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Daniel Rosel
2026-04-27 17:45:40 +02:00
parent acf5bb5409
commit b677e80b80
6 changed files with 338 additions and 140 deletions
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4
.gitignore vendored
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@@ -64,8 +64,8 @@ tests/e2e/test-results/
tests/e2e/node_modules/** tests/e2e/node_modules/**
# rl/sim run outputs # rl/sim run outputs
sim/rl/behavior_loader/*.dot # sim/rl/behavior_loader/*.dot
sim/rl/behavior_loader/*.png # sim/rl/behavior_loader/*.png
sim/rl/behavior_loader/*.svg sim/rl/behavior_loader/*.svg
sim/rl/behavior_loader/*.pdf sim/rl/behavior_loader/*.pdf
sim/rl/runs/ sim/rl/runs/

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paper/defense/defense.nav
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paper/defense/defense.pdf
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paper/defense/defense.tex
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@@ -2,12 +2,15 @@
% Build: cd paper/defense && pdflatex defense.tex && pdflatex defense.tex % Build: cd paper/defense && pdflatex defense.tex && pdflatex defense.tex
\documentclass[aspectratio=169,11pt]{beamer} \documentclass[aspectratio=169,11pt]{beamer}
% Narrative and visual refinements for final defense delivery.
\usepackage[utf8]{inputenc} \usepackage[utf8]{inputenc}
\usepackage[T1]{fontenc} \usepackage[T1]{fontenc}
\usepackage{lmodern} \usepackage{lmodern}
\usepackage{microtype} \usepackage{microtype}
\usepackage{amsmath,amssymb} \usepackage{amsmath,amssymb}
\usepackage{graphicx} \usepackage{graphicx}
\usepackage{xspace}
\usepackage{booktabs} \usepackage{booktabs}
\usepackage{appendixnumberbeamer} \usepackage{appendixnumberbeamer}
\usepackage{hyperref} \usepackage{hyperref}
@@ -29,6 +32,7 @@
\definecolor{PhantomSlate}{HTML}{24364C} \definecolor{PhantomSlate}{HTML}{24364C}
\definecolor{PhantomCyan}{HTML}{C97A3D} \definecolor{PhantomCyan}{HTML}{C97A3D}
\definecolor{PhantomIndigo}{HTML}{2F8F8A} \definecolor{PhantomIndigo}{HTML}{2F8F8A}
\definecolor{PhantomPeach}{HTML}{EEC39C}
\setbeamercolor{normal text}{fg=PhantomSlate,bg=PhantomPaper} \setbeamercolor{normal text}{fg=PhantomSlate,bg=PhantomPaper}
\setbeamercolor{alerted text}{fg=PhantomCyan!95!black} \setbeamercolor{alerted text}{fg=PhantomCyan!95!black}
@@ -78,6 +82,32 @@
\end{tikzpicture}% \end{tikzpicture}%
} }
\newcommand{\humaniconraw}{%
\begin{tikzpicture}[x=0.9ex,y=0.9ex]
\fill[PhantomIndigo] (0,1.55) circle (0.42);
\draw[PhantomInk,line width=0.20pt,fill=PhantomSlate!95!black] (0,0.0) ellipse (0.72 and 0.56);
\end{tikzpicture}%
}
\newcommand{\roboticonraw}{%
\begin{tikzpicture}[x=0.9ex,y=0.9ex]
\draw[PhantomInk,line width=0.20pt,rounded corners=0.35ex,fill=PhantomPeach] (-0.95,-0.78) rectangle (0.95,0.72);
