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Multi-step concentration of the configuration (Lem. 10, Gast et al. 2023)

Proved
MarkovEntanglement.rmab_multi_step_concentration

by tianyipeng · Aug 28, 2026 · Mathlib c5ea003 (Lean v4.30.0)

There is a constant KKK, independent of the system size NNN, such that for every horizon ttt and every δ>0\delta > 0δ>0 the configuration of the NNN-agent chain after ttt steps stays close to the ttt-th iterate of the explicit mean-field map applied to the starting configuration:

Pr⁡[ ∥m[t]−Φt(m)∥∞≥(1+K+K2+⋯+Kt) δ  ∣  m[0]=m ]  ≤  2 t ∣S∣ e−Nδ2/2,\Pr\Big[\, \|m[t] - \Phi^t(m)\|_\infty \ge (1 + K + K^2 + \cdots + K^t)\,\delta \;\Big|\; m[0] = m \,\Big] \;\le\; 2\,t\,|S|\,e^{-N\delta^2/2},Pr[∥m[t]−Φt(m)∥∞​≥(1+K+K2+⋯+Kt)δ​m[0]=m]≤2t∣S∣e−Nδ2/2,

where Φ\PhiΦ is the explicit mean-field map at the system's exact activation fraction ⌊αN⌋/N\lfloor \alpha N \rfloor / N⌊αN⌋/N. The iterate must be that of the explicit map: a map merely characterised on the configurations the NNN-agent system can occupy leaves that lattice after one step, and off the lattice the characterisation says nothing, so no bound of this shape could hold for it.

Two effects are being combined. Each single step contributes a deviation of size δ\deltaδ with failure probability ∣S∣e−2Nδ2|S| e^{-2N\delta^2}∣S∣e−2Nδ2 — Hoeffding's inequality for the NNN conditionally independent indicators that make up the next configuration, applied coordinatewise and union-bounded over the ∣S∣|S|∣S∣ coordinates. And a deviation incurred at step jjj is then transported forward by the remaining t−jt - jt−j applications of the mean-field map, which is Lipschitz with some constant KKK uniformly in the activation fraction; the geometric sum 1+K+⋯+Kt1 + K + \cdots + K^t1+K+⋯+Kt is the accumulated amplification.

Taken alone this bound degrades with the horizon whenever K>1K > 1K>1. What rescues it is the local stability of Lemma 11: near the mean-field fixed point the dynamics contract, so the amplification stops compounding and the horizon can be chosen uniformly. The two lemmas are used together, never separately.

The formalized bound weakens the source's constants: the exponent is Nδ2/2N\delta^2/2Nδ2/2 rather than 2Nδ22N\delta^22Nδ2 (the per-step tail is proved by a sub-Gaussian chord bound — convexity of the exponential plus cosh⁡λ≤eλ2/2\cosh \lambda \le e^{\lambda^2/2}coshλ≤eλ2/2 — rather than Hoeffding's lemma), and the prefactor is 2∣S∣2|S|2∣S∣ per step because the sup-norm event union-bounds over two tails for each coordinate. The functional form, and with it the 1/N1/\sqrt{N}1/N​ rate of Theorem 7, is unchanged.

Preamble
import Mathlib
import Definitions.Def_markov_entanglement_meanfield

open scoped BigOperators
open MarkovEntanglement
Formal statement
namespace MarkovEntanglement

variable {S : Type*} [Fintype S] [DecidableEq S]

/-- Lemma 10 (Multi-step Concentration; Lemma C.4 of Gast, Gaujal and Yan 2023).  There is a
constant `K`, independent of `N`, such that for every horizon `t` and every `δ > 0` the
configuration after `t` steps stays within `(1 + K + ⋯ + Kᵗ) δ` of the `t`-th iterate of the
explicit mean-field map, except on an event of probability at most `2 t |S| e^{−Nδ²/2}`.
The exponent constant is weakened from the source's `2Nδ²` to `Nδ²/2` (a sub-Gaussian
chord bound in place of Hoeffding's lemma) and the prefactor doubled (two tails per
coordinate); neither affects the `1/√N` rate downstream.

