Fix parameters as in The Ising Population Data (i) , adopt the Ising population data with those parameters, and adopt The Ising Population Model Instantiates the Data of the Fluctuation Theory and The Even-Split Equilibrium of the Ising Population Model is a Stationary Mean-Field Triple and is Optimal , so that the equilibrium triple ( S , A , P ) (S,A,P) ( S , A , P ) is a stationary mean-field triple for β \beta β , ( L , G ) (L,G) ( L , G ) and the extensions ( U , V , β ˉ ) (U,V,\bar{\beta}) ( U , V , β ˉ ) , ( U c , L ˉ , G ˉ ) (U_{c},\bar{L},\bar{G}) ( U c , L ˉ , G ˉ ) . Let H i j ( t ) H_{ij}(t) H ij ( t ) (i , j ∈ { 1 , 2 , 3 , 4 } i,j\in\{1,2,3,4\} i , j ∈ { 1 , 2 , 3 , 4 } ) and F γ δ F_{\gamma\delta} F γ δ (γ , δ ∈ { 1 , 2 } \gamma,\delta\in\{1,2\} γ , δ ∈ { 1 , 2 } ) be the fluctuation Hessian coefficients of these data, and let E t \mathcal{E}_{t} E t , B t \mathcal{B}_{t} B t , E ~ t \tilde{\mathcal{E}}_{t} E ~ t , H t S S H^{SS}_{t} H t SS , H t S A H^{SA}_{t} H t S A , H t A S H^{AS}_{t} H t A S , H t A A H^{AA}_{t} H t AA , F ⋆ F^{\star} F ⋆ , Θ t ⋆ \Theta^{\star}_{t} Θ t ⋆ and Θ ~ t ⋆ \tilde{\Theta}^{\star}_{t} Θ ~ t ⋆ be the fluctuation LQG data of ( S , A , P ) (S,A,P) ( S , A , P ) relative to ( U , V , β ˉ ) (U,V,\bar{\beta}) ( U , V , β ˉ ) , ( U c , L ˉ , G ˉ ) (U_{c},\bar{L},\bar{G}) ( U c , L ˉ , G ˉ ) and the observation-rate extension ( U ~ , β ~ ˉ ) (\tilde{U},\bar{\tilde{\beta}}) ( U ~ , β ~ ˉ ) . Following Completion of Squares and A Priori Control Bound for the Fluctuation Cost , define for t ∈ [ 0 , T ] t\in[0,T] t ∈ [ 0 , T ] the real matrices E t E_{t} E t (2 × 2 2\times2 2 × 2 ), B t \mathsf{B}_{t} B t (2 × 2 2\times2 2 × 2 ), Q t Q_{t} Q t (2 × 2 2\times2 2 × 2 ), V t V_{t} V t (2 × 2 2\times2 2 × 2 ), R t R_{t} R t (2 × 2 2\times2 2 × 2 ) and F ^ \hat{F} F ^ (2 × 2 2\times2 2 × 2 ) by
E t δ γ = ∂ γ b ˉ δ ( S t , A t ) , B t δ j = ∂ 2 + j b ˉ δ ( S t , A t ) , E^{\delta\gamma}_{t}=\partial_{\gamma}\bar{b}^{\delta}(S_{t},A_{t}),\qquad \mathsf{B}^{\delta j}_{t}=\partial_{2+j}\bar{b}^{\delta}(S_{t},A_{t}), E t δ γ = ∂ γ b ˉ δ ( S t , A t ) , B t δ j = ∂ 2 + j b ˉ δ ( S t , A t ) ,
Q t γ δ = 1 4 ( H γ δ ( t ) + H δ γ ( t ) ) , V t γ j = 1 2 ( H γ , 2 + j ( t ) + H 2 + j , γ ( t ) ) , R t i j = 1 4 ( H 2 + i , 2 + j ( t ) + H 2 + j , 2 + i ( t ) ) , F ^ γ δ = 1 4 ( F γ δ + F δ γ ) , Q^{\gamma\delta}_{t}=\tfrac{1}{4}\bigl(H_{\gamma\delta}(t)+H_{\delta\gamma}(t)\bigr),\quad V^{\gamma j}_{t}=\tfrac{1}{2}\bigl(H_{\gamma,2+j}(t)+H_{2+j,\gamma}(t)\bigr),\quad R^{ij}_{t}=\tfrac{1}{4}\bigl(H_{2+i,2+j}(t)+H_{2+j,2+i}(t)\bigr),\quad \hat{F}^{\gamma\delta}=\tfrac{1}{4}\bigl(F_{\gamma\delta}+F_{\delta\gamma}\bigr), Q t γ δ = 4 1 ( H γ δ ( t ) + H δ γ ( t ) ) , V t γj = 2 1 ( H γ , 2 + j ( t ) + H 2 + j , γ ( t ) ) , R t ij = 4 1 ( H 2 + i , 2 + j ( t ) + H 2 + j , 2 + i ( t ) ) , F ^ γ δ = 4 1 ( F γ δ + F δ γ ) ,
for γ , δ ∈ { 1 , 2 } \gamma,\delta\in\{1,2\} γ , δ ∈ { 1 , 2 } and i , j ∈ { 1 , 2 } i,j\in\{1,2\} i , j ∈ { 1 , 2 } ; these are the matrices written with the same symbols in that theorem, and the sans-serif B t \mathsf{B}_{t} B t is distinct from the rate bound B = a ˉ B=\bar{a} B = a ˉ while the matrices V t V_{t} V t are distinct from the open set V V V of clause The Ising Population Data (iii) . Write I I I for the 2 × 2 2\times2 2 × 2 identity matrix. Then the following hold, for every t ∈ [ 0 , T ] t\in[0,T] t ∈ [ 0 , T ] .
