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updating build

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This commit is contained in:
Daniel Rosel
2026-04-24 11:44:20 +02:00
parent 9bbacd6bdc
commit acf5bb5409
4 changed files with 194 additions and 168 deletions
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6
Makefile
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@@ -44,7 +44,7 @@ SWEEP_ENV_LOAD = set -a; [ -f "$(SWEEP_ENV_FILE)" ] && . "$(SWEEP_ENV_FILE)" ||
.PHONY: help
help:
@echo "pdf.build pdf.watch pdf.clean pdf.genpop pdf.genpop.watch pdf.summary pdf.summary.watch pdf.arxiv pdf.defense | test.backend test.e2e test.all | web.dev | install | train | benchmark | benchmark.simple | benchmark.agent | train.agent | train.bootstrap | stats.lines | docs.platform | manim.defense manim.defense.hq manim.render manim.render.full manim.render.poster manim.render.appendix manim.render.all"
@echo "pdf.build pdf.watch pdf.clean pdf.genpop pdf.genpop.watch pdf.summary pdf.summary.watch pdf.arxiv pdf.defense pdf.defense.html | test.backend test.e2e test.all | web.dev | install | train | benchmark | benchmark.simple | benchmark.agent | train.agent | train.bootstrap | stats.lines | docs.platform | manim.defense manim.defense.hq manim.render manim.render.full manim.render.poster manim.render.appendix manim.render.all"
@echo "backend.server backend.provider backend.worker | platform.up platform.down platform.logs | docker.train.publish"
@echo "data.pull data.push data.whoclicked.publish | study.margin-erosion study.margin-erosion.quick study.margin-erosion.plot"
@echo "tpu.ray.bootstrap tpu.ray.deps tpu.ray.verify tpu.ray.teardown"
@@ -114,6 +114,10 @@ pdf.summary.watch:
pdf.defense:
@cd paper/defense && pdflatex -interaction=nonstopmode defense.tex && pdflatex -interaction=nonstopmode defense.tex
.PHONY: pdf.defense.html
pdf.defense.html:
@bash paper/defense/build_html.sh
.PHONY: test.backend
test.backend:
@$(NX) run research:test

194
paper/defense/defense.nav
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@@ -2,124 +2,124 @@
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paper/defense/defense.pdf
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112
paper/defense/defense.tex
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@@ -124,6 +124,8 @@
\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}
@@ -134,40 +136,43 @@
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}
]
\node[flow,fill=PhantomCyan!18] (recon) at (-3.1,1.1)
\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[flow,fill=PhantomIndigo!16] (buy) at (3.1,1.1)
\node<2->[flow,fill=PhantomIndigo!16] (buy) at (3.1,1.1)
{\textbf{Clean execution session}\\buys using the best found quote};
\draw[-{Stealth[length=3mm]},ultra thick,PhantomSlate] (recon.east) -- (buy.west);
\node[font=\scriptsize\bfseries,text=PhantomSlate] at (0,1.98)
\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[densely dashed,thick,PhantomCyan!90!black]
\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[note] at (0,-0.65)
\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}]
\node[card,fill=PhantomInk,text=white] at (-4.05,0)
\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[card,fill=PhantomCyan!16,text=PhantomSlate] at (0,0)
\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[card,fill=PhantomIndigo!12,text=PhantomSlate] at (4.05,0)
{\large$\lim_{N\to\infty}\mathrm{COI}=0$\\[-0.05em]\footnotesize theorem-level pressure};
\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}
{\footnotesize\textbf{Implication:} if quote discovery and purchase split, standard session-based pricing overestimates willingness to pay.}
\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 \textbf{Mechanism (SQ2):} independent reconnaissance pushes realizable price toward the order-statistics floor.
\item \textbf{Signal (SQ1):} human and agent sessions are behaviorally separable from trajectories alone.
\item \textbf{Control (SQ3):} the session score feeds a robust pricing learner under contamination uncertainty.
