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95 lines
3.9 KiB
Markdown
95 lines
3.9 KiB
Markdown
<img width="200" align="left" src="https://github.com/user-attachments/assets/d148b00d-e9f9-4280-89cc-0cc866e17251" />
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### PHANTOM
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[](https://github.com/velocitatem/PHANTOM/actions/workflows/latex.yml)
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[](https://pub-d5b94a3c29fd40c6b3881946e463fdb7.r2.dev/thesis-latest.pdf)
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[](https://sites.research.google/trc/faq/)
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[](https://phantom-hotel.vercel.app)
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[](https://phantom-airline.vercel.app)
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```mermaid
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mindmap
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PHANTOM((PHANTOM Project))
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North Star
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Study how automated actors change markets
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Build an experimentation platform for real-world-like commerce
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Two-loop learning system
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Online observation loop
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Offline "defense gym" loop
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Core Economic Questions
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Price Discovery
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How prices respond to demand signals
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How signal quality changes with bots/agents
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Demand & Elasticity
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Shifts in willingness-to-pay
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Short-run vs long-run elasticity
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Market Efficiency & Welfare
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Consumer surplus vs producer surplus
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Deadweight loss from frictions/manipulation
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Price Discrimination & Segmentation
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Behavioral feature-based segmentation
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Fairness vs profitability tradeoffs
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Information Asymmetry
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Agents amplify search and arbitrage
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Sellers infer more about buyers; buyers infer more about sellers
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Strategic Interaction
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Consumers vs firms vs agents
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Feedback loops: policy ↔ behavior ↔ price
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Market Power & Competition
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Algorithmic pricing as competitive tool
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Risks: tacit coordination / "algorithmic collusion"
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Externalities
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Congestion and attention costs
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Spillovers: one segment’s behavior affects others’ prices
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System-Level View
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Participants
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Humans
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Agents (automated buyers/actors)
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Firms (pricing decision-makers)
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Platform (measurement + control layer)
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Markets Simulated
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Repeated transactions
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Limited inventory / capacity constraints (conceptually)
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Time dynamics (learning over time)
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Interventions
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Pricing policies
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Experiment assignment / randomized exposure
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Agent behavioral policies (task-driven)
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Measurement & Causal Inference
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What is observed
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Actions (search, click, purchase intent)
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Context (product attributes, time, exposure)
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Outcomes (conversion, revenue, churn proxies)
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Identification strategy
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A/B tests and randomization
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Counterfactual baselines
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Robustness checks (offline replay)
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Key metrics
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Revenue / profit proxies
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Conversion & bounce
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Price volatility / stability
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Welfare proxies (e.g., dispersion, access)
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Risk, Governance, and Ethics
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Manipulation & Integrity
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Bot-driven demand distortion
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Measurement contamination
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Fairness & Transparency
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Differential pricing concerns
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Explainability and auditability
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Safety Constraints
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Guardrails on price moves
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Monitoring for runaway feedback loops
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Outputs
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Insights
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When do agents raise/lower prices via behavior shifts?
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Which market designs are robust to automation?
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Defenses
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Agent-aware pricing policies (robust control)
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Detection + mitigation strategies (feature-level separability)
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Platform Value
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Reusable testbed for market + AI-agent research
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```
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