The iterate must be that of the **explicit** map at the system's exact activation fraction
`⌊αN⌋ / N`: a map merely characterised on the configurations of the `N`-agent system leaves
the lattice after one step, where the characterisation says nothing.  The error amplifies
geometrically in `t` because each step's deviation is transported by the (Lipschitz) explicit
map, while the per-step failure probability is the Hoeffding bound for `N` conditionally
independent indicators. -/
theorem rmab_multi_step_concentration
    (P0 P1 : Matrix S S ℝ) (hP0 : IsTransitionMatrix P0) (hP1 : IsTransitionMatrix P1)
    (ν : S → ℝ) (hν : Function.Injective ν) (α : ℝ) (hα : 0 < α) (hα1 : α < 1) :
    ∃ K : ℝ, 0 ≤ K ∧
      ∀ (N : ℕ), 0 < N →
        ∀ (π : (Fin N → S) → (Fin N → Bool) → ℝ),
          IsIndexPolicy ν ⌊α * (N : ℝ)⌋₊ π →
          ∀ (s0 : Fin N → S) (t : ℕ) (δ : ℝ), 0 < δ →
            ∑ s ∈ Finset.univ.filter (fun s : Fin N → S =>
                (∑ j ∈ Finset.range (t + 1), K ^ j) * δ ≤
                  supNorm (fun x => configuration s x
                    - meanFieldIterate
                        (meanFieldMap P0 P1 ν ((⌊α * (N : ℝ)⌋₊ : ℝ) / (N : ℝ)))
                        t (configuration s0) x)),
              rmabLaw P0 P1 π s0 t s
              ≤ 2 * (t : ℝ) * (Fintype.card S : ℝ)
                  * Real.exp (-(N : ℝ) * δ ^ 2 / 2) := by
  sorry

/-! ### M6 — Lemma 11, local stability (Gast, Gaujal and Yan) -/

end MarkovEntanglement
Source
Nicolas Gast, Bruno Gaujal and Chen Yan, 'Exponential asymptotic optimality of Whittle index policy' (2023), Lemma C.4; cited as Lemma 10 in Chen and Peng, arXiv:2506.02385v3, Appendix I, p. 43
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What the Lean code literally says, in plain math · claude-opus-5

Fix a finite type SSS of states with decidable equality (nothing forces SSS to be nonempty). Given

  • two real S×SS\times SS×S matrices P0,P1P_0,P_1P0​,P1​, each assumed to be a transition matrix, meaning every entry satisfies Pa(x,y)≥0P_a(x,y)\ge 0Pa​(x,y)≥0 and every row satisfies ∑y∈SPa(x,y)=1\sum_{y\in S}P_a(x,y)=1∑y∈S​Pa​(x,y)=1;
  • a priority function ν:S→R\nu:S\to\mathbb Rν:S→R assumed injective (distinct states get distinct priority values);
  • a real number α\alphaα with 0<α<10<\alpha<10<α<1;

the statement asserts the existence of a real constant KKK with K≥0K\ge 0K≥0, depending only on the data just listed (SSS, P0P_0P0​, P1P_1P1​, ν\nuν, α\alphaα) and on nothing quantified later, such that for every natural number NNN with N>0N>0N>0, for every function π:(Fin N→S)→(Fin N→Bool)→R\pi:(\mathrm{Fin}\,N\to S)\to(\mathrm{Fin}\,N\to\mathrm{Bool})\to\mathbb Rπ:(FinN→S)→(FinN→Bool)→R that is an index policy with budget MN:=⌊αN⌋NM_N:=\lfloor\alpha N\rfloor_{\mathbb N}MN​:=⌊αN⌋N​ (the natural-number floor of αN\alpha NαN; since 0<α<10<\alpha<10<α<1 this is an integer with 0≤MN≤N−10\le M_N\le N-10≤MN​≤N−1, and MN=0M_N=0MN​=0 when N=1N=1N=1), for every initial joint state s0:Fin N→Ss_0:\mathrm{Fin}\,N\to Ss0​:FinN→S, for every t∈Nt\in\mathbb Nt∈N and for every real δ>0\delta>0δ>0, the inequality

∑s∈B  Lt(s)  ≤  2 t ∣S∣ e−Nδ2/2\sum_{s\in B}\;\mathcal L_t(s)\;\le\;2\,t\,|S|\,e^{-N\delta^2/2}s∈B∑​Lt​(s)≤2t∣S∣e−Nδ2/2

holds, where the ingredients are the following, all spelled out from their definitions.

