

Finite-mass preservation may be checked on cylinders and the whole space. Measure preservation can be checked on a generating pi-system.

[F3]

Cylinder correlation limits imply Borel mixing. Mixing is checkable on a generating pi-system.

[F4]

Preservation extends to the completion under countable choice. Compositions, iterates and completions preserve invariance.

[F5]

Strong mixing implies ergodicity for probability systems. Mixing implies weak mixing, which implies ergodicity.

Proof

Given: Assume countable choice. The one-sided left shift σ:ΩΩ, (σx)j=xj+1, preserves the fair-coin probability and its completion, is strongly mixing for both, and hence is ergodic.

1.1

For a cylinder C prescribed on F, σ1C imposes the same values on F+1. It is a cylinder with the same number of fixed coordinates and the same mass. Inverse images of prefix cylinders are open, so sigma is continuous and hence Borel measurable. Cylinders together with the empty set form a generating pi-system containing Omega. Since its mass is one, the preservation criterion applies; the completion clause then gives completed preservation.

F1F2F4
2.1

If C and H prescribe finite coordinate sets F and G, then σnH prescribes G+n. For all sufficiently large n these are disjoint from F, so their intersection is a cylinder prescribing F+G coordinates. Its mass is 2FG=p(C)p(H). Empty sets give zero correlations. The mixing criterion now proves mixing for arbitrary Borel pairs.

step 1.1F1F3
3.1

For two completed sets replace each by a Borel core modulo a Borel null cover, as supplied by the completion construction. The symmetric difference of the intersection and its Borel version lies in the union of the first null cover and the nth pullback of the second. Preservation makes that union null. Correlations and marginal masses therefore agree exactly with their Borel versions for each n, proving completed mixing. Finally the mixing implication proves ergodicity for both probability spaces. Countable choice is inherited from the fair-coin extension and completion; coordinate calculations use only finite counting.

step 1.1step 2.1F1F4F5
LemmaStatement: AI-adaptedProof: AI-adaptedaudited 2026-09-13Open item page →

A countable dense family of continuous functions on a compact metric space

Statement

Assume the Axiom of Countable Choice. If (K,d) is a nonempty compact metric space, there is a sequence (fj)j1 in C(K,R) such that for every fC(K,R) and ε>0 some j satisfies ffj<ε. One may use all rational polynomials in finitely many distance functions to an enumerated dense subset, including the rational constant functions.

Facts & Assumptions

[F1]

Under ACω, compact metric spaces have at most countable dense subsets. A compact metric space has a countable dense subset, by countable choice.

[F2]

A nonempty at most countable set admits an enumeration, with repetitions. A nonempty set is at most countable iff it is a surjective image of N.

[F3]

The rational numbers are countably infinite. Q is countably infinite.

[F4]

Finite products of at most countable sets are at most countable, by induction on the number of factors. A product of two at most countable sets is at most countable.

[F5]

Under ACω, a countable union of at most countable sets is at most countable. Countable unions of at most countable sets, assuming ACω.

[F6]

The uniform closure of a unital real function algebra is a lattice. The countable approximant selections in its proof are justified here by ACω. The uniform closure of a unital real function algebra is closed under absolute value, maximum, and minimum.

[F7]

A unital separating real algebra interpolates prescribed values at two distinct points. A unital separating real function algebra interpolates arbitrary values at two distinct points.

Proof

Given: Assume the Axiom of Countable Choice. If (K,d) is a nonempty compact metric space, there is a sequence (fj)j1 in C(K,R) such that for every fC(K,R) and ε>0 some j satisfies ffj<ε. One may use all rational polynomials in finitely many distance functions to an enumerated dense subset, including the rational constant functions.

1.1

By [F1] fix a countable dense subset D of K. It is nonempty: otherwise no ball around a point of the nonempty space K would meet it. By [F2] enumerate it as (xj)j1. Put hj(x)=d(x,xj). The triangle inequality gives hj(x)hj(y)d(x,y), so each hj is continuous. Every continuous real function v on K is bounded: the open sets {v<m} for positive integers m cover K, and a finite subcover yields a bound. Thus all supremum norms below are finite.

