Proofread for seminar. Updated mathematic notation

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@ -217,7 +217,7 @@ JEL Classification Nos.: H4, Q2},
url = {https://er.jsc.nasa.gov/seh/ricetalk.htm}, url = {https://er.jsc.nasa.gov/seh/ricetalk.htm},
} }
@Article{Flux.jl-2018, @Article{Innes2018,
author = {Michael Innes and Elliot Saba and Keno Fischer and Dhairya Gandhi and Marco Concetto Rudilosso and Neethu Mariya Joy and Tejan Karmali and Avik Pal and Viral Shah}, author = {Michael Innes and Elliot Saba and Keno Fischer and Dhairya Gandhi and Marco Concetto Rudilosso and Neethu Mariya Joy and Tejan Karmali and Avik Pal and Viral Shah},
title = {Fashionable Modelling with Flux}, title = {Fashionable Modelling with Flux},
journal = {CoRR}, journal = {CoRR},

@ -73,20 +73,18 @@ and remaining policy questions.
\section{References} \section{References}
\printbibliography \printbibliography
\newpage \newpage
\section{Appedicies} %TODO: write appendicies
\subsection{Mathematical Notation} %\section{Appedicies}
Needs completed. %\subsection{Mathematical Notation}
%Needs completed.
%\subsection{Deriving Marginal Survival Rates}\label{APX:Derivations:SurvivalRates} %\subsection{Deriving Marginal Survival Rates}\label{APX:Derivations:SurvivalRates}
%\subfile{sections/apx_01_MarginalSurvivalRates} %\subfile{sections/apx_01_MarginalSurvivalRates}
\subsection{Deriving Euler Equations}\label{APX:Derivations:EulerEquations}
\subfile{sections/apx_02_GeneralizedEuEqSteps}
%\subsection{Collected Assumptions and Caveats}\label{APX:CollectedAssumptions}
\subsection{Collected Assumptions and Caveats}\label{APX:CollectedAssumptions} %I hope to write a section clearly explaining assumptions, caveats, and shortcomings here.
I hope to write a section clearly explaining assumptions, caveats, and shortcomings here. %These will later get written back into the other sections, but I want to collect them
These will later get written back into the other sections, but I want to collect them %in a single place first.
in a single place first.
%time periods are long enough for debris to disperse after collisions. %time periods are long enough for debris to disperse after collisions.
%Only a single type of debris %Only a single type of debris
%With my current computational idea; each constellation provides the same risk to each other constellation %With my current computational idea; each constellation provides the same risk to each other constellation

@ -117,7 +117,7 @@ Specifically, I permit:
\begin{itemize} \begin{itemize}
\item Heterogeneous agent types including commercial, scientific, and military. \item Heterogeneous agent types including commercial, scientific, and military.
\item Asymetric constellations. \item Asymetric constellations.
\item Inter- and intra- constellation risk is not assumed to be equal. \item Inter- and intra- constellation risk to differ.
\end{itemize} \end{itemize}
each of which are important qualities of the current orbital environment. each of which are important qualities of the current orbital environment.
None of these aspects are considered in the papers that I have reviewed so far. None of these aspects are considered in the papers that I have reviewed so far.

@ -18,8 +18,8 @@ subscripts $s_t$ denote time periods.
in period $t$ in period $t$
\item $D_t$ represents the level of debris at period $t$. \item $D_t$ represents the level of debris at period $t$.
\end{itemize} \end{itemize}
I've used curly braces (i.e. $\{ s^j_t \}$) to represent the set I've used the capital letters $S_t$ and $X_t$ to represent the set (vector)
of constellations' stocks. of constellations' stocks and policy decisions respectively.
\subsubsection{Satellite Stocks} \subsubsection{Satellite Stocks}
Each constellation consists of a number of satellites in orbit, controlled by the same operator and Each constellation consists of a number of satellites in orbit, controlled by the same operator and
@ -29,14 +29,15 @@ Of course, satellite stocks can be increased by launching more satellites.
