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735 lines
30 KiB
TeX
735 lines
30 KiB
TeX
% Final thesis defense (PHANTOM)
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% Build: cd paper/defense && pdflatex defense.tex && pdflatex defense.tex
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\documentclass[aspectratio=169,11pt]{beamer}
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% Narrative and visual refinements for final defense delivery.
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\usepackage[utf8]{inputenc}
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\usepackage[T1]{fontenc}
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\usepackage{lmodern}
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\usepackage{microtype}
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\usepackage{amsmath,amssymb}
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\usepackage{graphicx}
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\usepackage{xspace}
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\usepackage{booktabs}
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\usepackage{appendixnumberbeamer}
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\usepackage{hyperref}
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\usepackage{tikz}
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\usetikzlibrary{arrows.meta,calc,positioning,fit,shapes.geometric,shapes.misc}
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\graphicspath{{../src/chapters/figures/results/generated/final/plots/}{../src/chapters/}}
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\usetheme[
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progressbar=frametitle,
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]{moloch}
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\molochset{sectionpage=none,subsectionpage=none}
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\usefonttheme{professionalfonts}
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\setbeamertemplate{frame numbering}[fraction]
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% Palette
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\hypersetup{colorlinks=true,urlcolor=PhantomCyan!40!white,linkcolor=PhantomPaper}
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\title{PHANTOM}
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\subtitle{Pricing Heuristics Against Non-human Transaction Orchestration Mechanisms}
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\author{Daniel Rösel}
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\institute{IE University, Madrid \\ Supervisor: Alberto Martín Izquierdo}
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\date{\today}
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\titlegraphic{%
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\begin{tikzpicture}
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\shade[left color=PhantomCyan,right color=PhantomIndigo] (0,0) rectangle (0.55\paperwidth,0.06);
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\end{tikzpicture}%
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}
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\newcommand{\stagebar}[1]{}
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\newcommand{\metriccard}[2]{%
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\begin{tikzpicture}
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\node[
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draw=PhantomInk,
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rounded corners=3pt,
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fill=PhantomCyan!10,
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minimum width=3.05cm,
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minimum height=1.25cm,
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align=center
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] {\Large\bfseries #1\\[-0.2em]{\scriptsize #2}};
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\end{tikzpicture}%
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}
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\newcommand{\humaniconraw}{%
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\begin{tikzpicture}[x=0.9ex,y=0.9ex]
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\end{tikzpicture}%
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}
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\newcommand{\roboticonraw}{%
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}
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\newcommand{\humanicon}{\raisebox{-0.45ex}{\humaniconraw}\xspace}
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\newcommand{\roboticon}{\raisebox{-0.45ex}{\roboticonraw}\xspace}
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\newcommand{\usersagentslabel}{Users \humanicon + agents \roboticon}
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\newcommand{\humanagentpair}{\humanicon, \roboticon}
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\newcommand{\humanagentmix}{\humanicon/\roboticon}
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\tikzset{every node/.append style={text=PhantomSlate}}
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\begin{document}
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{
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\setbeamercolor{background canvas}{bg=PhantomInk}
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\begin{frame}[plain]
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\vfill
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\centering
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{\color{white}\Huge\bfseries PHANTOM\par}
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\vspace{0.6em}
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{\color{PhantomCyan}\rule{0.45\paperwidth}{0.06cm}\par}
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\vspace{0.8em}
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{\large\color{white!90!black}Pricing heuristics against non-human transaction orchestration\par}
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\vfill
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{\color{white!75!black}\normalsize Daniel Rösel\par}
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{\color{white!65!black}\small IE University \textbullet\ Supervisor: Alberto Martín Izquierdo\par}
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\vspace{1.2em}
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{\footnotesize\color{PhantomCyan!80!white}\href{https://velocitatem.github.io/PHANTOM/}{\texttt{velocitatem.github.io/PHANTOM}}}
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\vfill
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\end{frame}
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}
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\begin{frame}{Roadmap: one argument in six stages (15 min)}
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\centering
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\begin{tikzpicture}[
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font=\scriptsize\sffamily,
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stage/.style={draw=PhantomInk,rounded corners=3pt,fill=PhantomCyan!10,minimum width=1.95cm,minimum height=1.05cm,align=center},
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flow/.style={-{Stealth[length=2.0mm,width=1.8mm]},line width=1pt,PhantomSlate}
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]
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\node[stage,fill=PhantomCyan!14] (intro) {Intro\\2m};
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\node[stage,right=0.30cm of intro] (platform) {Platform\\4m};
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\node[stage,right=0.30cm of platform] (signal) {Signal\\4m};
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\node[stage,right=0.30cm of signal] (drrl) {DR-RL\\4m};
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\node[stage,right=0.30cm of drrl] (results) {Results\\1m};
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\node[stage,right=0.30cm of results] (close) {Close};
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\draw[flow,shorten <=2pt,shorten >=2pt] (intro.east) -- (platform.west);
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\draw[flow,shorten <=2pt,shorten >=2pt] (platform.east) -- (signal.west);
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\draw[flow,shorten <=2pt,shorten >=2pt] (signal.east) -- (drrl.west);
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\draw[flow,shorten <=2pt,shorten >=2pt] (drrl.east) -- (results.west);
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\draw[flow,shorten <=2pt,shorten >=2pt] (results.east) -- (close.west);
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\end{tikzpicture}
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\vspace{0.75em}
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\begin{block}{Main research question}
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How can dynamic pricing preserve margin integrity when transactions are increasingly mediated by non-human agents?
