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\title{Pricing Heuristics Against Non-human Transaction Orchestration Mechanisms}

\author{Daniel Rösel}
\email{daniel@alves.world}
\affiliation{%
  \institution{IE University}
  \city{Madrid}
  \country{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}

\maketitle

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\appendix
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