Assuming satellites are not actively deorbited, we get the Assuming satellites are not actively deorbited, we get the
following general law of motion for each constellation $i$. following general law of motion for each constellation $i$.
\begin{align} \begin{align}
s^i_{t+1} = \left( 1 - l^i(\{s^j_t\}, D_t)\right)s^i_t + x^i_t s^i_{t+1} = \left( R^i(S_t, D_t)\right)s^i_t + x^i_t
%Couple of Notes: %Couple of Notes:
% This does not allow for natural decay of satellites. % This does not allow for natural decay of satellites.
% Nor does it include a deorbit decision. % Nor does it include a deorbit decision.
% Representing those might be: % Representing those might be:
% - \eta s^i_t - y^i_t % - \eta s^i_t - y^i_t
\end{align} \end{align}
Where $l^i(\cdot)$ represents the rate at which satellites are destroyed by collisions. Where $R^i(\cdot)$ represents the constellation $i$'s survival rate, making
$1-R^1()$ the rate at which they are destroyed or damaged by collisions.
%Assumption: %Assumption:
\subsubsection{Collision Efficiencies} \subsubsection{Collision Efficiencies}
@ -54,8 +55,8 @@ are operated for different purposes and require different orbital properties.
%This could be explained as Coordination across time (time travel doesn't exist yet) %This could be explained as Coordination across time (time travel doesn't exist yet)
This coordination is also complicated by the fact that constellations are not This coordination is also complicated by the fact that constellations are not
designed nor launched at the same time. designed nor launched at the same time.
Consequently an operator may choos to minimize their total risk when launching Consequently, while an operator may choose to minimize their total risk when launching
a constellation, the later launch of constellations may lead to a suboptimal orbit design. a constellation, the launch of later constellations may lead to a suboptimal orbit design.
It is important to note that satellite-on-satellite collisions are rare\footnote{ It is important to note that satellite-on-satellite collisions are rare\footnote{
I am only aware of one collision between satellites, I am only aware of one collision between satellites,
and one of them was abandoned at the time.\cref{ListOfOrbitalIncidents} and one of them was abandoned at the time.\cref{ListOfOrbitalIncidents}
@ -63,18 +64,14 @@ It is important to note that satellite-on-satellite collisions are rare\footnote
but this may be due to the fact that evasive maneuvers are usually taken but this may be due to the fact that evasive maneuvers are usually taken
when collisions appear reasonably possible. when collisions appear reasonably possible.
These intra-collision efficiencies can be represented in the satellite survival rate $R^i(\cdot)$ as:
These collision efficiencies can be represented in the satellite destruction rate $l^i(\cdot)$ when:
\begin{align} \begin{align}
\parder{l^i}{s^k_t}{} > 0 ~~\forall k \in \{1,\dots,N)\\ \parder{R^i}{s^k_t}{} < 0 ~~\forall k \in \{1,\dots,N)\\
\parder{l^i}{s^j_t}{} > \parder{l^i}{s^i_t}{} ~~\forall j\neq i \parder{R^i}{s^j_t}{} < \parder{R^i}{s^i_t}{} ~~\forall j\neq i
\end{align} \end{align}
Note that an additional satellite in any constellation increases the probability of loosing
a satellite from a given constellation, and this risk is lower
for the home constellation of the additional satellite.
Note that it is reasonable to assume that the loss of satellites to collisions should be Note that we assume that the loss of satellites to collisions is
increasing in the level of debris: $\parder{l^i}{D_t}{} >0$. increasing in the level of debris: $\parder{R^i}{D_t}{} < 0$.
\subsubsection{Debris} \subsubsection{Debris}
Debris is generated by various processes, including: Debris is generated by various processes, including:
@ -83,29 +80,28 @@ Debris is generated by various processes, including:
\item Satellite launches, operations, failures, or intentional destruction. \item Satellite launches, operations, failures, or intentional destruction.
