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Author SHA1 Message Date
copilot-swe-agent[bot]
0aed8e7311 Add EXPOSE directives to all Dockerfiles with port documentation
Co-authored-by: velocitatem <60182044+velocitatem@users.noreply.github.com>
2025-11-12 14:25:37 +00:00
copilot-swe-agent[bot]
90ba7588cc Refactor services into individual Dockerfiles
Co-authored-by: velocitatem <60182044+velocitatem@users.noreply.github.com>
2025-11-12 14:19:07 +00:00
copilot-swe-agent[bot]
df3082cff5 Initial plan 2025-11-12 14:15:32 +00:00
6b7060450c updated outline for paper from issue 2025-11-07 14:46:02 +01:00
Daniel Alves Rösel
f6e780fdf1 13 create outline for research paper draft (#18)
* updated outline for paper from issue

* extra paper sections and some formalization of series data

* algorithms and acknowledgements
2025-11-07 14:39:59 +01:00
Daniel Alves Rösel
f427943ebc Merge pull request #16 from velocitatem/15-define-the-color-and-style-scheme-for-the-hotel-and-airline-platforms
15 define the color and style scheme for the hotel and airline platforms
2025-11-06 14:18:17 +01:00
15 changed files with 176 additions and 9 deletions

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@@ -1,7 +1,9 @@
services:
redis:
container_name: "PHANTOM-redis"
image: redis:7-alpine
build:
context: ./docker
dockerfile: Redis.dockerfile
ports:
- "${REDIS_PORT:-6378}:6379"
volumes:
@@ -9,7 +11,9 @@ services:
restart: unless-stopped
zookeeper:
container_name: "PHANTOM-zookeeper"
image: confluentinc/cp-zookeeper:latest
build:
context: ./docker
dockerfile: Zookeeper.dockerfile
environment:
ZOOKEEPER_CLIENT_PORT: 2181
ports:
@@ -17,7 +21,9 @@ services:
kafka:
container_name: "PHANTOM-kafka"
image: confluentinc/cp-kafka:7.5.0
build:
context: ./docker
dockerfile: Kafka.dockerfile
depends_on:
- zookeeper
environment:
@@ -36,7 +42,9 @@ services:
redpanda-console:
container_name: "PHANTOM-redpanda-console"
image: docker.redpanda.com/redpandadata/console:latest
build:
context: ./docker
dockerfile: RedpandaConsole.dockerfile
depends_on:
- kafka
environment:

7
docker/Kafka.dockerfile Normal file
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@@ -0,0 +1,7 @@
FROM confluentinc/cp-kafka:7.5.0
# Expose Kafka ports
# 9092: External client connections
# 29092: Internal broker communication
# 9999: JMX monitoring port
EXPOSE 9092 29092 9999

4
docker/Redis.dockerfile Normal file
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@@ -0,0 +1,4 @@
FROM redis:7-alpine
# Expose Redis port
EXPOSE 6379

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@@ -0,0 +1,4 @@
FROM docker.redpanda.com/redpandadata/console:latest
# Expose Redpanda Console web UI port
EXPOSE 8080

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@@ -0,0 +1,4 @@
FROM confluentinc/cp-zookeeper:latest
# Expose Zookeeper client port
EXPOSE 2181

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@@ -6,14 +6,19 @@
(setq TeX-command-extra-options
"-file-line-error -interaction=nonstopmode")
(TeX-add-to-alist 'LaTeX-provided-class-options
'(("report" "12pt") ("article" "12pt") ("acmart" "sigconf" "nonacm")))
'(("report" "12pt") ("article" "12pt") ("acmart" "sigconf" "nonacm" "natbib=false")))
(TeX-run-style-hooks
"latex2e"
"preamble"
"chapters/01-intro"
"chapters/02-literature-review"
"chapters/03-methodology"
"chapters/04-results"
"chapters/05-discussion"
"chapters/06-conclusion"
"../build/concatenated_code"
"acmart"
"acmart10")
(TeX-add-symbols
'("footnotetextcopyrightpermission" 1)))
:latex)

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@@ -6,5 +6,11 @@
%% \label{fig:example}
%% \end{figure}
\section{Know They Enemy}
To know how to overcome we need to
\section{Introduction}
Research Objectives and Contribution: What are we making, why and who should care?
\subsection{Motivation and Market Context}
Current market dynamics and trends of dynamic pricing and AI agents. Future projections of AI agents. Key stakeholders that are discussing this and reporting on it (Thales). Who is most affected
\subsection{Solution Space Overview}
Different approaches and perspectives, here also add a preview of what will be developed and explored in the lit review.