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\end{block}
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\vspace{0.35em}
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{\footnotesize Dynamic pricing has often been treated as a secondary optimization layer; agent-mediated shopping turns it into a primary margin-risk surface.}
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\stagebar{1}
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\end{frame}
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\begin{frame}{Motivation: one everyday pricing story}
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\footnotesize
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\begin{columns}[T,onlytextwidth]
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\column{0.53\textwidth}
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\begin{block}{Imagine you sell weekend hotel rooms online}
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A customer asks an assistant to scout many quotes first, then buys in a clean session at the best discovered price.
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\end{block}
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\begin{alertblock}{Why this matters to everyday people}
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If this behavior is untreated, honest shoppers can face noisier prices and a weaker shopping experience because pricing reacts to manipulated intent signals.
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\end{alertblock}
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\column{0.44\textwidth}
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\centering
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\begin{tikzpicture}[
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font=\scriptsize\sffamily,
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card/.style={draw=PhantomInk,rounded corners=5pt,minimum width=3.8cm,minimum height=1.0cm,align=center},
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flow/.style={-{Stealth[length=2.2mm]},thick,PhantomSlate}
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]
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\node[card,fill=PhantomCyan!15] (seller) at (0,1.55) {Seller posts rooms};
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\node[card,fill=white] (recon) at (0,0.2) {Recon by agent \roboticon};
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\node[card,fill=PhantomIndigo!12] (buy) at (0,-1.15) {Purchase by user \humanicon};
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\draw[flow] (seller) -- (recon);
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\draw[flow] (recon) -- (buy);
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\node[font=\tiny\itshape,text=PhantomSlate] at (0,-1.95) {query and purchase split across sessions};
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\end{tikzpicture}
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\end{columns}
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\vspace{-0.15em}
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{\scriptsize\textbf{Takeaway:} protect legitimate shoppers \humanicon while detecting orchestrated recon \roboticon before pricing leakage compounds.}
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\stagebar{1}
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\end{frame}
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\begin{frame}{Policy first: one rule maps context into price actions}
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\begin{columns}[T,onlytextwidth]
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\column{0.55\textwidth}
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\begin{block}{Policy definition}
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\[
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p_t = \pi(x_t)
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\]
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where context \(x_t\) includes product state, time, and behavior signals from the session.
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\end{block}
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\begin{itemize}[<+->]
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\item Behavior proxy \(\hat q\) is tracked for both user-like and agent-like sessions \((\humanagentpair)\).
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\item The score \(f(\tau')\) is a soft estimate that a trajectory is agent-mediated \roboticon.
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\item We see reward only for the chosen price action, which motivates a contextual-bandit view first.