\item Collisions between \item Collisions between
\begin{itemize} \begin{itemize}
\item Two satellites \item Two satellites.
\item A satellite and debris \item A satellite and debris.
\item Two pieces of debris \item Two pieces of debris.
\end{itemize} \end{itemize}
all generate more debris.
\end{itemize} \end{itemize}
It leaves orbit when atmospheric drag slows it down enough to reenter the atmosphere. Debris leaves orbit when atmospheric drag slows it down enough to reenter the atmosphere.
Because the atmosphere is negligible for many orbits, reentry can easily take decades Because the atmosphere is negligible for many orbits, reentry can easily take decades
or centuries. or centuries.
These effects can be represented by the following general law of motion. These effects can be represented by the following general law of motion.
\begin{align} \begin{align}
D_{t+1} = (1-\delta)D_t + g(D_t) + \gamma(\{s^j_t\},D_t) + \Gamma(\{x^j_t\}) D_{t+1} = (1-\delta)D_t + g(D_t) + \gamma(S_t,D_t) + \Gamma(X_t)
\end{align} \end{align}
For simplicity, I formulate this more specifically as: For simplicity, I formulate this more specifically as:
\begin{align} \begin{align}
D_{t+1} = (1-\delta)D_t + g(D_t) D_{t+1} = (1-\delta + g)D_t
+ \sum^N_{i=1} \gamma l^i(\{s^j_t\},D_t) + \gamma \sum^N_{i=1} (1-R^i(S_t,D_t)) \cdot s^i_t
+ \Gamma \sum^n_{j=1} \{x^j_t\} + \Gamma \sum^n_{j=1} x^i_t
\end{align} \end{align}
where $ \Gamma, \gamma$ represent the debris generated by each where $ \Gamma, \gamma$ represent the debris generated by each
launch and collision respectively, launch and collision respectively.
while $\delta,g(\cdot)$ represent the decay rate of debris and the Similarly $\delta$ and $g$ represent the decay rate of debris and the
autocatalysis\footnote{ autocatalysis\footnote{
Using terminology from \cite(RaoRondina2020). Using terminology from \cite(RaoRondina2020).
} of debris generation. } of debris generation.

@ -10,7 +10,7 @@ A few methods have been used to model this behavior in the economics literature.
The first one I want to explain was developed by \cite{Adilov2018}. The first one I want to explain was developed by \cite{Adilov2018}.
They characterize kessler syndrome as the point in time at which an orbit is They characterize kessler syndrome as the point in time at which an orbit is
unusable as each satellite in orbit will be destroyed within a single time period. unusable as each satellite in orbit will be destroyed within a single time period.
In my notation, this is that $l^i(\{s^j_t\}, D_t) = 1$. In my notation, this is that $R^i(S_t, D_t) = 0 ~ \forall i$.
The benefit of this approach is that it is algebraically simple. The benefit of this approach is that it is algebraically simple.
It was used in to show that firms will stop launching before It was used in to show that firms will stop launching before
orbits are rendered physically useless. orbits are rendered physically useless.
@ -22,11 +22,12 @@ They define it in terms of a ``kessler region'', the set of satellite stocks and
such that the limit of debris in the future is infinite. such that the limit of debris in the future is infinite.
Mathematically this can be represented as: Mathematically this can be represented as:
\begin{align} \begin{align}
\kappa = \left\{ \{s^j_t\}, D_t : \kappa = \left\{ S_t, D_t :
\lim_{k\rightarrow \infty} D_{t+k}\left(\{s^j_{t+k-1}\}, D_{t+k-1}, \{x^j\}\right) = \infty \right\} \lim_{k\rightarrow \infty} D_{t+k}\left(S_{t+k-1}, D_{t+k-1}, X_t\right)
= \infty \right\}
\end{align} \end{align}
There are a few issues with this approach, even though it captures the essence of kessler syndrome There are a few issues with this approach, even though it captures the essence of kessler syndrome
better than the definition proposed by Adilov et al. better than the definition proposed by \cite{Adilov2018}.
