% Final thesis defense (PHANTOM)
% Build: cd paper/defense && pdflatex defense.tex && pdflatex defense.tex
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\title{PHANTOM}
\subtitle{Pricing Heuristics Against Non-human Transaction Orchestration Mechanisms}
\author{Daniel Rösel}
\institute{IE University, Madrid \\ Supervisor: Alberto Martín Izquierdo}
\date{\today}

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\newcommand{\stagebar}[1]{}

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\begin{document}

{
\setbeamercolor{background canvas}{bg=PhantomInk}
\begin{frame}[plain]
  \vfill
  \centering
  {\color{white}\Huge\bfseries PHANTOM\par}
  \vspace{0.6em}
  {\color{PhantomCyan}\rule{0.45\paperwidth}{0.06cm}\par}
  \vspace{0.8em}
  {\large\color{white!90!black}Pricing heuristics against non-human transaction orchestration\par}
  \vfill
  {\color{white!75!black}\normalsize Daniel Rösel\par}
  {\color{white!65!black}\small IE University \textbullet\ Supervisor: Alberto Martín Izquierdo\par}
  \vspace{1.2em}
  {\footnotesize\color{PhantomCyan!80!white}\href{https://velocitatem.github.io/PHANTOM/}{\texttt{velocitatem.github.io/PHANTOM}}}
  \vfill
\end{frame}
}

\begin{frame}{Roadmap: one argument in six stages (15 min)}
  \centering
  \begin{tikzpicture}[
    font=\scriptsize\sffamily,
    stage/.style={draw=PhantomInk,rounded corners=3pt,fill=PhantomCyan!10,minimum width=1.95cm,minimum height=1.05cm,align=center},
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    \node[stage,fill=PhantomCyan!14] (intro) {Intro\\2m};
    \node[stage,right=0.30cm of intro] (platform) {Platform\\4m};
    \node[stage,right=0.30cm of platform] (signal) {Signal\\4m};
    \node[stage,right=0.30cm of signal] (drrl) {DR-RL\\4m};
    \node[stage,right=0.30cm of drrl] (results) {Results\\1m};
    \node[stage,right=0.30cm of results] (close) {Close};

    \draw[flow,shorten <=2pt,shorten >=2pt] (intro.east) -- (platform.west);
    \draw[flow,shorten <=2pt,shorten >=2pt] (platform.east) -- (signal.west);
    \draw[flow,shorten <=2pt,shorten >=2pt] (signal.east) -- (drrl.west);
    \draw[flow,shorten <=2pt,shorten >=2pt] (drrl.east) -- (results.west);
    \draw[flow,shorten <=2pt,shorten >=2pt] (results.east) -- (close.west);
  \end{tikzpicture}

  \vspace{0.75em}
  \begin{block}{Main research question}
    How can dynamic pricing preserve margin integrity when transactions are increasingly mediated by non-human agents?
  \end{block}
  \vspace{0.35em}
  {\footnotesize Dynamic pricing has often been treated as a secondary optimization layer; agent-mediated shopping turns it into a primary margin-risk surface.}
  \stagebar{1}
\end{frame}

\begin{frame}{Agentic recon creates direct financial pressure on pricing power}
  \centering
  \begin{tikzpicture}[
    font=\small\sffamily,
    flow/.style={draw=PhantomInk,rounded corners=6pt,minimum width=5.3cm,minimum height=1.25cm,align=center},
    note/.style={draw=PhantomInk!55,rounded corners=4pt,minimum width=11.2cm,minimum height=0.95cm,align=center,fill=white,font=\scriptsize}
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    \path[use as bounding box] (-6.1,-1.25) rectangle (6.1,2.25);
    \node<1->[flow,fill=PhantomCyan!18] (recon) at (-3.1,1.1)
      {\textbf{Recon session}\\samples multiple quotes};
    \node<2->[flow,fill=PhantomIndigo!16] (buy) at (3.1,1.1)
      {\textbf{Clean execution session}\\buys using the best found quote};
    \draw<2->[-{Stealth[length=3mm]},ultra thick,PhantomSlate] (recon.east) -- (buy.west);
    \node<2->[font=\scriptsize\bfseries,text=PhantomSlate] at (0,1.98)
      {query and purchase are decoupled};
    \draw<3->[densely dashed,thick,PhantomCyan!90!black]
      (recon.south east) .. controls +(1.15,-0.95) and +(-1.15,-0.95) .. (buy.south west);
    \node<3->[note] at (0,-0.65)
      {The platform sees behavior proxy $\hat q$, while true demand response $d(p\mid\theta)$ stays latent.};
  \end{tikzpicture}

