PHANTOM

Agent-aware dynamic pricing research platform for studying how automated transaction orchestration changes pricing power, and for testing defenses that recover margin while protecting legitimate user experience.

Live demos: Hotel | Airline | Academic page

What this repository includes

PHANTOM is a mixed research + engineering monorepo with:

• a thesis (LaTeX) formalizing Cost of Information (COI) erosion under agentic reconnaissance,
• a mode-switching web storefront (hotel and airline) for controlled human/agent interaction collection,
• backend services for event ingestion and pricing,
• an experimentation stack for benchmarks, contamination studies, and robust policy training.

Why this matters

Dynamic pricing relies on demand signals collected during browsing. LLM-driven agents can split reconnaissance and execution into separate sessions, which weakens those signals and can collapse extractable price premium. PHANTOM exists to measure that mechanism directly and evaluate practical defenses in a controlled environment.

Quick start (local platform)

1) Prerequisites

• Docker + Docker Compose
• Node.js + npm
• Python 3.8+
• latexmk (only if you want to build the paper locally)

2) Install workspace tooling and create env files

npm install
cp .env.example .env
cp .env.sweep.example .env.sweep

3) Fill required values in .env

At minimum, set these before starting services:

NEXT_PUBLIC_SUPABASE_URL=...
NEXT_PUBLIC_SUPABASE_ANON_KEY=...
AIRFLOW_FERNET_KEY=...
AIRFLOW_SECRET_KEY=...

4) Start the platform and web app

make platform.up
make web.dev

5) Verify

• Web app: http://localhost:3000
• Backend health: http://localhost:5000/health
• Pricing provider health: http://localhost:5001/health
• Airflow UI: http://localhost:8085
• Kafka console (Redpanda): http://localhost:8084 (using .env.example defaults)

Common commands

Goal	Command
Show all available workflows	make help
Start/stop platform services	make platform.up / make platform.down
Stream docker logs	make platform.logs
Run backend tests	make test.backend
Run end-to-end tests	make test.e2e
Build thesis PDF	make pdf.build
Watch thesis while editing	make pdf.watch
Build general-public thesis variant	make pdf.genpop
Run quick margin-erosion study	make study.margin-erosion.quick
Run benchmark without W&B logging	make benchmark LOCAL_BENCHMARK_ARGS='--tiers static,surge,linear --alpha-values 0.0,0.3 --episodes 3 --no-wandb'

System map

flowchart LR
  U[Human / Agent Browser] --> W[Next.js Web App]
  W -->|Price requests| P[Pricing Provider]
  W -->|Interaction events| B[Backend Ingest API]
  B --> K[Kafka]
  K --> A[Airflow + Worker Jobs]
  A --> R[Redis Model Registry]
  P -->|Session/global prices| W
  E[Research Engine + Experiments] --> A
  E --> R

Loading

Configuration

Core runtime (.env)

Variable	Purpose	Typical value
STORE_MODE	Web mode switch (hotel or airline)	hotel
BACKEND_PORT	Backend API port	5000
PROVIDER_PORT	Pricing provider port	5001
KAFKA_HOST	Kafka host for local runtime	localhost
KAFKA_PORT	Kafka external port	9092
REDIS_PORT	Redis exposed port	6377
REDPANDA_CONSOLE_PORT	Kafka console UI port	8084
NEXT_PUBLIC_SUPABASE_URL	Product catalog/data source URL	required
NEXT_PUBLIC_SUPABASE_ANON_KEY	Product catalog/data source key	required
AIRFLOW_FERNET_KEY	Airflow crypto key	required
AIRFLOW_SECRET_KEY	Airflow webserver secret	required

Training and sweep settings (.env.sweep)

Variable	Purpose
WANDB_API_KEY	Required for training/benchmark runs that log to Weights & Biases
WANDB_ENTITY	Optional W&B entity override
WANDB_PROJECT	W&B project name (default: capstone)
GITHUB_TOKEN	Required for make train.bootstrap
SWEEP_ID	Required for sweep-agent workflows (train.agent, benchmark.agent)

Repository layout

Path	Role
paper/	Thesis source, bibliography, and build artifacts
web/	Next.js storefront and experiment interaction surface
backend/server/	FastAPI ingestion API and product retrieval endpoints
backend/provider/	FastAPI pricing service backed by model registry data
backend/worker/	Celery worker for asynchronous jobs
engine/	Training and benchmarking entrypoints
experiments/	Data processing, ETL ideas, and analysis assets
docker/	Dockerfiles for platform services
tests/e2e/	Playwright end-to-end tests
docs/	Academic project page (GitHub Pages root) + MkDocs config
docs/src/	Markdown sources for the operator documentation site
docs/documentation/	MkDocs build output (gitignored; run make docs.platform; served at /documentation/ on Pages)
SETUP.md	Unified operator guide: stack, kernels, RL training, thesis refs by chapter

Operational notes

• make platform.up starts the dockerized backend stack; the Next.js app is run separately with make web.dev.
• make test.e2e expects backend (5000), web (3000), and Airflow (8085) to be up.
• Research commands (make train, make benchmark*, make train.agent) auto-load .env.sweep.
• Paper builds call paper/concat_code.sh before compilation to flatten code into the appendix.

Operator documentation

• Full setup guide (platform + research): SETUP.md
• Hosted operator docs (after make docs.platform): …/PHANTOM/documentation/ on GitHub Pages

Research artifacts

• Thesis PDF: thesis-latest.pdf or hosted PDF
• Public dataset: velocitatem/whoclickedit
• Project page: velocitatem.github.io/PHANTOM

Acknowledgments

This work is supported by Google TPU Research Cloud resources.