Paper first fillout (#39)

* initial environemnt definitions

* high level defintion

* formlating the reward simply

* improved implementation

* tailored docker compose image for secondary tenaordboard

* preliminary desriptions and babble

* details on formulation and defintion of agent and its loop

* typos one

* more grammar issues

* fluidity improvements and refactors

* more decluttering and dnoising

* finalizing introduction review

* some methodology

* somehow this disappeared

* bit more of this and that

* methodology of how we do architectuer and online DP

* fix: compilation

* expanding on the taxonomy and economic references

* authoer notes

* acks + google GCP

* making space w new format nada lit review

* stronger lit review and more sources

* forgot about tables and graphs

* dedupe citations

* adding cloudflare

* fixing env vars

* updating docs with url

* upating embed

* fixing the url

* paper badge

* formaliztaion of rewards and adding definitions

* noisy formulations

* connecting some more dots here

* adding significant weight in prices

* fixing error

* fixing typos and consistency

* extra math formulations and refferenceot DRO

* fixing diagram of loops

* github mindmap

* fixing erro and thiknig about big picture

* enhancing the website

* goals methodology and gitignore

* some more references and theory links

* talking about some wtp

* feature: added wordcounter

* forcing latex builds and fixining the bib #

* refactor: update Cost of Information equations and notation for clarity

* some more math and refactors

* refactor: unify notation and improve clarity in COI equations

* refactor: generalize master function for demand estimation and pricing strategies

* we dont like math but we have to do it :(

* refactor: enhance Cost of Information framework with additional context and illustration

* refactor: enhance literature review and methodology sections with economic theory insights and system architecture details

* alining format to fit the rubric

* refactoring bibliography

* fix: align

* mdp additionally

* trying different title

* adding balance figure

* agentic givergence, finally

* fix: figure fonts adjusted to match

paper/src/main.tex

@@ -1,39 +1,30 @@
 % -*- TeX-master: t -*-
-\documentclass[sigconf,nonacm,natbib=false]{acmart}
+\documentclass[12pt,letterpaper]{article}
 
-% Remove ACM copyright/conference info for thesis
-\settopmatter{printacmref=false}
-\renewcommand\footnotetextcopyrightpermission[1]{}
 \pagestyle{plain}
 
 \input{preamble}
 
 \begin{document}
 
-\title{Pricing Heuristics Against Non-human Transaction Orchestration Mechanisms}
+\title{Adversarially Distributionally Robust Optimization and Reinforcement Learning for Informed Dynamic Pricing under Strategic Demand Contamination}
 
-\author{Daniel Rösel}
-\email{daniel@alves.world}
-\affiliation{%
-  \institution{IE University}
-  \city{Madrid}
-  \country{Spain}
+\author{
+  Daniel Rösel\thanks{Primary author and student researcher. Email: daniel@alves.world} \\
+  IE University, Madrid, Spain \\[1em]
+  Alberto Martín Izquierdo\thanks{Thesis advisor. Email: amartini@faculty.ie.edu} \\
+  IE University, Madrid, Spain
 }
 
-\author{Alberto Martín Izquierdo}
-\email{amartini@faculty.ie.edu}
-\affiliation{%
-  \institution{IE University}
-  \city{Madrid}
-  \country{Spain}
-}
-
-\begin{abstract}
-The primary objective of this thesis is to develop and validate pricing heuristics that protect e-commerce platforms from systematic exploitation by Large Language Model (LLM) agents within dynamic pricing environments. As AI agents increasingly mediate consumer transactions, they enable users to circumvent the Cost of Information (the price premium accumulated through demand signal expression) by conducting reconnaissance in isolated sessions before executing purchases through clean sessions at base prices. This research will make an anticipatory contribution by adapting recommendation system methodologies to distinguish between genuine human browsing behaviour and agent-orchestrated information gathering, thereby enabling pricing systems to maintain margin integrity without degrading the user experience for legitimate customers or getting rid of leads generated by LLMs.
-\end{abstract}
+\date{\today}
 
 \maketitle
 
+\begin{abstract}
+The primary objective of this thesis is to develop and validate pricing heuristics that protect e-commerce platforms from systematic exploitation by Large Language Model (LLM) agents within dynamic pricing environments. As AI agents increasingly mediate consumer transactions, they enable users to circumvent the Cost of Information (the price premium accumulated through demand signal expression) by conducting reconnaissance in isolated sessions before executing purchases through clean sessions at base prices. This research will make an anticipatory contribution by adapting recommendation system methodologies to distinguish between genuine human browsing behavior and agent-orchestrated information gathering, thereby enabling pricing systems to maintain margin integrity without degrading the user experience for legitimate customers or getting rid of leads generated by LLMs.
+\end{abstract}
+
+
 \input{chapters/01-intro}
 \input{chapters/02-literature-review}
 \input{chapters/03-methodology}
@@ -42,11 +33,19 @@ The primary objective of this thesis is to develop and validate pricing heuristi
 \input{chapters/06-conclusion}
 
 
+\section*{Acknowledgments}
+Eugene Bykovets, PhD - ETH for helping with problem formulation.
+Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC).
+
 \printbibliography
 
 \clearpage
-\onecolumn
 \appendix
+\section{Terminology}
+\begin{description}
+\item[Agent $A$] An actor of non-human nature, powered by an LLM.
+\item[Human $H$] An individual human with some job to be done.
+\end{description}
 \input{../build/concatenated_code}
 
 \end{document}