The issues it faces are generally the case of not delineating between kessler regions The issues it faces are generally the case of not delineating between kessler regions
with significantly different economic outcomes. with significantly different economic outcomes.
% doesn't account for speed of divergence % doesn't account for speed of divergence
@ -38,7 +39,7 @@ The former is a global emergency, while the latter is effectively non-existant.
The last disadvantage I'd like to mention is that determining whether a The last disadvantage I'd like to mention is that determining whether a
series is divergent depends on constructing mathematical proofs. series is divergent depends on constructing mathematical proofs.
This makes it difficult to computationally identify whether a given state This makes it difficult to computationally identify whether a given state
constitutes as kessler syndrome. constitutes is in the kessler region.
@ -49,13 +50,15 @@ fashions than \cite{RaoRondina2020}, for which I term the regions
First, define the $\epsilon$-kessler region as: First, define the $\epsilon$-kessler region as:
\begin{align} \begin{align}
\kappa = \left\{ \{s^j_t\}, D_t : \kappa_\epsilon = \left\{ S_t, D_t :
\forall k \geq 0, D_{t+k+1} - D_{t+k} \geq \epsilon > 0 \right\} \forall k \geq 0, D_{t+k+1} - D_{t+k} \geq \epsilon > 0 \right\}
\end{align} \end{align}
%show that this is similar to saying that all non \epsilon kessler regions are bounded by the %show that this is similar to saying that all non \epsilon kessler regions are bounded by the
%derivative, i.e. are lipshiz %derivative, i.e. are lipshiz
The continuous time equivalent of this condition is defining the non-kessler regions by The continuous time equivalent of this condition is defining the non-kessler regions by
an upper bound on the derivative of debris generation\footnote{A lipshitz-like condition}. an upper bound on the derivative of debris generation\footnote{
Note that the non-proto-kessler region is defined by a lipshitz-like condition
}.
It is easily shown that this criteria is sufficient to guarantee Rao and Rondina's criteria. It is easily shown that this criteria is sufficient to guarantee Rao and Rondina's criteria.
@ -84,45 +87,45 @@ of the kessler region would capture this behavior, but the $\epsilon$-kessler de
would not. would not.
A particularly pathological case is where debris cycles between just below the cutoff level to A particularly pathological case is where debris cycles between just below the cutoff level to
significantly above the cutoff, leading to a highly divergent behavior not captured by this definition. significantly above the cutoff, leading to a highly divergent behavior not captured by this definition.
Also, by simulating a phase diagram (for a given solution to the model)
As far as computability goes, by simulating a phase diagram (for a given solution to the model)
we can determine what sections are in the $\epsilon$-kessler region. we can determine what sections are in the $\epsilon$-kessler region.
This is a major benefit in a computational model.
A related and more general concept is the ``proto-kesslerian'' region, which is A related and more general concept is the ``proto-kesslerian'' region, which is
defined as the stock and debris levels such that: defined as the stock and debris levels such that:
\begin{align} \begin{align}
\kappa = \left\{ \{s^j_t\}, D_t : \kappa_\text{proto} = \left\{ S_t, D_t :
D_{t+1} - D_{t} \geq \varepsilon > 0 \right\} D_{t+1} - D_{t} \geq \varepsilon > 0 \right\}
\end{align} \end{align}
%Note that the debris level is in a $\epsilon$-kessler region when it is in a proto-kesslerian region %Note that the debris level is in a $\epsilon$-kessler region when it is in a proto-kesslerian region
%for all future periods. %for all future periods.
This even simpler to compute than the phase diagram, and can be used to generate a topological view This even simpler to compute than the phase diagram, and can be used to generate a topological view
of proto-kesslerian regions of degre $\varepsilon$. of various proto-kesslerian regions.