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@@ -0,0 +1,17 @@
\section{Literature Review}
\subsection{Foundational Concepts}
What is the taxonomy and definition of an agent and an actor in this case, a bit more about interaction models in sessions and about dynamic pricing algorithms.
\subsection{Problem Evidence and Market Impact}
Documented instances of agent-driven market disruptions - Quantitative evidence of pricing manipulation - Case studies from affected industries
\subsection{Theoretical Foundations: Economic Prallels}
Economic foundations: relating the problem to options pricing theory. Cost of Information (COI) concept and its relevance
\subsection{Landscape of Existing Work}
Previous efforts in adversarial computer use LLM agents, show how multi-faceted the whole problem is
Here we can show a market visualization (venn-like-diagram)

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@@ -0,0 +1,68 @@
\section{Methodology}
\subsection{Problem Formalization}
Mathematical formalization of agent-induced pricing distortions. Formal definition of potential loss mechanisms $\alpha D$
We consider a business across time during which we have an evolving vector $p_t \in \Re^N$ where $N$ is the number of products in our catalogue. our price vector is directly dependent on a demand function $q_t$ which we define as a linear method of a price elasticity matrix $B_t$. This is the same setup that Microsoft created in their research.
We gether interaction data from users interacting with a sample platform simulating a hotel/airline which generates interaction distributions $I_t = \{(p_t, q_t^\text{obs}, \pi_t)\}_{t=1}^T$
\subsection{Cost of Information Framework}
Mathematical demonstration and validation of the COI and citation backed evidence, and framework overview + show harm to user via other cost distortions. Maybe split into 3.2.1 (COI Theory) and 3.2.2 (Framework Design)
\subsection{System Architecture}
\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
\subsection{Dynamic Pricing Algorithm Analysis}
Deep dive into how the algorithm works, different kinds and justification for chosen appraoches + agent impact modeling and quantification.
\subsection{Reinforcement Learning Formulation}
How do we define the state space, action space and reward function breakdown and algorithm benchmarking.
POSSIBLY: Expand into full subsections: 3.6.1 (State-Action Space), 3.6.2 (Reward Design), 3.6.3 (Benchmarking)
\begin{algorithm}[t]
\DontPrintSemicolon
\KwIn{stepsize $\eta$, smoothing $\delta$, rank $d$}
\For{$t=1$ \KwTo $T$}{
Sample $u_t$ on unit sphere; set $x_t^\prime=x_t+\delta u_t$\;
Set $p_t \gets U x_t^\prime$ and observe $q_t, R_t(p_t)$\;
$x_{t+1} \gets \Pi\_{\mathcal{X}}(x_t-\eta R_t(p_t) u_t)$\;
}
\caption{Online Pricing Optimization (template)}
\end{algorithm}

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@@ -0,0 +1,16 @@
\section{Results}
\subsection{Behavioral Analysis}
Include markov chains of transition matrices, compare distributions (look at Divergence metrics)
\subsection{Experimental Outcomes}
Align with defined objectives, show results and statistical significance (or not).
\subsection{Interpretation and Insights}
Inference from given patterns and show key findings.
\subsection{Anomalies}

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@@ -0,0 +1,9 @@
\section{Discussion}
\subsection{Risk Assessment and Limitations}
Acknowledge risks and constraints and data sizes.
\subsection{Implications of Findings}
Interpretation of results and altenrative scenarios with broader market implications.

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@@ -0,0 +1,8 @@
\section{Conclusion}
\subsection{Summary of contributions }
Restate the thesis and key findings with validation of research objectives.
\subsection{Future Works and Next Steps}
Identify the research gaps here and potential business implications and setup of business + Proposed extensions and a long term agenda.

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@@ -0,0 +1,3 @@
\section{Acknowledgements}
Eugene Bykovets, PhD - ETH

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@@ -35,6 +35,12 @@ The primary objective of this thesis is to develop and validate pricing heuristi
\maketitle
\input{chapters/01-intro}
\input{chapters/02-literature-review}
\input{chapters/03-methodology}
\input{chapters/04-results}
\input{chapters/05-discussion}
\input{chapters/06-conclusion}
\printbibliography

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@@ -4,10 +4,12 @@
\usepackage{csquotes}
\usepackage{subcaption}
\usepackage{siunitx}
\usepackage{tikz}
\usepackage{listings}
\usepackage{xcolor}
\usepackage[ruled,vlined]{algorithm2e}
\usetikzlibrary{positioning, shapes, arrows.meta, fit, backgrounds}
\lstset{
basicstyle=\ttfamily\footnotesize,
breaklines=true,