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26 lines
3.4 KiB
TeX
26 lines
3.4 KiB
TeX
\section{Conclusion}
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\label{sec:conclusion}
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This thesis examined reinforcement-learning policies for dynamic pricing when a fraction of traffic is orchestrated by non-human agents intent on extracting information before purchase. We introduced COI-oriented metrics, a behavioral distinguishability layer, and a distributionally robust training loop; empirical runs show where robustness helps and where it must be tuned.
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\subsection{Summary of contributions}
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Our work has yielded a broad set of dependencies which we carefully orchestrated to give us measurable results. To give a clear picture we outline the specific contributions of each stage of our work. The theoretical component formalizes why agent-mediated reconnaissance erodes pricing power, the behavioral component establishes that such contamination is detectable from interaction traces alone, the control component translates that distinguishability into a robust pricing mechanism, and the systems component provides the controlled experimental environment required to observe, test, and reproduce these effects.
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\begin{itemize}
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\item TPU-accelerated parallelization of the behavioral simulation and reinforcement learning pipeline, making large factorial sweeps tractable.
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\item Formalization of non-human transaction orchestration in e-commerce as a distinct source of contamination in dynamic pricing systems.
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\item Definition of the Cost of Information (COI) as a mechanism-level quantity for pricing power, together with a theorem on its erosion under increasing agent saturation.
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\item Design and implementation of a controlled e-commerce research platform on a hybrid Kappa--Lambda architecture for collecting and replaying high-fidelity interaction trajectories.
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\item Construction and empirical validation of a behavioral distinguishability framework that separates human and agent sessions from interaction signals alone using transition kernels and KL-based divergence.
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\item A generative contamination mechanism that injects learned agent behavior into the pricing environment for controlled robustness experiments.
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\item Translation of distinguishability scores into defensive pricing via distributionally robust reinforcement learning under non-stationary contamination.
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\item Evidence that contamination depresses revenue and that robustness gains are regime-dependent, so penalties and radii need calibration rather than a single default.
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\item Release of a public experimental artifact (code and dataset) for reproducing and extending work on agent-mediated traffic.
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\end{itemize}
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\subsection{Limitations and future work}
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Several constraints are intentional and could be relaxed later. Action weights in the demand proxy are hand-set; learning them from data is an obvious next step. The Stackelberg interface assumes a clean alternation between platform move and market response; richer histories (multi-agent, multi-platform) would need a less rigid state definition. Non-perishable catalog supply in the simulator widens the sim-to-real gap for inventory-constrained domains. Within-session contamination is modeled as stable; time-varying $\alpha$ inside a session would better match some attack patterns.
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Before any deployment, human baselines should grow beyond the convenience sample used here, and catalog scaling laws should be re-checked when transition matrices grow with SKU count. For the deployment of this methodology presented in our work.
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