Agent-wise marginal independence characterizes separable joint transitions
ProvedMarkovEntanglement.agentwise_marginal_independence_implies_separableAgent-wise marginal independence forces separability.
Let be a transition matrix on the joint state-action space of agents. Suppose that for every agent , summing over agent 's next state leaves a quantity that no longer depends on agent 's current state: for all and all joint state-action pairs ,
Then is separable in the sense of Definition 10: it is a finite affine combination of -fold tensor products of local transition matrices,
Notes
The converse is immediate, so this is a characterization of separability: an agent is decoupled from the joint chain exactly when its own marginal dynamics are blind to its own state. In the tensor picture, writing for the span of the transition matrices (Lemma 3), the hypothesis says lies in for each , and the conclusion is that the intersection over is — the -fold tensor-intersection identity that the two-agent argument of Appendix D only needs for .
import Mathlib import Definitions.Def_markov_entanglement_multi open scoped BigOperators open MarkovEntanglement
namespace MarkovEntanglement
theorem agentwise_marginal_independence_implies_separable
{N : ℕ} {S : Fin N → Type*} [∀ i, Fintype (S i)] [∀ i, DecidableEq (S i)]
(P : Matrix (Joint S) (Joint S) ℝ) (hP : IsTransitionMatrix P)
(hmarg : ∀ (i : Fin N) (p p' q : Joint S), (∀ j, j ≠ i → p j = p' j) →
∑ t : S i, P p (Function.update q i t) = ∑ t : S i, P p' (Function.update q i t)) :
IsSeparableN P := by
sorry
end MarkovEntanglement