\draw[PhantomInk,line width=0.20pt,fill=white] (-0.42,0.08) circle (0.21);
\draw[PhantomInk,line width=0.20pt,fill=white] (0.42,0.08) circle (0.21);
\fill[PhantomInk] (-0.42,0.08) circle (0.07);
\fill[PhantomInk] (0.42,0.08) circle (0.07);
\draw[PhantomInk,line width=0.20pt] (-0.30,-0.30) -- (0.30,-0.30);
\draw[PhantomInk,line width=0.20pt] (0,0.72) -- (0,1.03);
\fill[PhantomIndigo] (0,1.15) circle (0.10);
\end{tikzpicture}%
}
\newcommand{\humanicon}{\raisebox{-0.45ex}{\humaniconraw}\xspace}
\newcommand{\roboticon}{\raisebox{-0.45ex}{\roboticonraw}\xspace}
\newcommand{\usersagentslabel}{Users \humanicon + agents \roboticon}
\newcommand{\humanagentpair}{\humanicon, \roboticon}
\newcommand{\humanagentmix}{\humanicon/\roboticon}
\begin{document} \begin{document}
{ {
@@ -129,6 +159,71 @@
\stagebar{1} \stagebar{1}
\end{frame} \end{frame}
\begin{frame}{Motivation: one everyday pricing story}
\footnotesize
\begin{columns}[T,onlytextwidth]
\column{0.53\textwidth}
\begin{block}{Imagine you sell weekend hotel rooms online}
A customer asks an assistant to scout many quotes first, then buys in a clean session at the best discovered price.
\end{block}
\begin{alertblock}{Why this matters to everyday people}
If this behavior is untreated, honest shoppers can face noisier prices and a weaker shopping experience because pricing reacts to manipulated intent signals.
\end{alertblock}
\column{0.44\textwidth}
\centering
\begin{tikzpicture}[
font=\scriptsize\sffamily,
card/.style={draw=PhantomInk,rounded corners=5pt,minimum width=3.8cm,minimum height=1.0cm,align=center},
flow/.style={-{Stealth[length=2.2mm]},thick,PhantomSlate}
]
\node[card,fill=PhantomCyan!15] (seller) at (0,1.55) {Seller posts rooms};
\node[card,fill=white] (recon) at (0,0.2) {Recon by agent \roboticon};
\node[card,fill=PhantomIndigo!12] (buy) at (0,-1.15) {Purchase by user \humanicon};
\draw[flow] (seller) -- (recon);
\draw[flow] (recon) -- (buy);
\node[font=\tiny\itshape,text=PhantomSlate] at (0,-1.95) {query and purchase split across sessions};
\end{tikzpicture}
\end{columns}
\vspace{-0.15em}
{\scriptsize\textbf{Takeaway:} protect legitimate shoppers \humanicon while detecting orchestrated recon \roboticon before pricing leakage compounds.}
\stagebar{1}
\end{frame}
\begin{frame}{Policy first: one rule maps context into price actions}
\begin{columns}[T,onlytextwidth]
\column{0.55\textwidth}
\begin{block}{Policy definition}
\[
p_t = \pi(x_t)
\]
where context \(x_t\) includes product state, time, and behavior signals from the session.
\end{block}
\begin{itemize}[<+->]
\item Behavior proxy \(\hat q\) is tracked for both user-like and agent-like sessions \((\humanagentpair)\).
\item The score \(f(\tau')\) is a soft estimate that a trajectory is agent-mediated \roboticon.
\item We see reward only for the chosen price action, which motivates a contextual-bandit view first.
