Reason: Initial published proof: martingale cross-moment facts via dyadic truncation, second-moment matrix evolution, and integration-by-parts weighting by Z.
Proof
Write 1=1Ω0 (equal to 1 on the regular event Ω0 and 0 off it). Since Ω0 has probability 1, expectations are unchanged when integrands are modified off Ω0, and we use this silently. All uses of the Tonelli and Fubini theorems are on the product of [0,T] (trace Borel σ-algebra, restricted Lebesgue measure, total mass T by the toolkit) with the probability space (Ω,F,P), both finite, hence σ-finite.
Step 0 (measurability and bounds). Every Σt lies in the probability simplex (each agent occupies exactly one state, by the derived notation of the solution definition), so ∣st∣≤2N everywhere, any two points of the simplex having Euclidean norm at most 1. By the joint measurability lemma and measurability of sequentially continuous functions of measurable maps, the maps 1stγ, 1bγ(Σt,αt), and 1Θγδ(Σt,αt) are product-measurable: for the latter two, replace (Σt,αt) off Ω0 by a fixed point of Δl×Rm and compose the componentwise-measurable modified map with the sequentially continuous functions bγ and Θγδ, whose sequential continuity follows from the joint continuity clause of the transition-rate family and continuity of the coordinate factors in their defining formulas; for 1stγ, subtract the product-measurable (t,ω)↦1Stγ (continuous in t, via the composition lemma applied to (t,ω)↦t). On Ω0, ∣bγ∣≤2(l−1)B and ∣Θγδ∣≤2(l−1)B by part (a) of the martingale decomposition theorem, so ∣gsγ∣≤4N(l−1)B there. Since each Zγδ and z˙γδ is continuous, hence bounded, every expectation named in part (a) of the statement is finite and bounded in s, and its measurability in s follows from the Fubini theorem, the (1-modified) integrands being bounded and product-measurable on a finite product measure. This proves (a).
(1a) If X is a bounded random variable that is measurable with respect to the system filtration entry Fssys, then E[X(mtδ−msδ)]=0. Indeed, with ∣X∣≤c, the dyadic truncations Xn=2−n⌊2nX⌋ (a finite sum ∑kk2−n1Dn,k over the finitely many levels with ∣k∣2−n≤c+1, each Dn,k∈Fssys) satisfy ∣Xn−X∣≤2−n; the defining property of the square-integrable martingale Mδ gives E[1Dn,k(Mtδ−Msδ)]=0 for each level set, hence E[Xn(mtδ−msδ)]=0 by linearity, and ∣E[(X−Xn)(mtδ−msδ)]∣≤2−n(E∣mtδ∣+E∣msδ∣)→0, using that square-integrable variables are integrable (Cauchy-Schwarz for the mean-square norm against the constant 1).
(1b) E[s0γmtδ]=0: apply (1a) with s=0, X=s0γ (bounded by 2N; Σ0 is F0sys-measurable by part (iv) of the existence theorem) and m0δ=0.
(1c) E[gsγmtδ]=E[gsγmsδ]: apply (1a) with X=gsγ, which is bounded and Fssys-measurable (Σs and αs are adapted by part (iv) of the existence theorem, and bγ composed with them is measurable by the composition lemma; bγ(Ss,As) is a constant).
(1d) E[mtγmtδ]=∫[0,t]E[Θγδ(Σs,αs)]ds: this is the covariation identity, part (c) of the decomposition theorem, with r=0, D=Ω, multiplied by N, together with the Fubini theorem to exchange E and the time integral of the (after 1-modification) product-measurable bounded integrand.
Step 2 (evolution of the second-moment matrix). Fix γ,δ and set Ψγδ(t)=E[stγstδ]. Expanding the product of the two three-term representations of Step 1 and taking expectations termwise (all nine terms are integrable, every factor being bounded except the martingales, which are square-integrable):
the terms E[s0γmtδ] and E[mtγs0δ] vanishing by (1b). Now: E[s0γFtδ]=∫[0,t]E[s0γgsδ]ds by the Fubini theorem (bounded integrand). Pathwise, the integration by parts lemma on [0,t] with u0=v0=0 gives FtγFtδ=∫[0,t](gsγFsδ+Fsγgsδ)ds almost surely, so E[FtγFtδ]=∫[0,t]E[gsγFsδ+Fsγgsδ]ds (Fubini; ∣Fsγ∣≤4N(l−1)BT on Ω0). Also Ftγmtδ=∫[0,t]gsγds⋅mtδ=∫[0,t]gsγmtδds pathwise, so by the Fubini theorem (the integrand is dominated by 4N(l−1)B∣mtδ∣, which is integrable on the product) and (1c),
with each ψγδ bounded, and measurable by the same Fubini argument as in Step 0 applied to the bounded product-measurable integrands 1gγsδ and 1Θγδ.
Step 3 (weighting by Z). Fix t∈[0,T]; the case t=0 is trivial, so let t>0. Apply the integration by parts lemma on [0,t] to u=Zγδ (with density z˙γδ, continuous, Riemann and Lebesgue integrals agreeing) and v=Ψγδ (with density ψγδ):
Sum over γ,δ∈{1,…,l}. On the left, ∑γ,δZtγδΨγδ(t)=E[st⋅Ztst] by linearity of the expectation, and likewise at 0. In the integrand, ∑γ,δz˙γδ(s)Ψγδ(s)=E[ss⋅z˙(s)ss], while by the symmetry of Zs and relabeling of the summation indices,