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\title{Adversarially Distributionally Robust Optimization and Reinforcement Learning for Informed Dynamic Pricing under Strategic Demand Contamination}

\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
}

\date{\today}

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\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.
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\section*{Acknowledgments}
Eugene Bykovets, PhD - ETH for helping with problem formulation.
Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC).

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\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}
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