F1F2F8
1.2

Let A be the real algebra of finite polynomials in the hj and the constant function 1, and let Q be its subset with rational coefficients. These are continuous functions, since finite sums and products of continuous real functions are continuous. The set Q is nonempty and at most countable: monomials are coded by finite lists of natural indices (the empty list codes 1), and a polynomial is coded by a finite list of pairs consisting of a rational coefficient and such a monomial. Induction using [F4], and then [F5] over list lengths, makes both coding sets countable; their evaluation images are countable by composing an enumeration and using [F2]. This uses [F3] for the rational entries. The algebra A separates distinct x,y: choose xj with d(x,xj)<d(x,y)/3; then hj(y)>2d(x,y)/3>hj(x).

1.1F2F3F4F5
1.3

Write H=A in the supremum metric. It is a real vector space: if u,vH, choose approximants a,bA within any prescribed positive errors and use (u+v)(a+b)ua+vb, with the analogous scalar estimate. It is closed by the definition of closure. By [F6] it is closed under finite maximum and minimum. In applying that lemma, countable choice supplies its sequences of algebra approximants and polynomial approximants; the later diagonal indices can be taken least eligible integers. No arbitrary family indexed by K is selected.

1.11.2F6
1.4

Fix fC(K,R) and η>0. For a fixed xK, use the entire set Ax={uA:u(x)=f(x)}. The open sets {u>fη}, for all uAx, cover K: at yx, [F7] supplies one interpolant taking the values f(x),f(y), while the constant f(x) handles y=x. By [F8] finitely many of these open sets cover K; choose a representing function for each of these finitely many sets and form their maximum g. Then gH, g(x)=f(x), and g>fη throughout K. Only finitely many existential witnesses were needed for this fixed x.

1.21.3F7F8
1.5

Use the entire set G={gH:g>fη on K, g(x)=f(x) for some xK}. The preceding step proves that the open sets {g<f+η}, for all gG, cover K, without selecting one g for each x. A finite subcover and finitely many representatives give g1,,gs; their minimum vH satisfies fη<v<f+η pointwise, hence vfη. Given any δ>0, take η=δ/3 and an aA with av<δ/3. Then af<δ. This proves density of A, including when K is a singleton, for which constants alone interpolate.

1.31.4F8
2.1

For a=i=1rcimiA, where the mi are monomials, let Mi=mi. If r=0, then a=0Q. Otherwise, given δ>0, use [F9] to choose rational qi with qici<δ/[r(1+Mi)]. The finite sum q=iqimi belongs to Q and satisfies aqiciqiMi<δ, even when some Mi=0. Approximate f by a within ε/2 using the preceding step, and a by q within ε/2. An enumeration of the nonempty countable Q by [F2] is the required (fj). Countable choice has been used for the dense subset, the countable-union theorem, and the sequences in [F6]; all cover selections in the local density proof were finite.

1.11.21.5F2F6F9
LemmaStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

A compact-metric probability representation using countable choice

Statement

Assume the Axiom of Countable Choice. Let K be a nonempty compact metric space and let Λ:C(K,R)R be real-linear, positive in the sense that f0 implies Λ(f)0, and normalized by Λ(1)=1. Then there is a Borel probability μ on K with Λ(f)=Kfdμ for every continuous real f. It is outer regular on Borel sets and inner regular on open sets by compact subsets. No Dependent Choice is required.

Facts & Assumptions

[F2]

A closed subset of a compact metric space is compact. A closed subset of a compact metric space is compact.

[F3]

The measurable sets of an outer measure form a sigma-algebra carrying its restriction as a complete measure. Carathéodory's theorem: measurable sets form a sigma-algebra carrying a complete measure.

[F4]

The nonnegative integral is monotone and homogeneous. Monotonicity and nonnegative homogeneity of the nonnegative integral.

[F5]

The Lebesgue integral is linear on integrable real functions. The Lebesgue integral is linear on L1(μ).

Proof

Given: Assume the Axiom of Countable Choice. Let K be a nonempty compact metric space and let Λ:C(K,R)R be real-linear, positive in the sense that f0 implies Λ(f)0, and normalized by Λ(1)=1. Then there is a Borel probability μ on K with Λ(f)=Kfdμ for every continuous real f. It is outer regular on Borel sets and inner regular on open sets by compact subsets. No Dependent Choice is required.

