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* updated outline for paper from issue * extra paper sections and some formalization of series data * algorithms and acknowledgements
69 lines
3.1 KiB
TeX
69 lines
3.1 KiB
TeX
\section{Methodology}
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\subsection{Problem Formalization}
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Mathematical formalization of agent-induced pricing distortions. Formal definition of potential loss mechanisms $\alpha D$
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We consider a business across time during which we have an evolving vector $p_t \in \Re^N$ where $N$ is the number of products in our catalogue. our price vector is directly dependent on a demand function $q_t$ which we define as a linear method of a price elasticity matrix $B_t$. This is the same setup that Microsoft created in their research.
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We gether interaction data from users interacting with a sample platform simulating a hotel/airline which generates interaction distributions $I_t = \{(p_t, q_t^\text{obs}, \pi_t)\}_{t=1}^T$
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\subsection{Cost of Information Framework}
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Mathematical demonstration and validation of the COI and citation backed evidence, and framework overview + show harm to user via other cost distortions. Maybe split into 3.2.1 (COI Theory) and 3.2.2 (Framework Design)
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\subsection{System Architecture}
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\begin{figure}[ht]
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\centering
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\begin{tikzpicture}[
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node distance=1.5cm and 2.5cm,
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box/.style={rectangle, draw, thick, minimum height=1cm, minimum width=3cm, align=center, fill=blue!10},
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kafka/.style={rectangle, draw=orange, thick, minimum height=1cm, minimum width=3cm, align=center, fill=orange!15},
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arrow/.style={thick,->,>=Stealth}
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]
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% Nodes
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\node[box] (webapp) {Web Application \\ (Producer \& Consumer)};
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\node[kafka, below=of webapp] (kafka) {Apache Kafka \\ Cluster};
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\node[box, below=of kafka] (backend) {Backend Services / Microservices \\ (Producers and Consumers)};
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% Connections
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\draw[arrow] (webapp) to[out=210,in=150] node[above]{Publish} (kafka);
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\draw[arrow] (kafka) to[out=50,in=330] node[below]{Consume} (webapp);
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\draw[arrow] (backend) -- node[above]{Publish/Consume} (kafka);
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% Optional: Kafka internal components
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%\node[below=0.7cm of kafka, align=center] (topics) {Topics \\ Partitions};
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% Optional background
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\begin{scope}[on background layer]
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\node[draw, rounded corners, fill=orange!5, fit=(kafka), inner sep=0.3cm] {};
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\end{scope}
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\end{tikzpicture}
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\caption{Technical Diagram}
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\end{figure}
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High level overview of how it works
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\subsection{Experimental Design}
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Study methodology and approach. Data acquisition strategy. Defined objectives and success criteria. Observable metrics and KPIs
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\subsection{Dynamic Pricing Algorithm Analysis}
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Deep dive into how the algorithm works, different kinds and justification for chosen appraoches + agent impact modeling and quantification.
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\subsection{Reinforcement Learning Formulation}
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How do we define the state space, action space and reward function breakdown and algorithm benchmarking.
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POSSIBLY: Expand into full subsections: 3.6.1 (State-Action Space), 3.6.2 (Reward Design), 3.6.3 (Benchmarking)
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\begin{algorithm}[t]
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\DontPrintSemicolon
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\KwIn{stepsize $\eta$, smoothing $\delta$, rank $d$}
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\For{$t=1$ \KwTo $T$}{
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Sample $u_t$ on unit sphere; set $x_t^\prime=x_t+\delta u_t$\;
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Set $p_t \gets U x_t^\prime$ and observe $q_t, R_t(p_t)$\;
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$x_{t+1} \gets \Pi\_{\mathcal{X}}(x_t-\eta R_t(p_t) u_t)$\;
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}
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\caption{Online Pricing Optimization (template)}
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\end{algorithm}
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