### PHANTOM [](https://huggingface.co/datasets/velocitatem/whoclickedit) [](https://github.com/velocitatem/PHANTOM/actions/workflows/latex.yml) [](https://pub-d5b94a3c29fd40c6b3881946e463fdb7.r2.dev/thesis-latest.pdf) [](https://sites.research.google/trc/faq/) [](https://phantom-hotel.vercel.app) [](https://phantom-airline.vercel.app) ```mermaid mindmap PHANTOM((PHANTOM Project)) North Star Study how automated actors change markets Build an experimentation platform for real-world-like commerce Two-loop learning system Online observation loop Offline "defense gym" loop Core Economic Questions Price Discovery How prices respond to demand signals How signal quality changes with bots/agents Demand & Elasticity Shifts in willingness-to-pay Short-run vs long-run elasticity Market Efficiency & Welfare Consumer surplus vs producer surplus Deadweight loss from frictions/manipulation Price Discrimination & Segmentation Behavioral feature-based segmentation Fairness vs profitability tradeoffs Information Asymmetry Agents amplify search and arbitrage Sellers infer more about buyers; buyers infer more about sellers Strategic Interaction Consumers vs firms vs agents Feedback loops: policy ↔ behavior ↔ price Market Power & Competition Algorithmic pricing as competitive tool Risks: tacit coordination / "algorithmic collusion" Externalities Congestion and attention costs Spillovers: one segment’s behavior affects others’ prices System-Level View Participants Humans Agents (automated buyers/actors) Firms (pricing decision-makers) Platform (measurement + control layer) Markets Simulated Repeated transactions Limited inventory / capacity constraints (conceptually) Time dynamics (learning over time) Interventions Pricing policies Experiment assignment / randomized exposure Agent behavioral policies (task-driven) Measurement & Causal Inference What is observed Actions (search, click, purchase intent) Context (product attributes, time, exposure) Outcomes (conversion, revenue, churn proxies) Identification strategy A/B tests and randomization Counterfactual baselines Robustness checks (offline replay) Key metrics Revenue / profit proxies Conversion & bounce Price volatility / stability Welfare proxies (e.g., dispersion, access) Risk, Governance, and Ethics Manipulation & Integrity Bot-driven demand distortion Measurement contamination Fairness & Transparency Differential pricing concerns Explainability and auditability Safety Constraints Guardrails on price moves Monitoring for runaway feedback loops Outputs Insights When do agents raise/lower prices via behavior shifts? Which market designs are robust to automation? Defenses Agent-aware pricing policies (robust control) Detection + mitigation strategies (feature-level separability) Platform Value Reusable testbed for market + AI-agent research ```