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\end{itemize}
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\column{0.43\textwidth}
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\centering
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\begin{tikzpicture}[
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font=\scriptsize\sffamily,
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box/.style={draw=PhantomInk,rounded corners=4pt,minimum width=3.35cm,minimum height=0.85cm,align=center},
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flow/.style={-{Stealth[length=2.0mm]},thick,PhantomSlate}
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]
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\node[box,fill=white] (ctx) at (0,1.35) {Context \(x_t\)};
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\node[box,fill=PhantomIndigo!12] (pol) at (0,0.15) {Policy \(\pi\)};
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\node[box,fill=PhantomCyan!15] (act) at (0,-1.05) {Price action \(p_t\)};
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\draw[flow] (ctx) -- (pol);
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\draw[flow] (pol) -- (act);
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\node[font=\tiny\itshape,text=PhantomSlate] at (0,-1.75) {later extended from contextual bandits to DR-RL};
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\end{tikzpicture}
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\end{columns}
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\stagebar{1}
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\end{frame}
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\begin{frame}{Agentic recon creates direct financial pressure on pricing power}
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\centering
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\begin{tikzpicture}[
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font=\small\sffamily,
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flow/.style={draw=PhantomInk,rounded corners=6pt,minimum width=5.3cm,minimum height=1.25cm,align=center},
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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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]
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\path[use as bounding box] (-6.1,-1.25) rectangle (6.1,2.25);
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\node<1->[flow,fill=PhantomCyan!18] (recon) at (-3.1,1.1)
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{\textbf{Recon session \roboticon}\\samples multiple quotes};
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\node<2->[flow,fill=PhantomIndigo!16] (buy) at (3.1,1.1)
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{\textbf{Clean execution session \humanicon}\\buys using the best found quote};
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\draw<2->[-{Stealth[length=3mm]},ultra thick,PhantomSlate] (recon.east) -- (buy.west);
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\node<2->[font=\scriptsize\bfseries,text=PhantomSlate] at (0,1.98)
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{query and purchase are decoupled};
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\draw<3->[densely dashed,thick,PhantomCyan!90!black]
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(recon.south east) .. controls +(1.15,-0.95) and +(-1.15,-0.95) .. (buy.south west);
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\node<3->[note] at (0,-0.65)
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{The platform sees behavior proxy $\hat q$ (\humanagentpair), while true demand response $d(p\mid\theta)$ stays latent.};
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\end{tikzpicture}
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\vspace{0.05em}
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\begin{columns}[T,onlytextwidth]
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\column{0.31\textwidth}
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\uncover<4->{%
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\centering
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\begin{tikzpicture}[font=\scriptsize\sffamily]
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\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]
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{\large$\mathrm{COI}(\pi)=\mathbb{E}[P]-\underline p$\\[-0.05em]\footnotesize pricing power KPI};
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\end{tikzpicture}%
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}
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\column{0.31\textwidth}
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\uncover<5->{%
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\centering
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\begin{tikzpicture}[font=\scriptsize\sffamily]
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\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]
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{\large$\lim_{N\to\infty}\mathrm{COI}=0$\\[-0.05em]\footnotesize theorem as intuition guide};
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\end{tikzpicture}%
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}
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\column{0.34\textwidth}
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\uncover<6->{%
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\centering
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\begin{tikzpicture}[x=0.67cm,y=0.85cm,font=\scriptsize\sffamily]
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\draw[->,thick,PhantomSlate] (0,0) -- (4.2,0) node[right] {queries $N$};
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\draw[->,thick,PhantomSlate] (0,0) -- (0,2.05) node[above] {COI};
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\draw[very thick,PhantomCyan!95!black] (0.25,1.8) .. controls (1.2,1.35) and (2.25,0.62) .. (4.0,0.16);
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\draw[dashed,PhantomInk!65] (0,0.16) -- (4.0,0.16);
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\node[anchor=west,font=\tiny,text=PhantomSlate] at (2.35,0.28) {price-floor proximity};
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\end{tikzpicture}%
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}
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\end{columns}
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\vspace{-0.1em}
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\uncover<6->{\scriptsize\textit{The theorem gives direction, not prophecy: more independent recon pressure pushes realizable prices toward the floor.}}\\[0.1em]
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\uncover<7->{\scriptsize\textbf{Implication:} when quote discovery and purchase split, session-based pricing can overestimate willingness to pay.}
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\stagebar{1}
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\end{frame}