\end{itemize}
\column{0.43\textwidth}
\centering
\begin{tikzpicture}[
font=\scriptsize\sffamily,
box/.style={draw=PhantomInk,rounded corners=4pt,minimum width=3.35cm,minimum height=0.85cm,align=center},
flow/.style={-{Stealth[length=2.0mm]},thick,PhantomSlate}
]
\node[box,fill=white] (ctx) at (0,1.35) {Context \(x_t\)};
\node[box,fill=PhantomIndigo!12] (pol) at (0,0.15) {Policy \(\pi\)};
\node[box,fill=PhantomCyan!15] (act) at (0,-1.05) {Price action \(p_t\)};
\draw[flow] (ctx) -- (pol);
\draw[flow] (pol) -- (act);
\node[font=\tiny\itshape,text=PhantomSlate] at (0,-1.75) {later extended from contextual bandits to DR-RL};
\end{tikzpicture}
\end{columns}
\stagebar{1}
\end{frame}
\begin{frame}{Agentic recon creates direct financial pressure on pricing power} \begin{frame}{Agentic recon creates direct financial pressure on pricing power}
\centering \centering
\begin{tikzpicture}[ \begin{tikzpicture}[
@@ -138,44 +233,101 @@
] ]
\path[use as bounding box] (-6.1,-1.25) rectangle (6.1,2.25); \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) \node<1->[flow,fill=PhantomCyan!18] (recon) at (-3.1,1.1)
{\textbf{Recon session}\\samples multiple quotes}; {\textbf{Recon session \roboticon}\\samples multiple quotes};
\node<2->[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}; {\textbf{Clean execution session \humanicon}\\buys using the best found quote};
\draw<2->[-{Stealth[length=3mm]},ultra thick,PhantomSlate] (recon.east) -- (buy.west); \draw<2->[-{Stealth[length=3mm]},ultra thick,PhantomSlate] (recon.east) -- (buy.west);
\node<2->[font=\scriptsize\bfseries,text=PhantomSlate] at (0,1.98) \node<2->[font=\scriptsize\bfseries,text=PhantomSlate] at (0,1.98)
{query and purchase are decoupled}; {query and purchase are decoupled};
\draw<3->[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); (recon.south east) .. controls +(1.15,-0.95) and +(-1.15,-0.95) .. (buy.south west);
\node<3->[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.}; {The platform sees behavior proxy $\hat q$ (\humanagentpair), while true demand response $d(p\mid\theta)$ stays latent.};
\end{tikzpicture} \end{tikzpicture}
\vspace{0.25em} \vspace{0.05em}
\begin{tikzpicture}[font=\scriptsize\sffamily, \begin{columns}[T,onlytextwidth]
card/.style={draw=PhantomInk,rounded corners=4pt,minimum width=3.85cm,text width=3.55cm,minimum height=1.4cm,align=center}] \column{0.31\textwidth}
\path[use as bounding box] (-6.05,-0.95) rectangle (6.05,0.95); \uncover<4->{%
\node<4->[card,fill=PhantomInk,text=white] at (-4.05,0) \centering
{\large$\mathrm{COI}(\pi)=\mathbb{E}[P]-\underline p$\\[-0.05em]\footnotesize pricing power KPI}; \begin{tikzpicture}[font=\scriptsize\sffamily]
\node<5->[card,fill=PhantomCyan!16,text=PhantomSlate] at (0,0) \node[draw=PhantomInk,rounded corners=4pt,fill=PhantomInk,text=white,minimum width=0.97\linewidth,text width=0.84\linewidth,minimum height=1.2cm,align=center]
{\Large\bfseries$-9{,}014$\\[-0.05em]\footnotesize revenue units\\per +0.1 contamination}; {\large$\mathrm{COI}(\pi)=\mathbb{E}[P]-\underline p$\\[-0.05em]\footnotesize pricing power KPI};
\node<6->[card,fill=PhantomIndigo!12,text=PhantomSlate] at (4.05,0) \end{tikzpicture}%