"π\piπ is an index policy with budget MMM" means the conjunction of three conditions: (i) π(s,a)≥0\pi(s,a)\ge 0π(s,a)≥0 for all joint states sss and all action profiles a:Fin N→Boola:\mathrm{Fin}\,N\to\mathrm{Bool}a:FinN→Bool, and ∑aπ(s,a)=1\sum_{a}\pi(s,a)=1∑a​π(s,a)=1 for every sss (the sum ranging over all 2N2^N2N profiles); (ii) for all s,as,as,a, if π(s,a)≠0\pi(s,a)\neq 0π(s,a)=0 then aaa activates exactly MMM agents, i.e. #{i:ai=true}=M\#\{i:a_i=\text{true}\}=M#{i:ai​=true}=M; (iii) for every joint state sss and every agent index iii, the marginal activation probability

∑a[ ai=true ] π(s,a)\sum_{a}\big[\,a_i=\text{true}\,\big]\,\pi(s,a)a∑​[ai​=true]π(s,a)

equals qνM(s,si)q_\nu^M(s,s_i)qνM​(s,si​), where for x∈Sx\in Sx∈S, writing nx(s):=#{i:si=x}n_x(s):=\#\{i: s_i=x\}nx​(s):=#{i:si​=x} for the number of agents in state xxx and Hν(s,x):=∑y: ν(x)<ν(y)ny(s)H_\nu(s,x):=\sum_{y:\ \nu(x)<\nu(y)} n_y(s)Hν​(s,x):=∑y: ν(x)<ν(y)​ny​(s) for the number of agents in strictly higher-priority states,

qνM(s,x):={0,nx(s)=0,min⁡(nx(s), M⊖Hν(s,x))nx(s),otherwise,q_\nu^M(s,x):=\begin{cases}0,& n_x(s)=0,\\[2pt]\dfrac{\min\big(n_x(s),\,M\ominus H_\nu(s,x)\big)}{n_x(s)},&\text{otherwise,}\end{cases}qνM​(s,x):=⎩⎨⎧​0,nx​(s)min(nx​(s),M⊖Hν​(s,x))​,​nx​(s)=0,otherwise,​

with ⊖\ominus⊖ denoting truncated natural subtraction (it is 000 whenever Hν(s,x)≥MH_\nu(s,x)\ge MHν​(s,x)≥M). Nothing in the statement asserts that such a π\piπ exists for a given NNN; for any NNN admitting no such π\piπ the inner claim is vacuous.

Lt\mathcal L_tLt​ is the law of the joint state after ttt steps started deterministically at s0s_0s0​, defined recursively by L0(s)=1\mathcal L_0(s)=1L0​(s)=1 if s=s0s=s_0s=s0​ and 000 otherwise, and Lt+1(s′)=∑sLt(s) Π(s,s′)\mathcal L_{t+1}(s')=\sum_{s}\mathcal L_t(s)\,\Pi(s,s')Lt+1​(s′)=∑s​Lt​(s)Π(s,s′), where one step is

Π(s,s′)=∑a:Fin N→Boolπ(s,a)∏j=0N−1(aj ? P1(sj,sj′):P0(sj,sj′)),\Pi(s,s')=\sum_{a:\mathrm{Fin}\,N\to\mathrm{Bool}}\pi(s,a)\prod_{j=0}^{N-1}\big(a_j\,?\,P_1(s_j,s'_j):P_0(s_j,s'_j)\big),Π(s,s′)=a:FinN→Bool∑​π(s,a)j=0∏N−1​(aj​?P1​(sj​,sj′​):P0​(sj​,sj′​)),

i.e. the agents draw a joint action profile from π\piπ and then move independently, agent jjj using P1P_1P1​ if activated and P0P_0P0​ otherwise.