1.1

For an open set UK define tU(x)=infyKUd(x,y) when KU, and tK=1. The triangle inequality, followed by the infimum, shows that tU is 1-Lipschitz in the first case; it is positive at every point of U because that point has a ball contained in U, and it vanishes outside U. If a nonempty closed F lies in U, the sets {tU>1/m} for positive integers m cover F. Adjoining KF and using [F1] gives a δ>0 with tU>δ on F. Consequently h=min(1,max(0,2tU/δ1)) equals 1 on F, takes values in [0,1], and has support contained in {tUδ/2}U. Here support means the closure in K of the nonzero set, which is compact by [F2]. For F= use h=0. Any compact subset of a metric space is closed: the empty subset is closed, and for a nonempty compact subset, if x is outside it, its balls centered at y of radii d(x,y)/3 have a finite subcover; the minimum of these finitely many positive radii gives a ball at x missing the subset. Thus these cutoffs apply also to every compact F.

F1F2
1.2

Write fU for continuous f with 0f1 and support contained in U. Positivity and linearity give monotonicity of Λ by applying positivity to differences, and Λ(f)f by comparison with constants. The norm is finite: the open sets {f<m} cover K and a finite subcover bounds f. Define ρ(U)=supfUΛ(f) and μ(E)=infEU openρ(U). The zero function and the open set K make both families nonempty; 0ρ(U)1, ρ()=0, and ρ(K)=1. Monotonicity immediately implies μ(U)=ρ(U) for open U, and monotonicity and zero empty-set value for μ.

F1
2.1

For a closed nonempty Fi=1sUi with finitely many open Ui, put ti=tUi. Compactness, applied to the sets {maxiti>1/m} and KF, gives δ>0 with maxiti>δ on F. Set wi=(tiδ/2)+ and w=iwi. Define φi=wimin(2/δ,1/w) where w>0, and zero where w=0. Near a zero of w the formula is (2/δ)wi, proving continuity there. Each φi has support in {tiδ/2}Ui, is nonnegative and at most 1, and iφi=min(2w/δ,1)=1 on the open neighborhood {w>δ/2} of F. For empty F use all zero functions. This constructs a finite subordinate partition without any selection principle.

step 1.1F1
3.1

For open U=n1Un and fU, its closed support F is covered by finitely many distinct Uni by [F1] after adjoining KF. If F is empty, Λ(f)=0. Otherwise the partition in step 2.1 gives f=ifφi, with fφiUni. Hence Λ(f)iρ(Uni)nρ(Un); taking the supremum proves open-set subadditivity. Given arbitrary En and ε>0, countable choice now selects simultaneously open UnEn with ρ(Un)<μ(En)+ε2n. These are a specified countable family of nonempty sets of admissible opens. Thus μ(nEn)ρ(nUn)nμ(En)+ε. Letting ε decrease to zero proves that μ is an outer measure; if the sum on the right is infinite the inequality is immediate and no selection is needed.

step 2.1step 1.2F1
4.1

For open G,V and fVG, with support F. For any gVF, the supports are disjoint, so f+gV. Therefore ρ(V)Λ(f)+ρ(VF)Λ(f)+μ(VG). Taking the supremum over f gives ρ(V)μ(VG)+μ(VG). For arbitrary EV, monotonicity replaces V in the two terms by E; taking the infimum over open VE proves μ(E)μ(EG)+μ(EG). The opposite inequality is outer subadditivity. Thus every open G is Carathéodory measurable, and [F3] gives a Borel measure μ. It satisfies μ(K)=ρ(K)=1 and is outer regular by its defining infimum and μ(U)=ρ(U).