{\large$\lim_{N\to\infty}\mathrm{COI}=0$\\[-0.05em]\footnotesize theorem as intuition guide}; }
\end{tikzpicture}
\vspace{0.15em} \column{0.31\textwidth}
\uncover<6->{\scriptsize\textit{The theorem is used to guide intuition: independent reconnaissance pressure can drive realizable prices toward the floor.}}\\[0.2em] \uncover<5->{%
\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.} \centering
\begin{tikzpicture}[font=\scriptsize\sffamily]
\node[draw=PhantomInk,rounded corners=4pt,fill=PhantomIndigo!12,text=PhantomSlate,minimum width=0.97\linewidth,text width=0.84\linewidth,minimum height=1.2cm,align=center]
{\large$\lim_{N\to\infty}\mathrm{COI}=0$\\[-0.05em]\footnotesize theorem as intuition guide};
\end{tikzpicture}%
}
\column{0.34\textwidth}
\uncover<6->{%
\centering
\begin{tikzpicture}[x=0.67cm,y=0.85cm,font=\scriptsize\sffamily]
\draw[->,thick,PhantomSlate] (0,0) -- (4.2,0) node[right] {queries $N$};
\draw[->,thick,PhantomSlate] (0,0) -- (0,2.05) node[above] {COI};
\draw[very thick,PhantomCyan!95!black] (0.25,1.8) .. controls (1.2,1.35) and (2.25,0.62) .. (4.0,0.16);
\draw[dashed,PhantomInk!65] (0,0.16) -- (4.0,0.16);
\node[anchor=west,font=\tiny,text=PhantomSlate] at (2.35,0.28) {price-floor proximity};
\end{tikzpicture}%
}
\end{columns}
\vspace{-0.1em}
\uncover<6->{\scriptsize\textit{The theorem gives direction, not prophecy: more independent recon pressure pushes realizable prices toward the floor.}}\\[0.1em]
\uncover<7->{\scriptsize\textbf{Implication:} when quote discovery and purchase split, session-based pricing can overestimate willingness to pay.}
\stagebar{1} \stagebar{1}
\end{frame} \end{frame}
\begin{frame}{The thesis answers one chain: mechanism \(\to\) signal \(\to\) control} \begin{frame}{The thesis answers one chain: mechanism \(\to\) signal \(\to\) control}
\begin{enumerate}[<+->]\setlength{\itemsep}{0.45em} \begin{columns}[T,onlytextwidth]
\item Can agent and human sessions be reliably distinguished from behavioral interaction signals alone? \column{0.32\textwidth}
\item How do agents affect prices and revenue that can be generated? \centering
\item Can we make new policies that maintain margin in agent contaminated systems? \begin{tikzpicture}[font=\scriptsize\sffamily]
\end{enumerate} \draw[rounded corners=4pt,draw=PhantomInk,fill=white] (-1.55,-1.1) rectangle (1.55,1.2);
\fill[PhantomCyan!85!black] (-0.75,0.35) circle (0.14);
\fill[PhantomCyan!85!black] (-0.45,0.70) circle (0.14);
\fill[PhantomCyan!85!black] (-0.15,0.45) circle (0.14);
\fill[PhantomIndigo!85!black] (0.35,-0.20) circle (0.14);
\fill[PhantomIndigo!85!black] (0.65,-0.45) circle (0.14);
\fill[PhantomIndigo!85!black] (0.95,-0.15) circle (0.14);
\draw[dashed,PhantomInk!60] (0.12,-0.92) -- (0.12,1.0);
\node[text=PhantomSlate,font=\tiny] at (0,-0.93) {behavior separability};
\end{tikzpicture}
{\footnotesize\textbf{SQ1}}\\[-0.15em]
{\scriptsize Can we distinguish \humanicon and \roboticon sessions from interactions alone?}
\vspace{0.35em} \column{0.32\textwidth}
\centering
\begin{tikzpicture}[font=\scriptsize\sffamily]
\draw[rounded corners=4pt,draw=PhantomInk,fill=white] (-1.55,-1.1) rectangle (1.55,1.2);
\draw[->,thick,PhantomSlate] (-1.2,-0.75) -- (1.2,-0.75);