  \vspace{0.25em}
  \begin{tikzpicture}[font=\scriptsize\sffamily,
    card/.style={draw=PhantomInk,rounded corners=4pt,minimum width=3.85cm,text width=3.55cm,minimum height=1.4cm,align=center}]
    \path[use as bounding box] (-6.05,-0.95) rectangle (6.05,0.95);
    \node<4->[card,fill=PhantomInk,text=white] at (-4.05,0)
      {\large$\mathrm{COI}(\pi)=\mathbb{E}[P]-\underline p$\\[-0.05em]\footnotesize pricing power KPI};
    \node<5->[card,fill=PhantomCyan!16,text=PhantomSlate] at (0,0)
      {\Large\bfseries$-9{,}014$\\[-0.05em]\footnotesize revenue units\\per +0.1 contamination};
    \node<6->[card,fill=PhantomIndigo!12,text=PhantomSlate] at (4.05,0)
      {\large$\lim_{N\to\infty}\mathrm{COI}=0$\\[-0.05em]\footnotesize theorem as intuition guide};
  \end{tikzpicture}

  \vspace{0.15em}
  \uncover<6->{\scriptsize\textit{The theorem is used to guide intuition: independent reconnaissance pressure can drive realizable prices toward the floor.}}\\[0.2em]
  \uncover<7->{\footnotesize\textbf{Implication:} most production dynamic pricing stacks are not yet ready for AI-agent traffic; when quote discovery and purchase split, session-based pricing overestimates willingness to pay.}
  \stagebar{1}
\end{frame}

\begin{frame}{The thesis answers one chain: mechanism \(\to\) signal \(\to\) control}
  \begin{enumerate}[<+->]\setlength{\itemsep}{0.45em}
    \item Can agent and human sessions be reliably distinguished from behavioral interaction signals alone?
    \item How do agents affect prices and revenue that can be generated?
    \item Can we make new policies that maintain margin in agent contaminated systems?
  \end{enumerate}

  \vspace{0.35em}
  \stagebar{1}
\end{frame}

\section{Platform Development}

\begin{frame}{Stage 1: We built a dual-loop platform to observe behavior and price exposure together}
  \centering
  \begin{tikzpicture}[
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    \node[box,fill=PhantomCyan!14] (actors) at (0,1.45) {Humans + Agents};
    \node[box,fill=white] (web) at (2.9,1.45) {Next.js\\storefront};
    \node[box,fill=white] (provider) at (5.8,1.45) {Pricing\\provider};
    \node[box,fill=white] (redis) at (8.7,1.45) {Redis\\serve layer};
    \node[box,fill=PhantomIndigo!10,minimum width=3.1cm] (kafka) at (4.35,-0.15) {Kafka topics\\behavior + price logs};
    \node[box,fill=PhantomCyan!10,minimum width=2.8cm] (airflow) at (8.0,-0.15) {Airflow + worker\\batch updates};

    \draw[arr] (actors) -- (web);
    \draw[arr] (web) -- (provider);
    \draw[arr] (provider) -- (redis);
    \draw[arr] (web.south) -- (kafka.north west);
    \draw[arr] (provider.south) -- (kafka.north east);
    \draw[arr] (kafka) -- (airflow);
    \draw[arr] (airflow.north) -| (redis.south);
    \draw[arr] (redis.west) to[bend left=35] (provider.east);

    \node[font=\tiny\itshape,text=PhantomSlate] at (2.2,-1.0) {Kappa: streaming telemetry};
    \node[font=\tiny\itshape,text=PhantomSlate] at (8.1,-1.0) {Lambda: offline learning + refresh};
  \end{tikzpicture}

