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some methodology

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Daniel Rosel
2025-12-12 17:18:45 +01:00
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paper/src/chapters/03-methodology.tex
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@@ -1,6 +1,5 @@
\section{Methodology} \section{Methodology}
\subsection{Problem Formalization} \subsection{Problem Formalization}
Mathematical formalization of agent-induced pricing distortions. Formal definition of potential loss mechanisms $\alpha D$ Mathematical formalization of agent-induced pricing distortions. Formal definition of potential loss mechanisms $\alpha D$
@@ -16,46 +15,19 @@ Mathematical demonstration and validation of the COI and citation backed evidenc
\subsection{System Architecture} \subsection{System Architecture}
In order for our research to have grounding in interactions we built a robust e-commerce web-platform. We initially conducted a survey of the leading platforms of airlines and hotel booking sites to identify the specific interface patterns that effectively manage complex travel data. Our analysis revealed a clear industry standard: while both sectors rely on tabbed service selection and left-sidebar filtering to streamline navigation, they diverge in result presentation—airlines utilize visual date-price bars and multi-step wizards to optimize for logistical transparency, whereas hotel platforms leverage image-led cards and scarcity triggers to drive emotional engagement and urgency. Our web framework defines a highly agnostic boilerplane which can be seeded with any data-modality with an easy-to-tailor pattern, which we leverage to define a \texttt{hotel} and \texttt{airline} mode. Both modes are then individually deployed via an envrionment level argument which adjusts the proxy routing with a custom middleware inside next.js to render only the desired mode. The purpose of this was to create a baseline adaptable to any use-case or desired commercial application.
\subsection{Experimental Design}
\begin{figure}[ht] \begin{figure}[ht]
\resizebox{\columnwidth}{!}{% \resizebox{\columnwidth}{!}{%
\input{chapters/loop_figure.tex} \input{chapters/loop_figure.tex}
} }
\caption{Overview of the Dynamic Pricing Tasks.} \caption{Overview of the Dynamic Pricing Tasks.}
\end{figure} \end{figure}
\begin{figure}[ht]
\centering
\begin{tikzpicture}[
node distance=1.5cm and 2.5cm,
box/.style={rectangle, draw, thick, minimum height=1cm, minimum width=3cm, align=center, fill=blue!10},
kafka/.style={rectangle, draw=orange, thick, minimum height=1cm, minimum width=3cm, align=center, fill=orange!15},
arrow/.style={thick,->,>=Stealth}
]
% Nodes
\node[box] (webapp) {Web Application \\ (Producer \& Consumer)};
\node[kafka, below=of webapp] (kafka) {Apache Kafka \\ Cluster};
\node[box, below=of kafka] (backend) {Backend Services / Microservices \\ (Producers and Consumers)};
% Connections
\draw[arrow] (webapp) to[out=210,in=150] node[above]{Publish} (kafka);
\draw[arrow] (kafka) to[out=50,in=330] node[below]{Consume} (webapp);
\draw[arrow] (backend) -- node[above]{Publish/Consume} (kafka);
% Optional: Kafka internal components
%\node[below=0.7cm of kafka, align=center] (topics) {Topics \\ Partitions};
% Optional background
\begin{scope}[on background layer]
\node[draw, rounded corners, fill=orange!5, fit=(kafka), inner sep=0.3cm] {};
\end{scope}
\end{tikzpicture}
\caption{Technical Diagram}
\end{figure}
High level overview of how it works
\subsection{Experimental Design}
Study methodology and approach. Data acquisition strategy. Defined objectives and success criteria. Observable metrics and KPIs Study methodology and approach. Data acquisition strategy. Defined objectives and success criteria. Observable metrics and KPIs
\subsection{Dynamic Pricing Algorithm Analysis} \subsection{Dynamic Pricing Algorithm Analysis}