\draw[->,thick,PhantomSlate] (-1.2,-0.75) -- (-1.2,0.85);
\draw[very thick,PhantomCyan!95!black] (-1.0,0.62) .. controls (-0.4,0.2) and (0.3,-0.18) .. (1.0,-0.58);
\node[text=PhantomSlate,font=\tiny] at (0,-0.95) {COI / revenue pressure};
\end{tikzpicture}
{\footnotesize\textbf{SQ2}}\\[-0.15em]
{\scriptsize How strong is price and revenue erosion under agentic contamination?}
\column{0.32\textwidth}
\centering
\begin{tikzpicture}[font=\scriptsize\sffamily]
\draw[rounded corners=4pt,draw=PhantomInk,fill=white] (-1.55,-1.1) rectangle (1.55,1.2);
\draw[thick,fill=PhantomIndigo!20,draw=PhantomInk] (0,0.82) -- (0.98,0.32) -- (0.98,-0.44) -- (0,-0.90) -- (-0.98,-0.44) -- (-0.98,0.32) -- cycle;
\draw[thick,PhantomInk] (0,-0.46) -- (0,0.38);
\draw[thick,PhantomInk] (0,-0.46) -- (0.42,-0.08);
\node[text=PhantomSlate,font=\tiny] at (0,-0.95) {robust policy control};
\end{tikzpicture}
{\footnotesize\textbf{SQ3}}\\[-0.15em]
{\scriptsize Can policy design recover margin while keeping UX stable?}
\end{columns}
\vspace{0.2em}
\stagebar{1} \stagebar{1}
\end{frame} \end{frame}
@@ -188,12 +340,12 @@
box/.style={draw=PhantomInk,rounded corners=3pt,minimum width=2.5cm,minimum height=0.9cm,align=center}, box/.style={draw=PhantomInk,rounded corners=3pt,minimum width=2.5cm,minimum height=0.9cm,align=center},
arr/.style={-{Stealth[length=2.2mm]},thick,PhantomSlate} arr/.style={-{Stealth[length=2.2mm]},thick,PhantomSlate}
] ]
\node[box,fill=PhantomCyan!14] (actors) at (0,1.45) {Humans + Agents}; \node[box,fill=PhantomCyan!14] (actors) at (0,1.45) {\usersagentslabel};
\node[box,fill=white] (web) at (2.9,1.45) {Next.js\\storefront}; \node[box,fill=white] (web) at (2.9,1.45) {Web\\storefront};
\node[box,fill=white] (provider) at (5.8,1.45) {Pricing\\provider}; \node[box,fill=white] (provider) at (5.8,1.45) {Pricing\\service};
\node[box,fill=white] (redis) at (8.7,1.45) {Redis\\serve layer}; \node[box,fill=white] (redis) at (8.7,1.45) {Serve\\cache};
\node[box,fill=PhantomIndigo!10,minimum width=3.1cm] (kafka) at (4.35,-0.15) {Kafka topics\\behavior + price logs}; \node[box,fill=PhantomIndigo!10,minimum width=3.1cm] (kafka) at (4.35,-0.15) {Event stream\\behavior + quote logs};
\node[box,fill=PhantomCyan!10,minimum width=2.8cm] (airflow) at (8.0,-0.15) {Airflow + worker\\batch updates}; \node[box,fill=PhantomCyan!10,minimum width=2.8cm] (airflow) at (8.0,-0.15) {Offline trainer\\batch updates};
\draw[arr] (actors) -- (web); \draw[arr] (actors) -- (web);
\draw[arr] (web) -- (provider); \draw[arr] (web) -- (provider);
@@ -357,7 +509,7 @@
\vspace{0.25em} \vspace{0.25em}
\begin{itemize} \begin{itemize}
\item<3-> The signed gap $g(\tau')$ is positive when a session is closer to agent behavior. \item<3-> The signed gap $g(\tau')$ is positive when a session is closer to agent behavior \roboticon (vs. human reference \humanicon).
\item<4-> Temperature $T$ calibrates how sharply the score moves away from uncertainty. \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<6-> Continuous scoring is used to steer contamination-aware pricing.
\item<7-> The design target is guidance, not a hard user-level ban decision. \item<7-> The design target is guidance, not a hard user-level ban decision.