step 1.2step 3.1F3
5.1

For compact FK, one has μ(F)=inf{Λ(h):hC(K,R), h1F}. Indeed such h is nonnegative everywhere, and for 0<ε<1 the open set U={h>1ε} contains F. Every gU satisfies gh/(1ε), whence μ(F)ρ(U)Λ(h)/(1ε), and then μ(F)Λ(h). Conversely for each open UF, step 1.1 supplies hU with h=1 on F; hence the displayed infimum is at most ρ(U). Infimizing over U gives the reverse inequality. Empty F has both sides zero using h=0. If fU and F=suppf, then fh for every h1F, so the compact formula gives Λ(f)μ(F). Taking the supremum over f proves μ(U)supFU compactμ(F); monotonicity proves equality. This is the required inner regularity on opens.

step 1.1step 1.2step 4.1
6.1

Let 0fC(K,R) and ε>0. Choose a positive integer N with fNε, put F0=K, Fn={fnε} for 1nN, and fn=min(ε,(f(n1)ε)+). These are continuous, the Fn are compact by [F2], f=n=1Nfn, and ε1Fnfnε1Fn1. The compact formula gives the lower bound εμ(Fn)Λ(fn); comparison with every continuous majorant of 1Fn1 gives the upper bound Λ(fn)εμ(Fn1). By [F4] the same bounds hold for fndμ. All these integrals are finite since fnε and μ(K)=1. Summing and applying [F5] locates both Λ(f) and fdμ in the same interval of length ε(μ(F0)μ(FN))ε. Since ε is arbitrary they are equal. Finally f=f+f proves equality for every real continuous f, using positivity, linearity and [F5]. Countable choice was spent only on the admissible open supersets in step 3.1; the cutoffs and partitions are explicit metric formulas.

step 1.1step 1.2step 3.1step 4.1step 5.1F2F4F5
LemmaStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Probability sequences on compact metric spaces have integral-convergent subsequences

Statement

Assume countable choice. Let (μn)n1 be Borel probabilities on a nonempty compact metric space K. There are strictly increasing positive integers nr and a Borel probability μ on K such that fdμnrfdμ for every fC(K,R). The limiting probability can be taken outer regular on Borel sets and inner regular on open sets.

Facts & Assumptions

[F1]

Under countable choice, C(K,R) has an enumerated uniformly dense family. A countable dense family of continuous functions on a compact metric space.

[F2]

Every bounded real sequence has a convergent subsequence. Bolzano-Weierstrass: every bounded real sequence has a convergent subsequence.

[F3]

A positive normalized real-linear functional on C(K,R) is represented by a regular Borel probability under countable choice. A compact-metric probability representation using countable choice.

[F4]

Nonnegative integrals are monotone and homogeneous. Monotonicity and nonnegative homogeneity of the nonnegative integral.

[F5]

Integrals of integrable functions are linear. The Lebesgue integral is linear on L1(μ).

Proof

Given: Assume countable choice. Let (μn)n1 be Borel probabilities on a nonempty compact metric space K. There are strictly increasing positive integers nr and a Borel probability μ on K such that fdμnrfdμ for every fC(K,R). The limiting probability can be taken outer regular on Borel sets and inner regular on open sets.

1.1

Fix the dense family (fj) of [F1]. Continuous real functions on K are bounded, as proved there; they are Borel measurable by continuity. For every Borel probability ν, [F4] bounds the integral of f by f, so f is integrable. Positivity and [F5], applied to fg±(fg), give fdνgdνfg. In particular an,j=fjdμn is a bounded real sequence for each j.

F1F4F5
1.2

There is a deterministic convergent-subsequence rule for a bounded real sequence (an) restricted to an infinite subset S of the positive integers, with a specified bound M0. Start with I0=[M,M]. Bisect the current closed interval; retain its left half if infinitely many indices in S have values there, and otherwise retain the right half, which must have infinitely many such indices. At stage m1 choose the least eligible index kmS greater than km1, with k0=0, whose value lies in Im. The indices exist because an infinite subset of the naturals is unbounded. The intervals are nested and have lengths 2M2m, so the selected values are Cauchy. They converge: [F2] provides a convergent subsequence with limit L, and the Cauchy estimate followed by the triangle inequality with a sufficiently late member of that subsequence gives akmL<ε for all sufficiently large m. This also works for M=0. Left-half precedence and least indices make every stage unique, so ordinary recursion suffices; no Dependent Choice is used.