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\begin{frame}{The thesis answers one chain: mechanism \(\to\) signal \(\to\) control}
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\begin{columns}[T,onlytextwidth]
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\column{0.32\textwidth}
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\centering
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\begin{tikzpicture}[font=\scriptsize\sffamily]
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\draw[rounded corners=4pt,draw=PhantomInk,fill=white] (-1.55,-1.1) rectangle (1.55,1.2);
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\fill[PhantomCyan!85!black] (-0.75,0.35) circle (0.14);
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\fill[PhantomCyan!85!black] (-0.45,0.70) circle (0.14);
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\fill[PhantomCyan!85!black] (-0.15,0.45) circle (0.14);
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\fill[PhantomIndigo!85!black] (0.35,-0.20) circle (0.14);
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\fill[PhantomIndigo!85!black] (0.65,-0.45) circle (0.14);
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\fill[PhantomIndigo!85!black] (0.95,-0.15) circle (0.14);
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\draw[dashed,PhantomInk!60] (0.12,-0.92) -- (0.12,1.0);
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\node[text=PhantomSlate,font=\tiny] at (0,-0.93) {behavior separability};
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\end{tikzpicture}
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{\footnotesize\textbf{SQ1}}\\[-0.15em]
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{\scriptsize Can we distinguish \humanicon and \roboticon sessions from interactions alone?}
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\column{0.32\textwidth}
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\centering
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\begin{tikzpicture}[font=\scriptsize\sffamily]
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\draw[rounded corners=4pt,draw=PhantomInk,fill=white] (-1.55,-1.1) rectangle (1.55,1.2);
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\draw[->,thick,PhantomSlate] (-1.2,-0.75) -- (1.2,-0.75);
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\draw[->,thick,PhantomSlate] (-1.2,-0.75) -- (-1.2,0.85);
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\draw[very thick,PhantomCyan!95!black] (-1.0,0.62) .. controls (-0.4,0.2) and (0.3,-0.18) .. (1.0,-0.58);
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\node[text=PhantomSlate,font=\tiny] at (0,-0.95) {COI / revenue pressure};
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\end{tikzpicture}
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{\footnotesize\textbf{SQ2}}\\[-0.15em]
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{\scriptsize How strong is price and revenue erosion under agentic contamination?}
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\column{0.32\textwidth}
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\centering
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\begin{tikzpicture}[font=\scriptsize\sffamily]
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\draw[rounded corners=4pt,draw=PhantomInk,fill=white] (-1.55,-1.1) rectangle (1.55,1.2);
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\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;
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\draw[thick,PhantomInk] (0,-0.46) -- (0,0.38);
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\draw[thick,PhantomInk] (0,-0.46) -- (0.42,-0.08);
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\node[text=PhantomSlate,font=\tiny] at (0,-0.95) {robust policy control};
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\end{tikzpicture}
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{\footnotesize\textbf{SQ3}}\\[-0.15em]
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{\scriptsize Can policy design recover margin while keeping UX stable?}
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\end{columns}
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\vspace{0.2em}
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\stagebar{1}
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\end{frame}
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\section{Platform Development}
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\begin{frame}{Stage 1: We built a dual-loop platform to observe behavior and price exposure together}
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\centering
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\begin{tikzpicture}[
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font=\scriptsize\sffamily,
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box/.style={draw=PhantomInk,rounded corners=3pt,minimum width=2.5cm,minimum height=0.9cm,align=center},
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arr/.style={-{Stealth[length=2.2mm]},thick,PhantomSlate}
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]
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\node[box,fill=PhantomCyan!14] (actors) at (0,1.45) {\usersagentslabel};
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\node[box,fill=white] (web) at (2.9,1.45) {Web\\storefront};
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\node[box,fill=white] (provider) at (5.8,1.45) {Pricing\\service};
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\node[box,fill=white] (redis) at (8.7,1.45) {Serve\\cache};
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\node[box,fill=PhantomIndigo!10,minimum width=3.1cm] (kafka) at (4.35,-0.15) {Event stream\\behavior + quote logs};
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\node[box,fill=PhantomCyan!10,minimum width=2.8cm] (airflow) at (8.0,-0.15) {Offline trainer\\batch updates};
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\draw[arr] (actors) -- (web);
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\draw[arr] (web) -- (provider);
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\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},
|
|
body/.style={anchor=west,text width=6.0cm,align=left,font=\scriptsize}
|
|
]
|
|
\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,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=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},
|
|
arr/.style={-{Stealth[length=2.2mm]},thick,PhantomSlate}
|
|
]
|
|
\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};
|
|
\node[draw=PhantomInk,rounded corners=3pt,fill=white,minimum width=3.9cm,minimum height=0.85cm] (b) at (0,0.25) {view\_item\_page};
|
|
\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};
|
|
\draw[-{Stealth[length=2.2mm]},thick,PhantomSlate] (a) -- node[right,font=\tiny]{0.64} (b);
|
|
\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 \roboticon (vs. human reference \humanicon).
|
|
\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 where higher $f(\tau')$ \roboticon increases leakage penalty.
|
|
\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}
|