  \vspace{0.35em}
  \begin{itemize}[<+->]
    \item Every quote has a matching behavioral context in the log stream.
    \item The same architecture supports reproducible stress tests before any live deployment.
  \end{itemize}
  \stagebar{2}
\end{frame}

\begin{frame}{Dataset card: compact, labeled, and experiment-ready}
  \begin{columns}[T,onlytextwidth]
    \column{0.60\textwidth}
    \centering
    \begin{tikzpicture}[
      font=\scriptsize\sffamily,
      chip/.style={draw=PhantomInk!40,rounded corners=2pt,inner sep=2.7pt},
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      \node[anchor=west,font=\footnotesize\bfseries,text=PhantomInk] at (-3.2,1.72) {WhoClickedIt dataset card};
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        {huggingface.co/datasets/velocitatem/whoclickedit};

      \node[anchor=west,chip,fill=PhantomIndigo!12] (humanrows) at (-3.2,0.52) {\textbf{Human rows} 798};
      \node[anchor=west,chip,fill=PhantomIndigo!12] at ([xshift=0.16cm]humanrows.east) {\textbf{Agent rows} 3076};

      \node[body,text=PhantomSlate] at (-3.2,-0.33)
        {Flat schema and explicit actor labels simplify session-aware train/test splits.};
      \node[body,font=\tiny\itshape,text=PhantomSlate!85] at (-3.2,-1.01)
        {Kafka provenance is retained for reproducibility and downstream analysis.};
    \end{tikzpicture}

    \column{0.38\textwidth}
    \centering
    \begin{tikzpicture}[font=\scriptsize\sffamily,
      stat/.style={draw=PhantomInk,rounded corners=5pt,minimum width=4.95cm,minimum height=1.33cm,align=center}]
      \node<1->[stat,fill=PhantomInk,text=white] at (0,1.95)
        {\Large\bfseries 29 Interviews\\[-0.1em]\footnotesize labeled trajectories in observed samples};
      \node<2->[stat,fill=PhantomCyan!14,text=PhantomSlate] at (0,0.25)
        {\Large\bfseries 45\% / 55\%\\[-0.1em]\footnotesize human/agent trajectory split};
      \node<3->[stat,fill=PhantomIndigo!12,text=PhantomSlate] at (0,-1.45)
        {\Large\bfseries 2 streams\\[-0.1em]\footnotesize interaction + price-log records};
    \end{tikzpicture}
  \end{columns}

  \stagebar{2}
\end{frame}

\begin{frame}{Experimental design controls goals, not instructions}
  \begin{columns}[T,onlytextwidth]
    \column{0.58\textwidth}
    \centering
    \begin{tikzpicture}[
      font=\scriptsize\sffamily,
      box/.style={draw=PhantomInk,rounded corners=3pt,minimum width=3.65cm,minimum height=0.95cm,align=center},
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      \node[box,fill=PhantomCyan!14] (tasks) at (0,1.8) {JTBD task pool\\hotel + airline modes};
      \node[box,fill=white] (assign) at (0,0.55) {Random assignment\\mode + task + actor id};
      \node[box,fill=PhantomIndigo!12] (run) at (0,-0.7) {Execution\\human or browser-use agent};
      \node[box,fill=white] (logs) at (0,-1.95) {Session logs\\$e=(a,i,t,\mu,\delta)$ + quotes};
      \draw[arr] (tasks) -- (assign);
      \draw[arr] (assign) -- (run);
      \draw[arr] (run) -- (logs);
    \end{tikzpicture}

    \column{0.40\textwidth}
    \begin{itemize}[<+->]\setlength{\itemsep}{0.55em}
      \item Agents run with \textbf{browser-use} and a model-swappable LLM router (default \texttt{gpt-5-mini}).
      \item Tasks are defined by outcomes, not scripted clicks, to preserve behavioral variety.
      \item Current release is stronger on hotel flows than airline flows.
    \end{itemize}
  \end{columns}
  \stagebar{2}
\end{frame}

\section{Distinguishability Construction}

\begin{frame}{Stage 2: A behavior kernel is a compact signature of navigation dynamics}
  \begin{columns}[T,onlytextwidth]
    \column{0.48\textwidth}
    \begin{block}{Definition}
      \[
        \hat P(s'\mid s)=\frac{N(s,s')}{\sum_k N(s,k)}
      \]
    \end{block}
    \begin{itemize}[<+->]
      \item Build one kernel per session, then prototypes for human and agent cohorts.
      \item Compare each incoming session to both prototypes with KL divergence.
    \end{itemize}