F2
2.1

Set S0=N>0. Recursively apply the rule of step 1.2 to coordinate an,j on Sj1 with M=fj, and let Sj be its infinite output index set. Thus SjSj1, and the jth coordinate converges along the increasing enumeration of Sj. Let nr be the rth smallest member of Sr. Since Sr+1Sr, its (r+1)th member is at least the (r+1)th member of Sr, which exceeds nr. Thus (nr) is strictly increasing. For every fixed j, all nr with rj belong to Sj and increase without bound, so fjdμnr converges. All index sets and enumerations are defined uniquely by the fixed rule.

1.11.2
3.1

For fC(K,R) and ε>0, choose one j with ffj<ε/3. For large r,s, step 2.1 gives fjdμnrfjdμns<ε/3; the two uniform error bounds of step 1.1 show that (fdμnr)r is Cauchy. It is bounded, and hence converges by the Cauchy-plus-[F2] argument in step 1.2. Define Λ(f) to be this unique limit. Passing to limits in [F5] proves real linearity, positivity passes to limits of nonnegative real numbers, and Λ(1)=1 since every μnr is a probability. Apply [F3] with exactly these hypotheses to obtain the regular Borel probability μ and the asserted convergence for every f. The choices used are those in [F1] and [F3], both explicitly bounded by countable choice; neither the nested extraction nor the definition of the unique limit selects an arbitrary family of witnesses.

1.11.22.1F1F2F3F5
TheoremStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Krylov–Bogolyubov existence of an invariant probability

Statement

Assume countable choice. Every continuous self-map T:KK of a nonempty compact metric space admits a Borel probability μ satisfying μ(T1E)=μ(E) for every Borel EK. Thus (K,B(K),μ,T) is a measure-preserving probability system.

Facts & Assumptions

[F1]

Under countable choice, probability sequences on nonempty compact metric spaces have subsequences converging against every real continuous test function to a Borel probability. Probability sequences on compact metric spaces have integral-convergent subsequences.

[F2]

The Dirac set function at a point is a probability on any sigma-algebra. A Dirac set function is a probability measure.

[F3]

Nonnegative measurable functions have increasing simple approximations. Every nonnegative measurable function is the increasing limit of simple measurable functions.

[F4]

Increasing nonnegative measurable approximations have increasing integrals converging to the limit integral. Monotone convergence for the integral.

[F5]

Integrals are linear on integrable real functions. The Lebesgue integral is linear on L1(μ).

[F6]

Finite measures agreeing on a generating pi-system and on total mass coincide. Finite measures agreeing on a generating pi-system and on the whole space are equal.

[F7]

Measure preservation means equality of the measure of every measurable set and its inverse image. Measure-preserving transformations and systems.

Proof

Given: Assume countable choice. Every continuous self-map T:KK of a nonempty compact metric space admits a Borel probability μ satisfying μ(T1E)=μ(E) for every Borel EK. Thus (K,B(K),μ,T) is a measure-preserving probability system.

1.1

Fix one point xK and, for N1, define μN(E)=N1j=0N1δTjx(E) on the Borel sets. By [F2], each summand is a probability; finite sums preserve countable additivity because a finite sum commutes with the increasing partial sums of a nonnegative series. Thus μN is a Borel probability. For indicators its integral is exactly N1j=0N11E(Tjx); the simple integral gives this for every nonnegative simple function. Applying [F3] and [F4], with finite sums of increasing limits, gives fdμN=N1j=0N1f(Tjx) for nonnegative Borel f. For bounded real f, apply this to f+,f and subtract by [F5]. The starting-point choice is a single existential instantiation, not an axiom of choice.

F2F3F4F5
2.1

By [F1] there are Nr and a Borel probability μ such that fdμNrfdμ for all continuous real f. Continuity of T ensures that fT is also continuous. Step 1.1 telescopes to (fTf)dμN=(f(TNx)f(x))/N, of absolute value at most 2f/N. Hence fTdμ=fdμ for every such f, by [F5] and passage to the two limits.

