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Entanglement bound with a shared global state

Proved
MarkovEntanglement.shared_state_local_transition_deviation_nonempty

by tianyipeng · Aug 7, 2026 · Mathlib c5ea003 (Lean v4.30.0)

markov-entanglementmulti-agent-mdpshared-statevalue-decomposition

Statement

Consider an NNN-agent Markov system with a shared global coordinate, so that a joint state is a pair p=(p1,z)p=(p_1,z)p=(p1​,z) of a local state-action for each agent together with a common coordinate zzz, and assume every agent's local space is nonempty. Fix an agent iii and write

mi(p,t)  =  ∑q : (q1)i=t1, q2=t2P(p,q),t∈Si×Z,m_i(p,t) \;=\; \sum_{q\,:\,(q_1)_i=t_1,\ q_2=t_2} P(p,q), \qquad t \in S_i \times Z,mi​(p,t)=q:(q1​)i​=t1​, q2​=t2​∑​P(p,q),t∈Si​×Z,

for the joint transition marginalised onto agent iii's coordinate together with the shared one.

Suppose the marginal is exactly local, i.e. there is a matrix PtrueP^{\mathrm{true}}Ptrue on Si×ZS_i \times ZSi​×Z with

mi(p,t)  =  Ptrue((p1)i,p2; t)for all p, t,m_i(p,t) \;=\; P^{\mathrm{true}}\bigl((p_1)_i, p_2;\, t\bigr) \qquad\text{for all } p,\,t,mi​(p,t)=Ptrue((p1​)i​,p2​;t)for all p,t,

and suppose a candidate local transition PiP_iPi​ approximates the same marginal entrywise,

∣mi(p,t)−Pi((p1)i,p2; t)∣  ≤  Efor all p, t.\bigl| m_i(p,t) - P_i\bigl((p_1)_i, p_2;\, t\bigr) \bigr| \;\le\; \mathcal{E} \qquad\text{for all } p,\,t .​mi​(p,t)−Pi​((p1​)i​,p2​;t)​≤Efor all p,t.

Then PtrueP^{\mathrm{true}}Ptrue and PiP_iPi​ are uniformly close:

∥Ptrue−Pi∥∞  ≤  2 E.\bigl\| P^{\mathrm{true}} - P_i \bigr\|_\infty \;\le\; 2\,\mathcal{E}.​Ptrue−Pi​​∞​≤2E.

Notes

The shared-state analogue of the local transition bound, and the step that carries the entanglement machinery over to systems with a common observable coordinate. Together with the exact-decomposition result for separable shared-state systems it gives the shared-state theory its two halves: no error when the system is separable, a controlled error when it is not.

Why every local space must be nonempty. This hypothesis is not decoration. Both assumptions above quantify over joint states ppp, while the conclusion quantifies over Si×ZS_i \times ZSi​×Z. If some other agent j≠ij \neq ij=i has an empty state space, the joint space is empty, both hypotheses hold vacuously for arbitrary PtrueP^{\mathrm{true}}Ptrue and arbitrary E\mathcal{E}E, and yet Si×ZS_i \times ZSi​×Z can be nonempty — so the conclusion fails outright. A version of this statement without the nonemptiness assumption is refutable: take two agents with S0S_0S0​ empty and S1S_1S1​, ZZZ singletons, Ptrue≡5P^{\mathrm{true}} \equiv 5Ptrue≡5, Pi≡1P_i \equiv 1Pi​≡1 and E=0\mathcal{E}=0E=0.

With every SjS_jSj​ nonempty the proof is immediate, and this is what nonemptiness buys: given any s∈Si×Zs \in S_i \times Zs∈Si​×Z one can realise it, by taking an arbitrary local state for each agent and overwriting agent iii's with sss's first component. Evaluating both hypotheses at that joint state gives ∣Ptrue(s,t)−Pi(s,t)∣≤E|P^{\mathrm{true}}(s,t) - P_i(s,t)| \le \mathcal{E}∣Ptrue(s,t)−Pi​(s,t)∣≤E directly, so the stated constant 222 is slack here — it is inherited from the product-state versions, where E\mathcal{E}E measures a total variation distance carrying a factor 12\tfrac1221​, whereas here it already bounds individual entries.

Note also that the μ\muμ-weighted siblings of this statement need no such hypothesis: there a strictly positive occupancy measure with total mass one already forces the joint space to be inhabited.

Search terms: marginalised transition with shared state, agent-wise total variation, global coordinate multi-agent MDP, entrywise transition bound, weakly coupled MDP with common state.

Preamble
import Mathlib
import Definitions.Def_markov_entanglement_multi

open scoped BigOperators
open MarkovEntanglement
Formal statement
namespace MarkovEntanglement

theorem shared_state_local_transition_deviation_nonempty
    {N : ℕ} {S : Fin N → Type*} [∀ i, Fintype (S i)] [∀ i, DecidableEq (S i)]
    [∀ i, Nonempty (S i)]
    {Z : Type*} [Fintype Z] [DecidableEq Z]
    (P : Matrix (JointZ S Z) (JointZ S Z) ℝ) (hP : IsTransitionMatrix P)
    (i : Fin N) (E : ℝ)
    (Pi Ptrue : Matrix (S i × Z) (S i × Z) ℝ) (hPi : IsTransitionMatrix Pi)
    (htrue : ∀ p t, marginalZ i P p t = Ptrue (p.1 i, p.2) t)
    (hE : ∀ p t, |marginalZ i P p t - Pi (p.1 i, p.2) t| ≤ E) :
    ∀ s t, |Ptrue s t - Pi s t| ≤ 2 * E := by
  sorry

end MarkovEntanglement
Source
Shuze Chen and Tianyi Peng, *Multi-agent Markov Entanglement*, arXiv:2506.02385v3, Theorem 9, p. 46

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