1. (The Hessian coefficients.) (i) F γ δ = 0 F_{\gamma\delta}=0 F γ δ = 0 for all γ , δ \gamma,\delta γ , δ , and
H 11 ( t ) = H 22 ( t ) = ψ + 2 μ , H 12 ( t ) = H 21 ( t ) = 2 μ − ψ , H 33 ( t ) = H 44 ( t ) = 1 2 χ , H_{11}(t)=H_{22}(t)=\psi+2\mu,\qquad H_{12}(t)=H_{21}(t)=2\mu-\psi,\qquad H_{33}(t)=H_{44}(t)=\frac{1}{2\chi}, H 11 ( t ) = H 22 ( t ) = ψ + 2 μ , H 12 ( t ) = H 21 ( t ) = 2 μ − ψ , H 33 ( t ) = H 44 ( t ) = 2 χ 1 ,
while H i j ( t ) = 0 H_{ij}(t)=0 H ij ( t ) = 0 for every other pair ( i , j ) (i,j) ( i , j ) . In particular H i j ( t ) = H j i ( t ) H_{ij}(t)=H_{ji}(t) H ij ( t ) = H ji ( t ) for all i , j i,j i , j , and the coefficients do not depend on t t t .
2. (The coefficient matrices.) (ii)
E t = − v v ⊤ , B t = − 1 2 v v ⊤ , Q t = 1 2 ψ v v ⊤ + μ n n ⊤ , V t = 0 , R t = 1 4 χ I , F ^ = 0. E_{t}=-vv^{\top},\qquad \mathsf{B}_{t}=-\tfrac{1}{2}vv^{\top},\qquad Q_{t}=\tfrac{1}{2}\psi\,vv^{\top}+\mu\,\mathsf{n}\mathsf{n}^{\top},\qquad V_{t}=0,\qquad R_{t}=\frac{1}{4\chi}\,I,\qquad \hat{F}=0 . E t = − v v ⊤ , B t = − 2 1 v v ⊤ , Q t = 2 1 ψ v v ⊤ + μ n n ⊤ , V t = 0 , R t = 4 χ 1 I , F ^ = 0.
3. (The fluctuation LQG data.) (iii)
E t = − v v ⊤ = E t , B t = − 1 2 v v ⊤ = B t , E ~ t = q I + q 0 n n ⊤ , \mathcal{E}_{t}=-vv^{\top}=E_{t},\qquad \mathcal{B}_{t}=-\tfrac{1}{2}vv^{\top}=\mathsf{B}_{t},\qquad \tilde{\mathcal{E}}_{t}=q\,I+q_{0}\,\mathsf{n}\mathsf{n}^{\top}, E t = − v v ⊤ = E t , B t = − 2 1 v v ⊤ = B t , E ~ t = q I + q 0 n n ⊤ ,
Θ t ⋆ = v v ⊤ , Θ ~ t ⋆ = ( q 2 + q 0 ) I , F ⋆ = 0 , \Theta^{\star}_{t}=vv^{\top},\qquad \tilde{\Theta}^{\star}_{t}=\Bigl(\frac{q}{2}+q_{0}\Bigr)I,\qquad F^{\star}=0, Θ t ⋆ = v v ⊤ , Θ ~ t ⋆ = ( 2 q + q 0 ) I , F ⋆ = 0 ,
and the Hessian blocks are H t S S = 2 Q t H^{SS}_{t}=2Q_{t} H t SS = 2 Q t , H t S A = 0 H^{SA}_{t}=0 H t S A = 0 , H t A S = 0 H^{AS}_{t}=0 H t A S = 0 , H t A A = ( 2 χ ) − 1 I H^{AA}_{t}=(2\chi)^{-1}I H t AA = ( 2 χ ) − 1 I .