1.1F1F5
3.1

Define ν(E)=μ(T1E) for Borel E. The class of sets whose inverse images are Borel is a sigma-algebra containing the opens, since T is continuous; thus ν is defined on all Borel sets. Inverse images commute with complements and disjoint countable unions, so ν is a Borel probability. For indicators, 1Edν=1ETdμ. The finite simple-integral formula, then [F3] and [F4] on both sides, prove fdν=fTdμ for every nonnegative Borel f, including infinite values. Applying it first to f verifies integrability of fT whenever f is ν-integrable; positive/negative decomposition and [F5] then prove the real signed identity. Combining it with step 2.1 gives equality of μ and ν on all continuous real integrals.

2.1F3F4F5
4.1

To pass from these test functions to sets without invoking a stronger-choice LCH theorem, let F be a nonempty closed subset of K and put hm(z)=max(0,1md(z,F)) for m1. The infimum defining d(z,F) is 1-Lipschitz by the triangle inequality. It is zero on F and positive outside F, since the complement of F is open. Thus hm is continuous and 1hm1KF. By [F4] and the equal continuous integrals, μ(KF)=ν(KF); total masses one give μ(F)=ν(F). Empty F also has equal measure zero. The closed subsets form a nonempty pi-system containing K and generate the Borel sigma-algebra because their complements are exactly the opens. All hypotheses of [F6] hold, so μ=ν on the Borel sets. By the definition of ν, this is precisely [F7]. Countable choice enters through [F1]; the orbit, metric test functions and monotone simple approximants require no further selection principle.

2.13.1F1F4F6F7
False statementConstruction: AI-adaptedVerification: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

False: measure-preserving transformations are invertible

Statement

The assertion that every measure-preserving probability transformation is invertible, even after restriction to an invariant conull set, is false. Assuming countable choice, doubling on the Lebesgue circle is a counterexample.

Facts & Assumptions

[F1]

Doubling preserves the completed Lebesgue probability. Doubling preserves Lebesgue measure.

[F2]

Invertibility requires a bijection; modulo-null invertibility requires one on an invariant measurable conull restriction. Invertible measure-preserving systems.

[F4]

Finite unions of measurable null sets are null. Finite and countable subadditivity of measures.

Refutation

Given: The assertion that every measure-preserving probability transformation is invertible, even after restriction to an invariant conull set, is false. Assuming countable choice, doubling on the Lebesgue circle is a counterexample.

1.1

The map D(x)={2x} preserves the probability in [F1], but D(0)=D(1/2)=0 with 01/2. Therefore it is not injective and is not invertible in [F2]. Countable choice is the stated assumption of the Lebesgue probability supplier.

F1F2
2.1

More strongly, suppose C were a measurable conull subset of [0,1) on which D is injective, and put N=[0,1)C. For each x[0,1/2) the distinct points x,x+1/2 have equal images, so at least one belongs to N. Hence [0,1/2)N(N1/2). By [F3] both sets on the right are measurable and null, and [F4] makes their union null. This contradicts the interval measure 1/2 on the left. There is no injective conull restriction at all, in particular none satisfying the extra invariance and inverse-measurability requirements of [F2].

1.1F1F2F3F4
False statementConstruction: AI-adaptedVerification: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

False: ergodicity implies strong mixing

Statement

Assuming countable choice, the assertion that every ergodic probability-preserving transformation is strongly mixing is false: any irrational circle rotation gives a counterexample.

Facts & Assumptions

[F1]

Irrational circle rotations preserve Lebesgue probability and are ergodic. Circle rotation is ergodic for Lebesgue measure exactly at irrational angles.

[F2]

Irrational rotations have arbitrarily large positive iterates arbitrarily close to zero. Irrational circle orbits are dense.

[F3]

Strong mixing requires every set correlation to tend to the product of measures. Strong and weak mixing on a probability space.

Refutation

Given: Assuming countable choice, the assertion that every ergodic probability-preserving transformation is strongly mixing is false: any irrational circle rotation gives a counterexample.

1.1

Fix an irrational α and the ergodic Lebesgue system Rα of [F1]. Set A=[0,1/2), of measure 1/2. By [F2], define recursively nj to be the least integer greater than nj1, with n0=0, such that d({njα},0)<1/(j+3). The existence is [F2] and leastness gives unique choices. Thus nj and δj=d({njα},0)0.

