finished fixing references

CurrentWriting/assets/preambles/References.bib

@@ -686,4 +686,21 @@ JEL Classification Nos.: H4, Q2},
   primaryclass  = {astro-ph.EP},
 }
 
+@Article{Maliar2019,
+  author  = {Lilia Maliar and Serguei Maliar and Pablo Winant},
+  title   = {Will Artificial Intelligence Replace Computational Economists Any Time Soon?},
+  journal = {Center for Economic Policy Research: Monetary Economics and Fluctuations},
+  year    = {2019},
+}
+
+@Article{White1990,
+  author  = {Kurt Hornik and Maxwell Stinchcombe and Halbert White},
+  title   = {Universal Approximation of an Unknown Mapping and its Derivatives using Multilayer Feedforward Networks},
+  journal = {Neural Networks},
+  year    = {1990},
+  volume  = {3},
+  pages   = {551-560},
+  month   = jan,
+}
+
 @Comment{jabref-meta: databaseType:bibtex;}

CurrentWriting/sections/04_ConstellationOperator.tex

@@ -2,7 +2,7 @@
 \graphicspath{{\subfix{Assets/img/}}}
 
 \begin{document}
-With the laws of motion introduced in sections \cref{asdf}, we can now describe
+With the laws of motion introduced in sections \cref{SEC:Laws}, we can now describe
 the optimization problem facing each constellation operator.
 
 Each operator recieve utility in each period per 
@@ -44,8 +44,9 @@ for the existence of an euler equation.
 
 
 \subsubsection{Euler Equation}
-Appendix \cref{Appendix} contains more details on the math involved. 
-What follows is just a sketch of the applied method in matrix notation.
+Appendix \cref{APX:Derivations:EulerEquations} contains more details 
+on the math involved. 
+What follows is just a sketch of the method in matrix notation.
 
 As there is only one choice variable, we get a single optimality condition. 
 It can be written in various formats, with the latter matching the appendix the best.
@@ -68,7 +69,8 @@ It can be written in various formats, with the latter matching the appendix the
 
 As there are $N$ constellations we get $N$ satellite stocks, 
 $N-1$ decisions $x^{\sim i}$,
-and $1$ debris state for a total of $2N$ state variables\footnote{recall that $N$ is the number of constellations.}.
+and $1$ debris state for a total of $2N$ state 
+variables\footnote{recall that $N$ is the number of constellations.}.
 Thus there are $2N$ envelope conditions to be found:
 \begin{align}
 %    \nabla_{\vec s_t, \vec x^{\sim i}_t, D_t} V^i(\vec s_t, \vec x^{\sim i}_t, D_t) 
@@ -126,7 +128,7 @@ To solve this symbolicly involves inverting a $6 \times 6$ matrix.
 As matrix inversion has approximately an $O(n^3)$ computational complexity,
 this becomes unsustainable very quickly.
 
-Section \cref{SectionOnComputational} describes how to address this issue to generate
+Section \cref{SEC:Computation} describes how to address this issue to generate
 these euler equations using features of modern programming languages and linear algebra
 libraries.
 

CurrentWriting/sections/05_SocialPlanner.tex

@@ -28,7 +28,8 @@ Some particular features of the model include:
 \end{itemize}
 
 \subsubsection{Euler Equation}
-In accordance with Appendix \cref{Appendix}, find the $N$ optimality conditions:
+In accordance with Appendix \cref{APX:Derivations:EulerEquations}, 
+we find the $N$ optimality conditions:
 \begin{align}
     0 =& -\der{F(x^i_t)}{x^i_t}{}  
         + \beta \left[

CurrentWriting/sections/07_ComputationalApproach.tex

@@ -3,7 +3,7 @@
 
 \begin{document}
 The computational approach I have decided to take is an application of 
-\cite{MALIAR2018}, where the policy function is approximated using a
+\cite{Maliar2019}, where the policy function is approximated using a
 neural network.
 
 The approach uses the fact that the euler equation implicitly defines the 
@@ -17,7 +17,7 @@ allowing one to find $x(\dot)$ as the solution to a minimization problem.
 By choosing a neural network as the functional approximation, we are able to 
 use the fact that a NN with a single hidden layer can be used to approximate
 functions arbitrarily well 
-under certain conditions\footnote{FIND REFERENCE, SEE MALIAR}.
+under certain conditions \cref{White1990}.
 We can also
 take advantage of the significant computational and practical improvements
 currently revolutionizing Machine Learning.
@@ -74,9 +74,8 @@ and laws of motion functions, retuning a $k$-period transition function.
 The second step is to generate functions that represent the optimality conditions.
 By taking the appropriate derivatives with respect to the laws of motion and
 utility functions, this can be constructed explicitly.
-
 Once these two functions are completed, they can be combined to create
-the euler equations, as described in appendix \ref{appx??}.
+the euler equations, as described in appendix \ref{APX:Derivations:EulerEquations}.
 
 %%% Is it FaFCCs or recursion that allows this to occur?
 %%% I believe both are ways to approach the problem.
@@ -107,7 +106,7 @@ selecting from that distribution.
 
 One key question is how to handle the case of heterogeneous agents.
 When the laws of motion depend on other agents' decisions, as is the case 
-described in \ref{lawsOFMotion}, intertemporal iteration may
+described in \ref{SEC:Laws}, intertemporal iteration may
 require knowing the other agents best response function.
 I believe I can model this in the constellation operator's case
 by solving for the policy functions of each class of operator