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finalizing introduction review

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Daniel Rosel
2025-12-12 16:01:45 +01:00
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paper/src/chapters/01-intro.tex
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@@ -23,7 +23,7 @@ The industry has already seen legal action in cases like Amazon against Perplexi
\subsection{Solution Space Overview} \subsection{Solution Space Overview}
Dynamic pricing systems, as presented in \cite{Mueller2019}, often deal with sparse low-rank data of demand signals which, combined with contamination from agents, creates complex interactions that impact pricing. To further complicate the problem, certain commercial settings such as the one presented in \cite{Amjad2017} must address the true demand of products under censored observations. This provides a formulation for handling demand in our case with multiple kinds of commercial mediators: $\hat{q} \gets q_A + q_H$ where $q_A$ represents the distribution of demand generated by agentic mediators and $q_H$ represents that of true human demand. Dynamic pricing systems, as presented in \cite{Mueller2019}, often deal with sparse low-rank data of demand signals which, combined with contamination from agents, creates complex interactions that impact pricing. To further complicate the problem, certain commercial settings such as the one presented in \cite{Amjad2017} must address the true demand of products under censored observations. This provides a formulation for handling demand in our case with multiple kinds of commercial mediators: $\hat{q} \gets q_A + q_H$ where $q_A$ represents the distribution of demand generated by agentic mediators and $q_H$ represents that of true human demand.
We formally define interaction data as coming from some actor which can either be an agent ($A$) or human ($H$). An agent for purposes of our research is an algorithmic loop with the ability to access a web platform, perform actions on that platform such as (click, scroll and fill input field), the loop finishes in the moment when the judgement of provided definition of done by the internal large language model is satisfied. A detailed breakdown can be found in \cref{algagent-loop}. We formally define interaction data as coming from some actor which can either be an agent ($A$) or human ($H$). For purposes of this research, an agent is an algorithmic loop with the ability to access a web platform and perform actions such as clicks, scrolls, and input field fills. The loop terminates when the internal large language model judges the provided task definition as complete. A detailed breakdown can be found in \cref{algagent-loop}.
\begin{algorithm}[t] \begin{algorithm}[t]