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paper/src/chapters/01-intro.tex
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\section{Introduction} \section{Introduction}
In this paper we present an exploration and defense against the persence of new comercial entities present in digitially 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 off 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, 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. 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 theorizied 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). 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} \subsection{Motivation and Market Context}