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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.
In this paper we present an exploration and defense against the presence of new commercial entities in digitally powered platforms. This research establishes the following contributions: definition and formalization of non-human transactors in e-commerce platforms, development of a testing-ground for capturing the behavioral essence of these transactors across 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 establishment of a high-level KPI-affecting causal effect and cost-saving framework for the future of internet commerce in 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).
This research effort touches a large variety of domains, spanning behavioral economics for understanding the rationality of behavior as theorized by the concept of homo economicus, agent-based modeling to translate our learned separability into disjoint dynamic pricing systems, reinforcement learning which serves as the SOTA for price-learners, and dynamic pricing and market equilibrium theory 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}