%These are both easier to interpret and various approaches could be used to analyze how debris levels %These are both easier to interpret and various approaches could be used to analyze how debris levels
%transition between them. %transition between them.
%%%what would the integral of gradients weighted by the dividing line measure? just a thought. %%%what would the integral of gradients weighted by the dividing line measure? just a thought.
%Other thoughts %Other thoughts
% proto-kesslerian paths, paths that pass into a proto kesslerian region. % proto-kesslerian paths, paths that pass into a proto kesslerian region.
In order to capture the cyclic behavior that $\epsilon$-kessler regions miss, we can define a type of In order to capture the cyclic behavior that $\epsilon$-kessler regions miss,
path in the phase diagram called a proto-kesslerian path of degree $\epsilon$, which is any path we can define a type of
path in the phase diagram (called a proto-kessler path of degree $\epsilon$), which is any path
that enters the region. that enters the region.
For example, one could simulate a phase diagram and compare paths that fall into a given $\epsilon$-kessler region For example, one could simulate a phase diagram and compare paths that fall into a given $\epsilon$-kessler region
and paths that only temporarily pass into the equivalent proto-kesslerian regions. and paths that only temporarily pass into the equivalent proto-kesslerian regions.
Comparing the number of paths that fall into each region may give a useful metric for policies that are Comparing the number of paths that fall into each region may give a useful metric
for policies that are
designed to decrease the likelihood of kessler syndrome. designed to decrease the likelihood of kessler syndrome.
I believe, but have not verified, that some choices of $\varepsilon$, although permitting cycles, %I believe, but have not verified, that some choices of $\varepsilon$, although permitting cycles,
would relegate them to levels with minimal economic impact. %would relegate them to levels with minimal economic impact.
%Maybe can be studies by phase or flow diagrams?
%Consider where it cycles between just below epsilon and then to a large increase in debris?
%Area of research: What makes a good \epsilon? %Area of research: What makes a good \epsilon?
This leads to the important question of ``What makes a good value of $\epsilon$ or $\varepsilon$?'' This leads to the important question of ``What makes a good value of $\epsilon$ or $\varepsilon$?''
One method, in the spirit of \cite{Adilov2018}, is to choose a change in debris, $D_{t+1} - D_t$, such that One method, in the spirit of \cite{Adilov2018},
the loss of satellites in periods $t+1$ to $t+k$ is increased by or to a certain percentage, say 1\%. is to choose a change in debris, $D_{t+1} - D_t$,
such that the loss of satellites between periods $t$ to $t+k$ is
increased by or to a certain percentage, say 1\%.
I've put very little thought into addressing this general question so far, I've put very little thought into addressing this general question so far,
and need to analyze the implications of different choice rules. and need to analyze the implications of different choice rules.

@ -9,7 +9,7 @@ Actual functional specifications are described in \cref{SEC:Computation} on comp
Each operator recieve per-period benefits Each operator recieve per-period benefits
-- such as profits for firms and warfighting capability for militaries -- -- such as profits for firms and warfighting capability for militaries --
from their constellation from their constellation
according to $u^i(\{s^j_t\},D_t)$, which depends according to $u^i(S_t,D_t)$, which depends
on the current sizes of constellations and the level of debris. on the current sizes of constellations and the level of debris.
In addition, the operator pays for the launch of $x^i_t$ satellites In addition, the operator pays for the launch of $x^i_t$ satellites
according to a general cost function $F(x)$. according to a general cost function $F(x)$.
@ -17,12 +17,12 @@ These satellites will become operational in the subsequent period.