BBB is the set of joint states s:Fin N→Ss:\mathrm{Fin}\,N\to Ss:FinN→S satisfying

(∑j=0tK j) δ  ≤  ∥ cfg(s)−φt(cfg(s0)) ∥∞,\Big(\sum_{j=0}^{t}K^{\,j}\Big)\,\delta\;\le\;\big\|\,\mathrm{cfg}(s)-\varphi^{t}\big(\mathrm{cfg}(s_0)\big)\,\big\|_\infty ,(j=0∑t​Kj)δ≤​cfg(s)−φt(cfg(s0​))​∞​,

a non-strict inequality with the geometric factor on the left, the sum running over j=0,1,…,tj=0,1,\dots,tj=0,1,…,t (so it has t+1t+1t+1 terms and equals 111 when K=0K=0K=0, since 00=10^0=100=1). Here:

  • cfg(s)(x)=nx(s)/N\mathrm{cfg}(s)(x)=n_x(s)/Ncfg(s)(x)=nx​(s)/N is the empirical configuration (real division; N>0N>0N>0 is assumed);
  • ∥v∥∞=sup⁡x∈S∣v(x)∣\|v\|_\infty=\sup_{x\in S}|v(x)|∥v∥∞​=supx∈S​∣v(x)∣, a real supremum, which is 000 when SSS is empty;
  • φ=ΦP0,P1,ν,β\varphi=\Phi_{P_0,P_1,\nu,\beta}φ=ΦP0​,P1​,ν,β​ with activation fraction β:=MN/N=⌊αN⌋N/N\beta:=M_N/N=\lfloor\alpha N\rfloor_{\mathbb N}/Nβ:=MN​/N=⌊αN⌋N​/N — the rounded fraction, not α\alphaα itself — and φt\varphi^tφt is the ttt-fold composite (φ0\varphi^0φ0 being the identity, so at t=0t=0t=0 the comparison point is cfg(s0)\mathrm{cfg}(s_0)cfg(s0​) itself). The map Φ\PhiΦ acts on an arbitrary function m:S→Rm:S\to\mathbb Rm:S→R (no constraint that mmm be a probability vector is imposed by the definition) by
Φ(m)(y)=∑x∈S[(m(x)−f(m,x))P0(x,y)+f(m,x) P1(x,y)],f(m,x)=min⁡ ⁣(m(x), max⁡(0, β−∑y: ν(x)<ν(y)m(y))).\Phi(m)(y)=\sum_{x\in S}\Big[\big(m(x)-f(m,x)\big)P_0(x,y)+f(m,x)\,P_1(x,y)\Big],\qquad f(m,x)=\min\!\Big(m(x),\ \max\big(0,\ \beta-\textstyle\sum_{y:\ \nu(x)<\nu(y)} m(y)\big)\Big).Φ(m)(y)=x∈S∑​[(m(x)−f(m,x))P0​(x,y)+f(m,x)P1​(x,y)],f(m,x)=min(m(x), max(0, β−∑y: ν(x)<ν(y)​m(y))).

The right-hand side is 2 t ∣S∣ exp⁡ ⁣(−Nδ2/2)2\,t\,|S|\,\exp\!\big(-N\delta^2/2\big)2t∣S∣exp(−Nδ2/2), with ttt and ∣S∣=card(S)|S|=\mathrm{card}(S)∣S∣=card(S) cast to reals, the constant KKK appearing nowhere in it, and δ\deltaδ entering only through δ2\delta^2δ2. In particular, at t=0t=0t=0 the right-hand side is exactly 000, so the claim there is that the total L0\mathcal L_0L0​-mass of BBB is ≤0\le 0≤0; if SSS is empty and N>0N>0N>0 then there are no joint states at all, both sides are 000, and the whole statement is degenerate. For large δ\deltaδ the set BBB may be empty, making the left side an empty sum equal to 000. The quantifier order is essential: a single K≥0K\ge 0K≥0 must work simultaneously for all NNN, all admissible π\piπ, all starting states s0s_0s0​, all horizons ttt, and all δ>0\delta>0δ>0.

Human review
  • Endorsed by Shuze Chen · Aug 30, 2026

  • Endorsed by tianyipeng · Aug 30, 2026

    Confirmed by the mission captain (proposal self-audit).

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