F1F2
2.1

For a translation by a circle displacement with representative t[1/2,1/2], the half-circle A and its inverse translate overlap in length 1/2t: if 0t1/2, the part in A is [0,1/2t), up to endpoints, and for 1/2t0 it is [t,1/2). Consequently λ(ARαnjA)=1/2δj1/2. Strong mixing in [F3] would require the full sequence, hence this subsequence, to tend to λ(A)2=1/4. Since 1/21/4, mixing fails. Countable choice is inherited only from the ergodic Lebesgue system in [F1]; the return-index recursion is canonical.

1.1F1F3
False statementConstruction: AI-adaptedVerification: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

False: Poincare recurrence needs no finite total measure

Statement

Poincaré recurrence is false if the finite-total-measure hypothesis is omitted. Assuming countable choice, T(x)=x+1 on (R,L,λ) preserves measure, but no point of the positive-measure set E=[0,1) ever returns to E at a positive time.

Facts & Assumptions

[F1]
[F4]

A measurable self-map preserves measure exactly when each measurable inverse image has the original measure. Measure-preserving transformations and systems.

Refutation

Given: Poincaré recurrence is false if the finite-total-measure hypothesis is omitted. Assuming countable choice, T(x)=x+1 on (R,L,λ) preserves measure, but no point of the positive-measure set E=[0,1) ever returns to E at a positive time.

1.1

By [F1] this is a measure space with λ(R)=. For every Lebesgue measurable A, one has T1A=A1, which is measurable and has measure λ(A) by [F3]. Thus T is measurable and measure preserving by [F4], even though the total measure is infinite. The set E has measure one by [F2]. Countable choice is used to obtain the Lebesgue measure in [F1] and the interval value in [F2].

F1F2F3F4
2.1

Induction gives Tnx=x+n for every n0. If x[0,1) and n1, then x+n1, so Tnx[0,1). The exceptional set for recurrence is therefore all of E, of measure one, rather than a null subset. The time-zero visit does not satisfy the positive-return conclusion.

1.1
False statementConstruction: AI-adaptedVerification: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

False: an ergodic invariant sigma-algebra has only two sets

Statement

Assuming countable choice, it is false that an ergodic system has only the empty set and the whole space as strictly invariant measurable sets. For Lebesgue doubling on [0,1), the dyadic rationals form a nonempty proper strictly invariant null set.

Facts & Assumptions

[F1]

Doubling is ergodic for Lebesgue probability. Doubling is ergodic for Lebesgue measure.

[F2]

Strict invariance means exact equality with the inverse image. Strict and mod-null invariant sigma-algebras.

[F3]

Countable real sets are measurable and Lebesgue null under countable choice. Every at most countable subset of Rn is Lebesgue null; in particular λ1(Q)=0.

[F4]

Countable unions of finite sets are countable under countable choice. Countable unions of at most countable sets, assuming ACω.

Refutation

Given: Assuming countable choice, it is false that an ergodic system has only the empty set and the whole space as strictly invariant measurable sets. For Lebesgue doubling on [0,1), the dyadic rationals form a nonempty proper strictly invariant null set.

1.1

Put Q2=n0{k/2n:0k<2n, k integer}. Each level is finite, so [F4] and [F3] give measurability and λ(Q2)=0; it is also Borel as a countable union of finite closed subsets of the circle. It contains 0 and is proper: 1/3Q2, since 1/3=k/2n would give 2n=3k, while induction gives the residue of 2n modulo 3 as 1 for even n and 2 for odd n.

F3F4
2.1

If x=k/2n is dyadic, then D(x)={2x} is dyadic, including n=0 when x=0. Conversely, if D(x)=k/2n, write 2x=k/2n+ with {0,1}. Then x=(k+2n)/2n+1 is dyadic and belongs to [0,1). Hence D1Q2=Q2 exactly, as required by [F2]. By [F1] the system is ergodic, but its invariant sigma-algebra contains this nonempty proper set. Ergodicity only constrains its measure to zero or one. Countable choice is inherited from [F1], [F3] and [F4].

