From 1cc590abedd454421a36089701dd81f23a4242ce Mon Sep 17 00:00:00 2001 From: Daniel Rosel Date: Fri, 12 Dec 2025 20:15:29 +0100 Subject: [PATCH] bit more of this and that --- paper/src/chapters/03-methodology.tex | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/paper/src/chapters/03-methodology.tex b/paper/src/chapters/03-methodology.tex index ad2f925..2912bad 100644 --- a/paper/src/chapters/03-methodology.tex +++ b/paper/src/chapters/03-methodology.tex @@ -15,9 +15,7 @@ Mathematical demonstration and validation of the COI and citation backed evidenc \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. - - +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} @@ -28,7 +26,9 @@ In order for our research to have grounding in interactions we built a robust e- } \caption{Overview of the Dynamic Pricing Tasks.} \end{figure} -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} Deep dive into how the algorithm works, different kinds and justification for chosen appraoches + agent impact modeling and quantification.