Thus the $M$-period (possibly infinite), problem is: Thus the $M$-period (possibly infinite), problem is:
\begin{align} \begin{align}
\max_{\{\vec x_t\}^M}&~ \max_{\{x_t^i\}^M}&~
E\left[ \sum^M_{t=0} \beta^t u^i(\vec s_t, D_t) - F(x^i_t) \right] \\ \left[ \sum^M_{t=0} \beta^t u^i(S_t, D_t) - F(x^i_t) \right] \\
&\text{subject to:}\\ &\text{subject to:}\\
& s^i_{t+1} = (1-l^i(\vec s_t, D_t))s^i_t +x^i_t ~~~ \forall i \\ & s^j_{t+1} = R^j(S_t, D_t) s^j_t + x^j_t ~~~ \forall j \\
& D_{t+1} = (1-\delta)D_t + g(D_t) & D_{t+1} = (1-\delta + g) D_t
+ \gamma \sum^N_{i=1} l^i(\vec s_t, D_t) + \gamma \sum^N_{i=1} \left( 1-R^i(S_t, D_t) \right) s^i_t
+ \Gamma \sum^N_{i=1} x^i_t + \Gamma \sum^N_{i=1} x^i_t
\end{align} \end{align}
%Assumptions %Assumptions
@ -33,8 +33,8 @@ Thus the $M$-period (possibly infinite), problem is:
%\subsection{Infinite Period (Bellman) Equation} % Not sure how much help a new header is. %\subsection{Infinite Period (Bellman) Equation} % Not sure how much help a new header is.
The inifinite period version of the problem above can be rewritten in the bellman form as The inifinite period version of the problem above can be rewritten in the bellman form as
\begin{align} \begin{align}
V^i(\vec s_t, \vec x^{\sim i}_t, D_t) = \max_{x^i_t} u^i(\vec s_t, D_t) -F(x) V^i(S_t, x^{\sim i}_t, D_t) = \max_{x^i_t} u^i(S_t, D_t) -F(x^i_t)
+ \beta \left[ V^i(\vec s_{t+1}, \vec x^{\sim i}_{t+1}, D_{t+1}) \right] + \beta \left[ V^i(S_{t+1}, x^{\sim i}_{t+1}, D_{t+1}) \right]
\end{align} \end{align}
where $x^{\sim i}_t$ represents the launch decisions of all the other constellation where $x^{\sim i}_t$ represents the launch decisions of all the other constellation
operators. operators.

@ -5,12 +5,12 @@
The Social (Fleet) Planner's problem can be written in the bellman form as: The Social (Fleet) Planner's problem can be written in the bellman form as:
\begin{align} \begin{align}
W(S_t, D_t) =& \max_{X_t} \left[ W(S_t, D_t) =& \max_{X_t} \left[
\left(\sum^N_{i=1} u^i(\vec s_t, D_t) - F(x^i_t) \right) \left(\sum^N_{i=1} u^i(S_t, D_t) - F(x^i_t) \right)
+ \beta \left[ W(\vec s_{t+1}, D_{t+1}) \right]\right] \notag \\ + \beta \left[ W(S_{t+1}, D_{t+1}) \right]\right] \notag \\
&\text{subject to:} \notag \\ &\text{subject to:} \notag \\
& s^i_{t+1} = (1-l^i(\vec s_t, D_t))s^i_t +x^i_t ~~~ \forall i \notag \\ & s^i_{t+1} = (R^i(S_t, D_t)) s^i_t +x^i_t ~~~ \forall i \notag \\
& D_{t+1} = (1-\delta)D_t + g(D_t) & D_{t+1} = (1-\delta + g)D_t
+ \gamma \sum^N_{i=1} l^i(\vec s_t, D_t) + \gamma \sum^N_{i=1} \left(1-R^i(\vec s_t, D_t)\right) s^i_t
+ \Gamma \sum^N_{i=1} x^i_t + \Gamma \sum^N_{i=1} x^i_t
\end{align} \end{align}
%Some particular features of the model include: %Some particular features of the model include:
@ -21,8 +21,8 @@ The Social (Fleet) Planner's problem can be written in the bellman form as:
% including uncontrolled deorbits. % including uncontrolled deorbits.