1.1F1F2F3F4
False statementConstruction: AI-adaptedVerification: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

False: every orbit of an ergodic system is dense

Statement

Assuming countable choice, an ergodic probability-preserving continuous map need not have every orbit dense. Lebesgue doubling on the circle is ergodic, but the forward orbit of zero is the singleton {0}.

Facts & Assumptions

[F1]

Doubling is ergodic for Lebesgue probability. Doubling is ergodic for Lebesgue measure.

[F2]

The circle is represented by [0,1), with doubling and the circle metric. The circle, rotations and the doubling map.

Refutation

Given: Assuming countable choice, an ergodic probability-preserving continuous map need not have every orbit dense. Lebesgue doubling on the circle is ergodic, but the forward orbit of zero is the singleton {0}.

1.1

The map of [F2] satisfies D(0)=0, so induction gives Dn(0)=0 for every n0. Its orbit is exactly {0}. The circle ball of radius 1/8 centered at 1/2 is a nonempty open set disjoint from this orbit, since d(0,1/2)=1/2. Thus the orbit is not dense.

F2
2.1

Nevertheless [F1] proves that doubling is ergodic for Lebesgue probability, so the displayed orbit refutes the every-point assertion. The countable-choice assumption is needed for that measure-theoretic supplier; the fixed-orbit and open-ball calculations in step 1.1 are choice-free.

1.1F1
False statementConstruction: AI-adaptedVerification: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

False: constant continuous invariants characterize measure ergodicity

Statement

The assertion that a probability-preserving continuous map is measure ergodic whenever all of its everywhere invariant continuous real functions are constant is false. On the circle, doubling has only constant everywhere invariant continuous functions, but is not ergodic for μ=12δ0+14δ1/3+14δ2/3. This atomic counterexample is choice-free, and therefore also holds under countable choice.

Facts & Assumptions

[F1]

Use only the choice-free metric and map clauses defining the circle and D. The circle, rotations and the doubling map.

[F2]

A probability-preserving map is ergodic when each strictly invariant measurable set has measure zero or one. Ergodicity relative to an invariant measure.

[F3]

Dirac set functions are probabilities without a choice assumption. A Dirac set function is a probability measure.

[F4]

The natural numbers are cofinal in the reals. Every complete ordered field is Archimedean.

Refutation

Given: The assertion that a probability-preserving continuous map is measure ergodic whenever all of its everywhere invariant continuous real functions are constant is false. On the circle, doubling has only constant everywhere invariant continuous functions, but is not ergodic for μ=12δ0+14δ1/3+14δ2/3. This atomic counterexample is choice-free, and therefore also holds under countable choice.

1.1

For D from [F1], D(0)=0, D(1/3)=2/3 and D(2/3)=1/3. The measure μ displayed in the statement is a Borel probability by [F3] and finite additivity of the weighted sum of measures; countable additivity follows by commuting a finite sum with increasing partial sums. For every Borel E, μ(D1E)=121E(0)+141E(2/3)+141E(1/3)=μ(E), so it is invariant. Continuity of D gives its Borel measurability.

F1F3
1.2

Put Z=n0(Dn)1{0}. Each inverse image is Borel since Dn is continuous and {0} is closed, so Z is Borel. A point lies in Z exactly when it eventually maps to zero. If it does, then so does its image, since zero is fixed; conversely if its image eventually maps to zero, the point does one step later. Thus D1Z=Z. The point zero is in Z, while 1/3 and 2/3 stay in their two-cycle and never reach zero. Therefore μ(Z)=1/2, which violates [F2].

1.1F2
2.1

Now let continuous real f satisfy fD=f at every circle point. Iteration shows f(k/2n)=f(0) for 0k<2n, since Dn(k/2n)=0. For any x[0,1) put qn=2n2nx. Then 0xqn<2n, so qnx in the circle metric: 2nn+1 by induction and [F4] gives 2n0. Continuity yields f(x)=limnf(qn)=f(0). Hence all the stated continuous invariants are constant while the invariant probability is nonergodic. No Lebesgue measure, countable union of countable sets, or choice principle is used; the sets and sequences are explicitly defined.

1.11.2F1F4

5 · Examples, counterexamples and false statements

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