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# Pricing Heuristics Against Non-human Transaction Orchestration Mechanisms

Daniel Rösel
IE University
Bachelor's Thesis 2025
Advisor: Alberto Martín Izquierdo
Paper Supplementary Code arXiv

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## Abstract

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 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 work develops behavioral signature models using recommendation system techniques to profile session-level interaction, temporal engagement, and cross-session correlation. The AI Agent market is forecasted to grow from around USD 5-8 billion in 2025 to USD 42-52 billion by 2030, raising the question of how these systems should be designed for future robustness and how to maintain a competitive edge in the analytical components of e-commerce platforms.

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## BibTeX

    @thesis{Rosel2025PHANTOM,
      title={Pricing Heuristics Against Non-human Transaction Orchestration Mechanisms},
      author={R{\"o}sel, Daniel},
      school={IE University},
      year={2025},
      address={Madrid, Spain},
      type={Bachelor's Thesis},
      note={Advisor: Alberto Mart{\'i}n Izquierdo}
    }