\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}
@@ -222,18 +227,15 @@
]
\node[draw=PhantomInk,rounded corners=5pt,fill=white,minimum width=6.85cm,minimum height=4.45cm] at (0,0) {};
\node[anchor=west,font=\footnotesize\bfseries,text=PhantomInk] at (-3.2,1.72) {WhoClickedIt dataset card};
\node[anchor=west,draw=PhantomInk!35,rounded corners=2pt,fill=PhantomCyan!10,inner xsep=4pt,inner ysep=3pt,font=\scriptsize\ttfamily,text=PhantomSlate] at (-3.2,1.22)
\node[anchor=west,draw=PhantomInk!35,rounded corners=2pt,fill=PhantomCyan!10,inner xsep=4pt,inner ysep=3pt,text width=6.15cm,align=left,font=\tiny\ttfamily,text=PhantomSlate] at (-3.2,1.22)
{huggingface.co/datasets/velocitatem/whoclickedit};
\node[anchor=west,chip,fill=PhantomCyan!12] at (-3.2,0.65) {\textbf{Rows} 3874};
\node[anchor=west,chip,fill=PhantomCyan!12] at (-1.70,0.65) {\textbf{Cols} 42};
\node[anchor=west,chip,fill=PhantomCyan!12] at (-0.25,0.65) {\textbf{Sessions} 36};
\node[anchor=west,chip,fill=PhantomIndigo!12] (humanrows) at (-3.2,0.03) {\textbf{Human rows} 798};
\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,-0.68)
\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,-1.36)
\node[body,font=\tiny\itshape,text=PhantomSlate!85] at (-3.2,-1.01)
{Kafka provenance is retained for reproducibility and downstream analysis.};
\end{tikzpicture}
@@ -241,11 +243,11 @@
\centering
\begin{tikzpicture}[font=\scriptsize\sffamily,
stat/.style={draw=PhantomInk,rounded corners=5pt,minimum width=4.95cm,minimum height=1.33cm,align=center}]
\node[stat,fill=PhantomInk,text=white] at (0,1.95)
{\Large\bfseries 13 H / 16 A\\[-0.1em]\footnotesize labeled trajectories in thesis cohort};
\node[stat,fill=PhantomCyan!14,text=PhantomSlate] at (0,0.25)
\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[stat,fill=PhantomIndigo!12,text=PhantomSlate] at (0,-1.45)
\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}
@@ -253,7 +255,7 @@
\stagebar{2}
\end{frame}
\begin{frame}{Experimental design controls goals, not navigation paths}
\begin{frame}{Experimental design controls goals, not instructions}
\begin{columns}[T,onlytextwidth]
\column{0.58\textwidth}
\centering
@@ -335,26 +337,30 @@
\begin{frame}{Two divergence scores become one continuous control signal}
\centering
\[
f(\tau') = P(A\mid\tau') = \sigma\!\left(\frac{\Delta_H-\Delta_A}{T}\right)
\only<1>{\Delta_H = D_{KL}(\hat T'\mid\mid\bar T_H),\quad \Delta_A = D_{KL}(\hat T'\mid\mid\bar T_A)}%
\only<2>{g(\tau') = \Delta_H-\Delta_A}%
\only<3->{f(\tau') = P(A\mid\tau') = \sigma\!\left(\frac{g(\tau')}{T}\right)}
\]
\vspace{0.4em}
\begin{tikzpicture}[font=\scriptsize\sffamily]
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\draw[thick,PhantomSlate] (0,-0.16) -- (0,0.16);
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\node[anchor=north] at (4,0) {agent-like};
\node[anchor=north] at (0,0) {$\Delta_H-\Delta_A=0$};
\fill[PhantomCyan!75!black] (-2.2,0) circle (2.2pt);
\fill[PhantomIndigo!75!black] (2.2,0) circle (2.2pt);
\node[anchor=south,text=PhantomCyan!75!black] at (-2.2,0) {low $f(\tau')$};
\node[anchor=south,text=PhantomIndigo!75!black] at (2.2,0) {high $f(\tau')$};
\draw<2->[very thick,PhantomSlate] (-4,0) -- (4,0);
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\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 Continuous scoring is used to steer contamination-aware pricing.
\item The design target is guidance, not a hard user-level ban decision.
\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}
@@ -391,10 +397,21 @@
\begin{frame}{Reward composition penalizes leakage while guarding user experience}
\[
\only<1>{%
r_t =
{\color{PhantomInk}\underline{R(p_t,\hat Q_t)}}%
}%
\only<2>{%
r_t =
{\color{PhantomInk}\underline{R(p_t,\hat Q_t)}}
- {\color{PhantomCyan!95!black}\underline{\lambda\,f(\tau'_t)\,c_{\text{info}}}}%
}%
\only<3->{%
r_t =
{\color{PhantomInk}\underline{R(p_t,\hat Q_t)}}
- {\color{PhantomCyan!95!black}\underline{\lambda\,f(\tau'_t)\,c_{\text{info}}}}
- {\color{PhantomIndigo!95!black}\underline{\eta_{\text{ux}}\,UX(\tau'_t,p_t)}}
- {\color{PhantomIndigo!95!black}\underline{\eta_{\text{ux}}\,UX(\tau'_t,p_t)}}%
}%
\]
\vspace{0.45em}
@@ -415,6 +432,7 @@
\end{tikzpicture}
\column{0.32\textwidth}
\centering
\uncover<2->{%
\begin{tikzpicture}[font=\scriptsize\sffamily]
\node[
draw=PhantomInk,
@@ -426,9 +444,11 @@
align=center,
text=PhantomCyan!95!black
] {\textbf{Leakage term}\\[-0.08em]scales with agent-likelihood score};
\end{tikzpicture}
\end{tikzpicture}%
}
\column{0.32\textwidth}
\centering
\uncover<3->{%
\begin{tikzpicture}[font=\scriptsize\sffamily]
\node[
draw=PhantomInk,
@@ -440,13 +460,14 @@
align=center,
text=PhantomIndigo!95!black
] {\textbf{UX term}\\[-0.08em]discourages unstable pricing behavior};
\end{tikzpicture}
\end{tikzpicture}%
}
\end{columns}
\vspace{0.25em}
\begin{itemize}[<+->]
\item Baseline experiments use a query-tax leakage surrogate for tractability.
\item Supra-competitive anchor penalties are tracked as an additional safety rail.
\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}
@@ -532,6 +553,7 @@
\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}