Although the social planner controls each constellation, note that they do not reap additional Although the social planner controls each constellation, note that they do not reap additional
collision avoidance efficiencies. collision avoidance efficiencies.
One justification no social planner could concieve of every future use of an orbit One justification is that no social planner could concieve of every future use of an orbit
and consequentally constellations may be designed sequentially. and consequentally constellations will be designed sequentially.
This prevents intra-constellation benefits to be achieved across the entire fleet. This prevents intra-constellation benefits to be achieved across the entire fleet.
\end{document} \end{document}

@ -6,7 +6,9 @@
With the definitions of kessler syndrome and the law of debris given above, we can now With the definitions of kessler syndrome and the law of debris given above, we can now
explicitly describe the proto-kessler region. explicitly describe the proto-kessler region.
\begin{align} \begin{align}
\epsilon < -\delta D_t + g(D_t) + \gamma \sum^n_{j=1} l^i(\{s^j_t\},D_t) + \Gamma \sum^n_{j=1} \{x^j_t\} \epsilon < (g - \delta) D_t
+ \gamma \sum^n_{j=1} \left( 1-R^i(S_t,D_t) \right) s^i_t
+ \Gamma \sum^n_{j=1} x^j_t\}
\end{align} \end{align}
As being in the proto-kessler region is a prerequesit to being in the kessler region, we see that As being in the proto-kessler region is a prerequesit to being in the kessler region, we see that
the kessler region depends on the collision rates of the constellation operators. the kessler region depends on the collision rates of the constellation operators.

@ -25,7 +25,7 @@ where $v$ is an external weighting parameter which can be cross validated.
By choosing a neural network as the functional approximation, we are able to 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 use the fact that a NN with a single hidden layer can be used to approximate
functions arbitrarily well functions arbitrarily well
under certain conditions \cref{White1990}. under certain conditions \autocite{White1990}.
We can also We can also
take advantage of the significant computational and practical improvements take advantage of the significant computational and practical improvements
currently revolutionizing Machine Learning. currently revolutionizing Machine Learning.
@ -33,14 +33,14 @@ Some examples include the use of specialized hardware and the ability to transfe
learning between models, both of which can speed up functional approximation. learning between models, both of which can speed up functional approximation.
\subsection{Computational Plan} \subsection{Computational Plan}
The neural network library I've chosen to use is Flux.jl \cref{Flux.jl-2018}, The neural network library I've chosen to use is Flux.jl \cite{Innes2018}
a Neural Network library implmented in and for the Julia language, a Neural Network library implmented in and for the Julia language,
although the Bellman Residual Minimization algorithm would work equally well in although the Bellman Residual Minimization algorithm would work equally well in
PyTorch or TensorFlow PyTorch or TensorFlow
\footnote{ \footnote{
The initial reason I investigated Flux/Julia is due to the source to source The initial reason I investigated Flux/Julia is due to the source to source
Automatic Differentiation capabilities, which I intended to use to implement Automatic Differentiation capabilities, which I intended to use to implement
a generic version of \cref{Maliar2019}'s euler equation iteration method. a generic version of \cite{Maliar2019}'s euler equation iteration method.
While I still believe this is possible and that Flux represents one of the While I still believe this is possible and that Flux represents one of the
best tools available for that specific purpose, best tools available for that specific purpose,
I've been unsuccessful at implementing the algorithm. I've been unsuccessful at implementing the algorithm.
@ -137,7 +137,7 @@ including mixed nash equilibria if configured properly.
\subsection{Functional Forms} \subsection{Functional Forms}
The reference functional forms for the model are similar to those The reference functional forms for the model are similar to those
given in \autocite{RaoRondina2020}. given in \cite{RaoRondina2020}.
\begin{itemize} \begin{itemize}
\item The linear per-period benefit function: \item The linear per-period benefit function:
\begin{align} \begin{align}

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