@@ -466,7 +618,7 @@
\vspace{0.25em} \vspace{0.25em}
\begin{itemize} \begin{itemize}
\item<2-> Baseline experiments use a query-tax leakage surrogate for tractability. \item<2-> Baseline experiments use a query-tax leakage surrogate where higher $f(\tau')$ \roboticon increases leakage penalty.
\item<3-> Supra-competitive anchor penalties are tracked as an additional safety rail. \item<3-> Supra-competitive anchor penalties are tracked as an additional safety rail.
\end{itemize} \end{itemize}
\stagebar{4} \stagebar{4}

72
paper/defense/defense_appendix.tex
View File

@@ -30,9 +30,9 @@
\scriptsize \scriptsize
\begin{align*} \begin{align*}
\tau_s &= (e_{s,1},\ldots,e_{s,L_s}) && \text{session} \\ \tau_s &= (e_{s,1},\ldots,e_{s,L_s}) && \text{session} \\
\hat{q}_{t,i} &= \sum_{s\in S_t}\sum_k \omega(a_{s,k})\,\mathbf{1}[i_{s,k}=i] && \text{proxy} \\ \hat{q}_{t,i} &= \sum_{s\in S_t}\sum_k \omega(a_{s,k})\,\mathbf{1}[i_{s,k}=i] && \text{proxy }(\humanagentpair) \\
Q(p) &= (1-\alpha)\,\mathbb{E}_{\theta\sim D_H}[d(p;\theta)] \\ Q(p) &= (1-\alpha)\,\mathbb{E}_{\theta\sim D_H}[d(p;\theta)] \\
&\quad + \alpha\,\mathbb{E}_{\theta\sim D_A}[d(p;\theta)] + \epsilon_t && \text{mixture} \\ &\quad + \alpha\,\mathbb{E}_{\theta\sim D_A}[d(p;\theta)] + \epsilon_t && \text{mixture of }\humanagentmix \\
\mathrm{COI}(\pi) &= \mathbb{E}[P]-\underline{p} && \text{COI} \mathrm{COI}(\pi) &= \mathbb{E}[P]-\underline{p} && \text{COI}
\end{align*} \end{align*}
\end{frame} \end{frame}
@@ -63,7 +63,7 @@
\hat{q}_{t,i}=\sum_{s\in S_t}\sum_{k=1}^{L_s} \omega(a_{s,k})\,\mathbf{1}[i_{s,k}=i] \hat{q}_{t,i}=\sum_{s\in S_t}\sum_{k=1}^{L_s} \omega(a_{s,k})\,\mathbf{1}[i_{s,k}=i]
\] \]
\begin{alertblock}{Key distinction} \begin{alertblock}{Key distinction}
\(\hat{q}\) is an operational sensor from logs; true demand \(d(p;\theta)\) stays latent. Pricing reacts to \(\hat{q}\), so agent-shaped behavior poisons the signal. \(\hat{q}\) is an operational sensor from logs (\humanagentpair); true demand \(d(p;\theta)\) stays latent. Pricing reacts to \(\hat{q}\), so agent-shaped behavior can poison the signal.
\end{alertblock} \end{alertblock}
\end{frame} \end{frame}
@@ -286,21 +286,63 @@
\end{alertblock} \end{alertblock}
\end{frame} \end{frame}
\begin{frame}{Appendix: Stackelberg timing (words)} \begin{frame}{Appendix: why a Stackelberg game is a useful abstraction}
\begin{itemize} \footnotesize
\item Leader: platform sets price vector given current state and policy. \begin{columns}[T,onlytextwidth]
\item Follower: demand proxy updates from simulated trajectories drawn from \(\mathcal{G}(\alpha)\) and kernels \((\hat{T}_H,\hat{T}_A)\). \column{0.52\textwidth}
\item \textbf{Limbo} buffer stores alternating moves for a clean game history; relaxing strict alternation is listed future work. \begin{itemize}
\end{itemize} \item \textbf{Leader move}: the platform commits a quote via policy \(p_t=\pi(x_t)\).