    \column{0.50\textwidth}
    \centering
    \begin{tikzpicture}[font=\scriptsize\sffamily]
      \node[draw=PhantomInk,rounded corners=3pt,fill=PhantomCyan!12,minimum width=3.9cm,minimum height=0.85cm] (a) at (0,1.4) {page\_view};
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      \node[draw=PhantomInk,rounded corners=3pt,fill=PhantomIndigo!12,minimum width=3.9cm,minimum height=0.85cm] (c) at (0,-0.9) {add\_item\_to\_cart};
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      \draw[-{Stealth[length=2.2mm]},thick,PhantomSlate] (b) -- node[right,font=\tiny]{0.31} (c);
      \draw[-{Stealth[length=2.2mm]},thick,PhantomSlate!70] (b.east) .. controls +(1.1,0.5) and +(1.1,-0.5) .. node[right,font=\tiny]{0.52} (b.east);
      \node[font=\tiny\itshape,text=PhantomSlate] at (0,-1.7) {Kernel rows encode ``what usually comes next.''};
    \end{tikzpicture}
  \end{columns}
  \stagebar{3}
\end{frame}

\begin{frame}{Human and agent kernels are separable in the controlled cohort}
  \begin{columns}[T,onlytextwidth]
    \column{0.48\textwidth}
    \centering
    \textbf{Human transition structure}\par\vspace{0.2em}
    \includegraphics[width=\linewidth,height=0.46\textheight,keepaspectratio]{mdp_human.pdf}
    \column{0.48\textwidth}
    \centering
    \textbf{Agent transition structure}\par\vspace{0.2em}
    \includegraphics[width=\linewidth,height=0.46\textheight,keepaspectratio]{mdp_agent.pdf}
  \end{columns}

  \vspace{0.15em}
  \begin{columns}[T,onlytextwidth]
    \column{0.32\textwidth}\centering\metriccard{-3.35}{mean gap (human)}
    \column{0.32\textwidth}\centering\metriccard{+1.65}{mean gap (agent)}
    \column{0.32\textwidth}\centering\metriccard{$p<0.001$}{Mann-Whitney rank test}
  \end{columns}
  \stagebar{3}
\end{frame}

\begin{frame}{Two divergence scores become one continuous control signal}
  \centering
  \[
    \only<1>{\Delta_H = D_{KL}(\hat T'\mid\mid\bar T_H),\quad \Delta_A = D_{KL}(\hat T'\mid\mid\bar T_A)}%
    \only<2>{g(\tau') = \Delta_H-\Delta_A}%
    \only<3->{f(\tau') = P(A\mid\tau') = \sigma\!\left(\frac{g(\tau')}{T}\right)}
  \]

  \vspace{0.4em}
  \begin{tikzpicture}[font=\scriptsize\sffamily]
    \draw<2->[very thick,PhantomSlate] (-4,0) -- (4,0);
    \draw<2->[thick,PhantomSlate] (0,-0.16) -- (0,0.16);
    \node<2->[anchor=north] at (-4,0) {human-like};
    \node<2->[anchor=north] at (4,0) {agent-like};
    \node<3->[anchor=north] at (0,0) {$g(\tau')=0$};
    \fill<4->[PhantomCyan!75!black] (-2.2,0) circle (2.2pt);
    \node<4->[anchor=south,text=PhantomCyan!75!black] at (-2.2,0) {low $f(\tau')$};
    \fill<5->[PhantomIndigo!75!black] (2.2,0) circle (2.2pt);
    \node<5->[anchor=south,text=PhantomIndigo!75!black] at (2.2,0) {high $f(\tau')$};
  \end{tikzpicture}

  \vspace{0.25em}
  \begin{itemize}
    \item<3-> The signed gap $g(\tau')$ is positive when a session is closer to agent behavior.
    \item<4-> Temperature $T$ calibrates how sharply the score moves away from uncertainty.
    \item<6-> Continuous scoring is used to steer contamination-aware pricing.
    \item<7-> The design target is guidance, not a hard user-level ban decision.
  \end{itemize}
  \stagebar{3}
\end{frame}

