#### Project Links

##### Thesis PDF

Latest public build of the full thesis document.

IE University, 2025

##### PHANTOM Repository

Monorepo with paper source, platform code, and experiments.

Open Source

##### P4P Interaction Layer

Reusable storefront and logging layer released for replication.

Public Artifact

##### Hotel Mode Demo

Public deployment of the hotel-style experiment interface.

Live Demo

##### Airline Mode Demo

Public deployment of the airline-style experiment interface.

Live Demo

##### Blog Series

Behind-the-scenes posts covering thesis process, tooling, and insights.

To Boldly Code

##### Goal Library

Task definitions used to assign actor objectives in experiments.

Experiment Design

# Pricing Heuristics Against Non-human Transaction Orchestration Mechanisms

Daniel Rösel
IE University
Bachelor's Thesis 2025
Advisor: Alberto Martín Izquierdo
Blog Series Paper Goal Set Code Hotel Demo Airline Demo

## Abstract

When you shop online, prices often change based on how much interest you show — the more you browse, the more the site learns about your intent and may raise prices accordingly. This works because stores assume that a curious, engaged shopper is more likely to buy. But AI assistants are now doing the shopping research on behalf of users: they browse in one session to gather price information and then let the user purchase in a fresh session at the lower, unadjusted price. The store never sees the connection between the two, so it never gets to factor in that genuine intent — and loses the revenue it would have earned.

PHANTOM studies this problem and builds defenses against it. We created a realistic fake store (in hotel and airline modes) where both real people and AI agents were given shopping tasks, and we recorded every click, scroll, and page visit. By comparing how humans and AI agents move through a site, we found clear patterns that tell them apart. We then used those patterns to build a smarter pricing system that can recognize when it is likely talking to an AI scout and adjust its strategy accordingly — protecting the store's margins without making things worse for genuine shoppers.

## Project Scope

The current thesis revision extends both theory and implementation. The main research question is how a pricing system can preserve margin integrity when browsing and purchasing are increasingly orchestrated by AI agents.

- Formal contribution: a Cost of Information erosion theorem showing why price-query saturation can collapse dynamic pricing power.
- System contribution: a hybrid online/offline stack (Next.js storefront, pricing provider, Kafka event streams, Airflow ETL, Redis serving layer).
- Modeling contribution: class-specific transition kernels for human and agent behavior, with KL-divergence based separability scores.
- Control contribution: a contamination-aware DR-RL pricing policy trained under distributional uncertainty using Wasserstein-style robustness.

Controlled trials currently include balanced human and agent sessions with goal-driven tasks across hotel and airline interfaces. Early separability results are strong (Mann-Whitney U=2.0, p=0.0006), while robust pricing gains remain regime-dependent and are being calibrated in larger sweeps.

## Defense Scenes

## COI from first principles.

## Behavioral kernel construction: learning how humans and agents differ.

## Distributionally robust control loop.

## Full Thesis

## BibTeX

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