\item \textbf{Follower move}: session behavior then reacts (click, continue, abandon, purchase).
\item This ordering matches real serving APIs: price is emitted before response is observed.
\item Repeating this local sequence gives a tractable leader-follower control model.
\end{itemize}
\column{0.44\textwidth}
\centering
\begin{tikzpicture}[
font=\scriptsize\sffamily,
box/.style={draw=PhantomInk,rounded corners=4pt,minimum width=3.45cm,minimum height=0.9cm,align=center},
arr/.style={-{Stealth[length=2.0mm]},thick,PhantomSlate}
]
\node[box,fill=PhantomCyan!14] (l) at (0,1.2) {Leader: pricing policy};
\node[box,fill=white] (f) at (0,-0.05) {Follower: session response};
\node[box,fill=PhantomIndigo!10] (u) at (0,-1.3) {State update \& next round};
\draw[arr] (l) -- node[right,font=\tiny] {quote} (f);
\draw[arr] (f) -- node[right,font=\tiny] {events} (u);
\draw[arr] (u.west) to[bend left=35] node[left,font=\tiny] {context} (l.west);
\end{tikzpicture}
\end{columns}
\begin{alertblock}{Boundary}
We do \textbf{not} claim a full market equilibrium. We claim a useful timing model for explainable policy updates under contamination.
\end{alertblock}
\end{frame} \end{frame}
\begin{frame}{Appendix: three layers of evidence} \begin{frame}{Appendix: why Theorem 1 helps (without over-claiming)}
\footnotesize \footnotesize
\begin{description} \begin{columns}[T,onlytextwidth]
\item[Theorem 1] Formal COI erosion under independence and fixed-offer assumptions. \column{0.48\textwidth}
\item[Simulator] Dynamic, adaptive pricing and contamination sweeps (different status). \begin{block}{What the theorem gives us}
\item[Implementation] Local-$\alpha$ robust training; spirit of DRO without claiming a full numerical Wasserstein solver. \begin{itemize}
\end{description} \item A directional mechanism: independent recon pressure compresses COI.
\item A sanity check for reward design: leakage penalties should grow with recon likelihood.
\item A clean explanatory anchor for stakeholders and governance review.
\end{itemize}
\end{block}
\column{0.48\textwidth}
\begin{alertblock}{What the theorem does not claim}
\begin{itemize}
\item It is not a finite-sample forecast for every market.
\item It does not cover collusion or all adaptive adversaries.
\item It does not replace simulator evidence or offline policy validation.
\end{itemize}
\end{alertblock}
\end{columns}
\vspace{0.2em}
\begin{block}{Three evidence layers used in this thesis}
\textbf{Theorem 1} (mechanism direction) \(\rightarrow\) \textbf{simulator} (finite-regime quantification) \(\rightarrow\) \textbf{implementation} (local robust policy training).
\end{block}
\end{frame} \end{frame}
\begin{frame}{Appendix: composite strip (five plots, small multiples)} \begin{frame}{Appendix: composite strip (five plots, small multiples)}

4
scripts/nx_research.sh
View File

@@ -120,8 +120,8 @@ case "$cmd" in
python3 - <<'PY' python3 - <<'PY'
from pathlib import Path from pathlib import Path
skip = {"node_modules", ".venv", "venv"} skip = {"node_modules", ".venv", "venv", ".venv-ray"}
exts = {".ts", ".py"} exts = {".ts", ".py", ".ipynb"}
total = 0 total = 0
for path in Path(".").rglob("*"): for path in Path(".").rglob("*"):
if not path.is_file() or path.suffix not in exts or any(part in skip for part in path.parts): if not path.is_file() or path.suffix not in exts or any(part in skip for part in path.parts):