PHANTOM/paper/src/chapters/01-intro.tex

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\section{Introduction}

In this paper we present an exploration and defense against the presence of new commercial entities present in digitally powered platforms. This research aims to establish the following contributions: definition and formalization of the existence of non-human transactors in e-commerce platforms, development of a testing-ground for capturing the behavioral essence of these transactors on a large variety of digital systems, construction of a discriminative model (to prove separability) as a strong learner for downstream mitigation of contamination by non-human entities, translation of such learned separability into existing dynamic pricing machine learning loops, and finally we establish a high-level KPI affecting causal effect and cost-saving framework for the future of commerce done on the internet with the presence of such non-human learners.

This research effort touches a large variety of domains, spanning those such as: behavioral economics for understanding the rationality of behavior as theorized by the concept of homo economicus, agent-based modeling in our effort to translate our learned separability into disjoint dynamic pricing systems, reinforcement learning which serves as the SOTA for price-learners, dynamic pricing and economics market theory of equilibrium to understand the risks of possible supra-competitive pricing phenomena in cases of adversarial pricing systems (driving the market out of equilibrium).

\subsection{Motivation and Market Context}

The current innovation boom in generative artificial intelligence and its applications to knowledge-based work tasks has brought many competing technologies for browser-use automation, with most benchmarks and evals motivating \cite{Xia2025} the development of ability focusing on the performance of commercial research, understanding and transaction execution. \cite{Xie} The ``AI Agent'' market is forecasted to grow from around USD 5-8 billion in 2025 to USD 42-52 billion by 2030. This surge reflects adoption in e-commerce, customer service, and enterprise automation, where agents handle interactions previously done by humans. Begging the question of how should these systems be designed for future robustness as well as how to maintain a competitive edge in the analytical components of these e-commerce platforms. \cite{MarkNtelAdvisors2025}

The key stakeholders of the threat presented by the shift towards a growing share of the traffic coming from agents include online businesses and platform operators (especially in bot-heavy sectors like retail, travel, and financial services), their security/fraud and engineering teams, end users whose accounts and data are exposed (and whose experience degrades), regulators or legal stakeholders responding to breaches and fraud, and the attackers/bot operators driving the automation.\cite{Imperva2025}

The industry has already seen legal action coming from large cases like Amazon against Perplexity \cite{AmazonvsPerplexity} which comes from the nature of the difficulty of identifying traffic coming from hybrid systems like the Commet browser, which is a system that this paper does explore in order to better understand the nature of how the interaction data looks like and what it means for dynamic pricing or recommendation systems down the line. This observed impact indicates a need for prevention of secondary negative effects on the ``legacy'' systems which power modern revenue sources for many companies. Dynamic pricing algorithms rely on directly translating demand features $q$ to new price assignments $\hat{p}$ across a catalogue of products of size $N$.

\subsection{Solution Space Overview}
The dynamic pricing systems, as presented in \cite{Mueller2019}, often deal with sparse low-rank data of demand signals, which in combination with contamination from agents makes for a very complex set of interactions which have an impact on the pricing. To further complicate the problem in certain commercial settings, such as the one presented in \cite{Amjad2017}, we must address the true demand of products under censored observations. This sets us up with a good formulation for handling demand in our case of 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}.


\begin{algorithm}[t]
\DontPrintSemicolon

\SetKwInOut{Input}{Input}
\SetKwInOut{Output}{Output}

\Input{Goal $G$, Platform URL $u$, LLM $\mathcal{M}$}
\Output{Task completion result $r$}

Initialize browser instance $\mathcal{B}$ with connection to $u$\;
Construct prompt $\pi \gets \textsc{BuildPrompt}(G, u)$\;
$\text{done} \gets \text{False}$\;

\While{$\neg \text{done}$}{
    Observe current page state $s_t$ from $\mathcal{B}$\;
    Query $\mathcal{M}$ with $(\pi, s_t)$ to determine next action $a_t \in \{\text{click}, \text{scroll}, \text{fill}, \text{navigate}\}$\;
    Execute $a_t$ on $\mathcal{B}$ to transition to state $s_{t+1}$\;
    $\text{done} \gets \mathcal{M}.\textsc{JudgeCompletion}(G, s_{t+1})$\;
}

Extract final result $r$ from terminal state\;
\Return{$r$}\;

\caption{AI Agent's Interaction Loop}
\label{algagent-loop}
\end{algorithm}


The previously described goal of separability allows us to formulate a task which entails taking raw interaction data for either actor and creating a composite demand estimate $\hat{q}$.