\section{Distributionally Robust RL}

\begin{frame}{Stage 3: DR-RL trains against plausible contamination shifts, not one fixed world}
  \small
  \begin{columns}[T,onlytextwidth]
    \column{0.48\textwidth}
    \begin{block}{Ideal robust object}
      \[
        \mathcal U_\epsilon(\hat P_N)=\{Q: W_p(Q,\hat P_N)\le\epsilon\}
      \]
      \centering
      robust against distribution shift around the empirical demand law
    \end{block}

    \column{0.50\textwidth}
    \begin{block}{Engine approximation used in experiments}
      \[
        \mathcal A_{\epsilon_\alpha}(\alpha_0)=\{\alpha:|\alpha-\alpha_0|\le\epsilon_\alpha\}
      \]
      \centering
      small grid over $\alpha$ \;\textrightarrow\; inner worst-case candidate
    \end{block}
  \end{columns}
  \vspace{0.2em}
  \begin{alertblock}{Practical boundary}
    In code we solve a local robust loop around $\alpha_0$, not the full continuous Wasserstein adversary.
  \end{alertblock}
  \stagebar{4}
\end{frame}

\begin{frame}{Reward composition penalizes leakage while guarding user experience}
  \[
    \only<1>{%
      r_t =
      {\color{PhantomInk}\underline{R(p_t,\hat Q_t)}}%
    }%
    \only<2>{%
      r_t =
      {\color{PhantomInk}\underline{R(p_t,\hat Q_t)}}
      - {\color{PhantomCyan!95!black}\underline{\lambda\,f(\tau'_t)\,c_{\text{info}}}}%
    }%
    \only<3->{%
      r_t =
      {\color{PhantomInk}\underline{R(p_t,\hat Q_t)}}
      - {\color{PhantomCyan!95!black}\underline{\lambda\,f(\tau'_t)\,c_{\text{info}}}}
      - {\color{PhantomIndigo!95!black}\underline{\eta_{\text{ux}}\,UX(\tau'_t,p_t)}}%
    }%
  \]

  \vspace{0.45em}
  \begin{columns}[T,onlytextwidth]
    \column{0.32\textwidth}
    \centering
    \begin{tikzpicture}[font=\scriptsize\sffamily]
      \node[
        draw=PhantomInk,
        rounded corners=4pt,
        fill=PhantomInk!12,
        minimum width=0.98\linewidth,
        text width=0.88\linewidth,
        minimum height=1.28cm,
        align=center,
        text=PhantomInk
      ] {\textbf{Revenue term}\\[-0.08em]keeps market objective explicit};
    \end{tikzpicture}
    \column{0.32\textwidth}
    \centering
    \uncover<2->{%
      \begin{tikzpicture}[font=\scriptsize\sffamily]
        \node[
          draw=PhantomInk,
          rounded corners=4pt,
          fill=PhantomCyan!16,
          minimum width=0.98\linewidth,
          text width=0.88\linewidth,
          minimum height=1.28cm,
          align=center,
          text=PhantomCyan!95!black
        ] {\textbf{Leakage term}\\[-0.08em]scales with agent-likelihood score};
      \end{tikzpicture}%
    }
    \column{0.32\textwidth}
    \centering
    \uncover<3->{%
      \begin{tikzpicture}[font=\scriptsize\sffamily]
        \node[
          draw=PhantomInk,
          rounded corners=4pt,
          fill=PhantomIndigo!16,
          minimum width=0.98\linewidth,
          text width=0.88\linewidth,
          minimum height=1.28cm,
          align=center,
          text=PhantomIndigo!95!black
        ] {\textbf{UX term}\\[-0.08em]discourages unstable pricing behavior};
      \end{tikzpicture}%
    }
  \end{columns}

