1. Economics of Digital Currencies: Stablecoins and CBDCs
   The landscape of digital money is rapidly evolving, characterized by the competition and

coexistence of Centralized Stablecoins (fiat-backed), Decentralized Stablecoins (crypto-
collateralized), and emerging Central Bank Digital Currencies (CBDCs). Each asset offers

distinct trade-offs regarding liquidity, censorship resistance, peg stability, collateral
requirements, and privacy.
This project focuses on developing an economic model to formalize the interaction and
competition among these digital assets using the Lagos-Wright framework. Students will be
asked to:

• Familiarize themselves with the Lagos-Wright (2005) model and its applications to
  modern digital assets.
• Formalize the distinct economic frictions of centralized stablecoins, decentralized
  stablecoins, and CBDCs (e.g., liquidation risks, censorship probabilities, and holding
  limits) within the decentralized market’s medium-of-exchange function.
• Solve for the equilibrium conditions and pricing under which these different forms of
  digital money can coexist.
• Discuss welfare implications of the introduction of a CBDC into an economy where
  private stablecoins already circulate.
  Foundational literature on search-theoretic models of money and the specific baseline model
  will be provided at the beginning of the project.
  Useful Background Knowledge: Fintech, Asset Pricing, Monetary Economic Models

2. Artificial Intelligence and Innovation Through Recombination

Generative Artificial Intelligence, such as Large Language Models, relies on training data
created by humans. In economic terms, generative AI can be conceptualized as a technology
for “recombinant innovation”—combining existing ideas to produce new outputs. A commonly
expressed concern in the policy debate is that, while AI-driven research can boost innovation, it
can also depreciate the existing common pool of ideas. In other words, generative AI can lead
to a “crowding out” effect, where the market overinvests in recombinant AI innovation and
underinvests in the genuine human innovation.

This project focuses on formalizing these dynamics using an economic model.

Students will be asked to:

• Research innovation economics and recombinant growth models and adapt them into
  expanding variety or quality ladder frameworks.
• Formalize a dynamic model with two competing R&D technologies: genuine innovation
  (which adds to the idea pool) and AI-driven recombinant innovation (which depreciates
  the pool).
• Solve for the market equilibrium and compare it to a Social Planner’s optimum.
• Evaluate policy instruments, such as implementing a copyright or royalty tax on AI
  output to subsidize genuine human innovation and restore the optimal composition of
  research.

Useful Background Knowledge: Microeconomics, Industrial Organization

3. Economic Modeling of Decentralized Governance

Decentralized Autonomous Organizations (DAOs) rely on token-based voting, often using
delegated agents to make decisions. While delegation improves efficiency, it reintroduces
agency conflicts. In these systems, delegates build reputation over time, which can lead to a
decline in voter monitoring and eventually create opportunities for malicious behaviour and
governance failures. This project focuses on formalizing these dynamics using a dynamic game
over stochastic processes to study the principal-agent problem in decentralized governance.
Students will be asked to:

• Research the economics of token-based governance and the principal-agent problem.
• Familiarize themselves with a continuous-time governance processes.
• Solve the equilibrium of the model.
• Analyze the dynamics implied by the model’s equilibrium.

Useful Background Knowledge: Fintech, Asset Pricing, Monetary Economic Models

4. Industrial Organization of Supply Chains under Geopolitical Uncertainty
   
   Global supply chains operate as complex, multi-echelon networks spanning external suppliers,
   internal factories, and regional distribution centers. Firms traditionally optimize these networks
   for pure cost efficiency—minimizing unit landed costs and amortizing fixed sourcing costs
   through concentrated procurement. However, escalating geopolitical uncertainty (e.g., trade
   embargoes, localized conflicts, tariff regime shifts) introduces discrete, catastrophic shocks to
   transportation capacities and node availability.
   This project focuses on formalizing the trade-off between supply chain efficiency (Just-in-Time,
   single-sourcing) and resilience (safety stock, multi-sourcing) using Industrial Organization and
   microeconomic theory.
   Students will be asked to:

Prove the conditions under which a firm optimally transitions from a cost-minimizing
single-source strategy to a risk-mitigating dual-sourcing strategy as the variance of θ̃
increases.

Useful Background Knowledge: Microeconomics, Industrial Organization, Optimization

Formalize a stochastic cost function where q is the vector of procured quantities
across different nodes and θ̃ represents a vector of Markovian geopolitical shocks (e.g.,
link failures or sudden tariff spikes).