4. (Invertibility and the information matrix.) (iv) R t R_{t} R t and Θ ~ t ⋆ \tilde{\Theta}^{\star}_{t} Θ ~ t ⋆ are symmetric positive definite , hence invertible , with R t − 1 = 4 χ I R_{t}^{-1}=4\chi\,I R t − 1 = 4 χ I and ( Θ ~ t ⋆ ) − 1 = ( q 2 + q 0 ) − 1 I (\tilde{\Theta}^{\star}_{t})^{-1}=\bigl(\tfrac{q}{2}+q_{0}\bigr)^{-1}I ( Θ ~ t ⋆ ) − 1 = ( 2 q + q 0 ) − 1 I . Putting D ~ t = E ~ t ⊤ ( Θ ~ t ⋆ ) − 1 E ~ t \tilde{D}_{t}=\tilde{\mathcal{E}}_{t}^{\top}(\tilde{\Theta}^{\star}_{t})^{-1}\tilde{\mathcal{E}}_{t} D ~ t = E ~ t ⊤ ( Θ ~ t ⋆ ) − 1 E ~ t and
d ~ = q 2 q 2 + q 0 = 2 q 2 q + 2 q 0 > 0 , \tilde{d}=\frac{q^{2}}{\tfrac{q}{2}+q_{0}}=\frac{2q^{2}}{q+2q_{0}}>0 , d ~ = 2 q + q 0 q 2 = q + 2 q 0 2 q 2 > 0 ,
we have
D ~ t = 1 q 2 + q 0 ( q 2 I + 2 q 0 ( q + q 0 ) n n ⊤ ) , D ~ t v v ⊤ = d ~ v v ⊤ , v v ⊤ D ~ t = d ~ v v ⊤ . \tilde{D}_{t}=\frac{1}{\tfrac{q}{2}+q_{0}}\Bigl(q^{2}\,I+2q_{0}(q+q_{0})\,\mathsf{n}\mathsf{n}^{\top}\Bigr),\qquad \tilde{D}_{t}\,vv^{\top}=\tilde{d}\,vv^{\top},\qquad vv^{\top}\tilde{D}_{t}=\tilde{d}\,vv^{\top} . D ~ t = 2 q + q 0 1 ( q 2 I + 2 q 0 ( q + q 0 ) n n ⊤ ) , D ~ t v v ⊤ = d ~ v v ⊤ , v v ⊤ D ~ t = d ~ v v ⊤ .
5. (Hypothesis (JC) and hypothesis (H1).) (v) For every w = ( w 1 , w 2 , w 3 , w 4 ) ∈ R 4 w=(w^{1},w^{2},w^{3},w^{4})\in\mathbb{R}^{4} w = ( w 1 , w 2 , w 3 , w 4 ) ∈ R 4 ,
1 2 ∑ i = 1 4 ∑ j = 1 4 H i j ( t ) w i w j = ψ 2 ( w 1 − w 2 ) 2 + μ ( w 1 + w 2 ) 2 + 1 4 χ ( ( w 3 ) 2 + ( w 4 ) 2 ) ≥ c J ∣ w ∣ 2 , \frac{1}{2}\sum_{i=1}^{4}\sum_{j=1}^{4}H_{ij}(t)\,w^{i}w^{j}=\frac{\psi}{2}\bigl(w^{1}-w^{2}\bigr)^{2}+\mu\bigl(w^{1}+w^{2}\bigr)^{2}+\frac{1}{4\chi}\Bigl(\bigl(w^{3}\bigr)^{2}+\bigl(w^{4}\bigr)^{2}\Bigr)\ \ge\ c_{J}\,|w|^{2}, 2 1 i = 1 ∑ 4 j = 1 ∑ 4 H ij ( t ) w i w j = 2 ψ ( w 1 − w 2 ) 2 + μ ( w 1 + w 2 ) 2 + 4 χ 1 ( ( w 3 ) 2 + ( w 4 ) 2 ) ≥ c J ∣ w ∣ 2 ,
where
c J = min { ψ , 2 μ , 1 4 χ } > 0 , c_{J}=\min\Bigl\{\psi,\ 2\mu,\ \frac{1}{4\chi}\Bigr\}>0 , c J = min { ψ , 2 μ , 4 χ 1 } > 0 ,
and ∑ γ , δ F γ δ z γ z δ = 0 ≥ 0 \sum_{\gamma,\delta}F_{\gamma\delta}z^{\gamma}z^{\delta}=0\ge0 ∑ γ , δ F γ δ z γ z δ = 0 ≥ 0 for every z ∈ R 2 z\in\mathbb{R}^{2} z ∈ R 2 . Thus hypothesis (JC) of Localized Joint Coercivity of the Recentred N-Agent Cost Integrand under a Positive-Definite Fluctuation Hessian holds with the constant c J c_{J} c J , and hypothesis (H1) of Completion of Squares and A Priori Control Bound for the Fluctuation Cost holds with r = c J r=c_{J} r = c J , indeed with r = ( 4 χ ) − 1 r=(4\chi)^{-1} r = ( 4 χ ) − 1 .