  \vspace{0.25em}
  \begin{itemize}
    \item<2-> Baseline experiments use a query-tax leakage surrogate for tractability.
    \item<3-> Supra-competitive anchor penalties are tracked as an additional safety rail.
  \end{itemize}
  \stagebar{4}
\end{frame}

\begin{frame}{Computationally, wide sweeps are feasible only with aggressive optimization}
  \begin{columns}[T,onlytextwidth]
    \column{0.47\textwidth}
    \centering
    {\Large\(4\times4\times3\times2\times2=\mathbf{192}\)}\\[0.25em]
    {\scriptsize algorithms $\times$ contamination $\times$ robustness $\times$ COI penalty $\times$ action grid}

    \vspace{0.5em}
    \metriccard{160 PFLOPS}{peak aggregate TPU budget}\\[0.45em]
    \metriccard{\textasciitilde180 days}{net compute logged in full study}

    \column{0.51\textwidth}
    \begin{block}{Hot-path rewrite impact}
      \centering
      \begin{tabular}{@{}lcc@{}}
        \toprule
        Mode & Before & After \\
        \midrule
        Baseline step/s & 26.0 & 220.0 \\
        Robust step/s & 7.2 & 136.0 \\
        \bottomrule
      \end{tabular}
    \end{block}
    \vspace{0.1em}
    {\footnotesize
    \begin{itemize}[<+->]
      \item pandas lookup bottlenecks replaced with array/JAX-style loops.
      \item Throughput gains (8.5$\times$, 19$\times$) made broad sweeps practical.
    \end{itemize}}
  \end{columns}
  \stagebar{4}
\end{frame}

\section{Results}

\begin{frame}{Results: contamination hurts revenue; defended policies recover COI}
  \begin{columns}[T,onlytextwidth]
    \column{0.62\textwidth}
    \centering
    \includegraphics[width=\linewidth,height=0.60\textheight,keepaspectratio]{final_focus_coi_by_alpha.pdf}

    \column{0.30\textwidth}
    \metriccard{-90{,}140}{baseline contamination slope}\\[0.3em]
    \metriccard{\textasciitilde3\%}{short-run revenue cost of defense}\\[0.3em]
    \metriccard{Regime-dependent}{COI gains strongest at harder settings}
  \end{columns}
  \stagebar{5}
\end{frame}

\section{Conclusions}

\begin{frame}{Yes, with boundaries: we can defend margin integrity under agentic orchestration}
  \begin{columns}[T,onlytextwidth]
    \column{0.32\textwidth}
    \begin{block}{SQ1\;Distinguishability}
      \centering
      kernels are separable\\$p<0.001$
    \end{block}
    \column{0.32\textwidth}
    \begin{block}{SQ2\;Theoretical impact}
      \centering
      COI erosion mechanism\\proved in baseline limit
    \end{block}
    \column{0.32\textwidth}
    \begin{block}{SQ3\;Mitigation}
      \centering
      robust control shifts\\COI/revenue/UX trade-off
    \end{block}
  \end{columns}

  \vspace{0.35em}
  \begin{alertblock}{Boundary conditions}
    Evidence is from a controlled platform and a small labeled cohort; this is mechanism validation, not full production external validity.
  \end{alertblock}
  \stagebar{6}
\end{frame}

\begin{frame}{What this implies for real pricing systems}
  \begin{itemize}[<+->]\setlength{\itemsep}{0.7em}
    \item \textbf{Financially:} untreated reconnaissance behaves like an information leak and can compress sustainable margins.
    \item \textbf{Operationally:} behavior-only session scoring can be wired into pricing without relying on device fingerprinting.
    \item \textbf{Market exposure:} channels where dynamic pricing has been a secondary layer (aggregators, comparison funnels, promo traffic) are likely to be disrupted first.
    \item \textbf{Strategically:} robust pricing should be calibrated by regime; there is no single penalty that wins everywhere.
    \item \textbf{Before deployment:} larger human baselines, governance review, and legal safeguards are mandatory.
  \end{itemize}
  \stagebar{6}
\end{frame}

\begin{frame}[plain]
  \centering
  \vfill
  {\LARGE\bfseries Thank you}
  \vspace{0.8em}

  {\large Questions and discussion}

  \vfill
  {\footnotesize\color{PhantomSlate!80}Appendix follows: COI theorem derivation, reward composition, and sample-size notes.}
  \vfill
\end{frame}

\appendix
\input{defense_appendix}

\end{document}