Agent‑Based Simulations for DeFi Interest Rate Dynamics and Borrowing Costs

Introduction
The world of decentralized finance has grown from a niche experiment into a robust ecosystem that supports billions of dollars in loans, derivatives, and liquidity provision.
Where once interest rates were set by a single oracle or a hard‑coded algorithm, modern platforms now rely on complex feedback loops that respond to user behavior, market sentiment, and macro‑economic shocks—an evolution that is examined in depth in the post on dynamic interest rate modeling in DeFi with agent‑based borrowing mechanics.
To understand how these dynamics evolve, researchers and practitioners have turned to agent‑based simulations.
These models capture the heterogeneity of market participants, their strategic interactions, and the stochastic environment that characterizes blockchain markets.
In the following article we will walk through the motivations, design choices, and practical steps for building an agent‑based framework that simulates DeFi interest rate dynamics and borrowing costs.

Why Agent‑Based Models Matter
Traditional analytical models such as the Vasicek or Black‑Karasinski interest‑rate frameworks assume a homogeneous population of borrowers and lenders and rely on closed‑form solutions.
While mathematically elegant, these models fall short when applied to DeFi for several reasons:

• Heterogeneous participants: On‑chain actors include individual users, arbitrage bots, institutional vaults, and liquidity pools, each with distinct risk tolerances and strategies.
• Self‑fulfilling expectations: Interest rates in DeFi are often driven by algorithmic supply curves that adjust in real time, creating path dependence that classical models cannot capture.
• Layer‑2 interactions: Layer‑2 rollups and cross‑chain bridges add latency, gas costs, and additional risk factors that affect borrowing decisions.
• Regulatory shock absorbers: DeFi protocols can incorporate dynamic risk parameters (e.g., collateralization ratios) that respond to external regulatory signals.

An agent‑based approach accommodates these nuances by explicitly modeling each participant as an autonomous decision maker.
The collective behavior of thousands of agents generates emergent macro‑level dynamics that can be studied, validated, and optimized.

Designing the Agent Environment

Defining Agents
At the heart of the simulation is a set of agent archetypes.
Typical categories include:

• Borrowers: Users who request loans in one or more tokens, motivated by yield farming, arbitrage, or liquidity needs.
• Lenders: Providers of capital, often institutional or automated strategies, who earn interest and may adjust collateral ratios.
• Governance Actors: Protocol holders who can vote on parameter changes such as target borrowing rates or risk buffers.
• Oracles: External data providers that feed market prices and volatility into the interest‑rate curve.

Each agent is endowed with a utility function, a risk tolerance, and a behavioral rule set.
For example, a borrower might choose a loan size based on the expected return from a farming opportunity minus the borrowing cost, while a lender may adjust the supplied liquidity to maintain a desired exposure.

State Variables
To accurately simulate interest rate dynamics, the environment tracks:

• Token balances of each agent.
• Collateral positions and their valuation over time.
• Interest‑rate curves defined by supply–demand functions that respond to platform metrics such as total debt or circulating supply.
• Gas and transaction cost estimates, which influence the effective cost of borrowing or lending.

These variables are updated synchronously at each time step, typically expressed in blocks or minutes.

Time Horizon and Granularity
The simulation can run over days, weeks, or months of on‑chain time, depending on the research question.
Higher granularity allows modeling of flash loan attacks or rapid liquidation events, while coarser steps focus on long‑term equilibrium dynamics.

Implementing the Supply Curve
A common DeFi approach is to use a piecewise linear supply curve:

InterestRate = BaseRate + k * (TotalDebt / CollateralSupply)

where k is a slope parameter reflecting the protocol’s risk appetite.
Agents react to changes in InterestRate by adjusting their borrowing or lending behavior.
By incorporating a feedback loop, the curve itself becomes a function of agent decisions, leading to potential oscillations or convergence.

Behavioral Rules
The simulation relies on rule‑based decision making, often expressed as:

1. Borrower Rule

   • Compute expected yield from an opportunity.
   • If expected yield minus borrowing cost > threshold, place a borrow request.

2. Lender Rule

   • Monitor current interest rates and projected loan demand.
   • Deposit or withdraw liquidity to target a risk‑adjusted exposure.

3. Governance Rule

   • Evaluate protocol health metrics (e.g., under‑collateralization).
   • Vote to adjust BaseRate or k if thresholds are breached.

4. Oracle Rule

   • Update token prices at each tick, injecting stochastic shocks.

These rules can be deterministic or incorporate probabilistic elements to model uncertainty and bounded rationality.
The approach to borrowing mechanics and dynamic interest rates is explored in the article on exploring borrowing mechanics in DeFi through dynamic interest rate agent models.

Modeling Market Shocks

Volatility Injection
A realistic simulation must include price shocks, reflecting events such as large token sell‑offs or sudden macro‑economic news.
We model price dynamics using a Geometric Brownian Motion (GBM):

dP = μ * P * dt + σ * P * dW

where μ is the drift, σ the volatility, and dW a Wiener process.
Agents observe the new price before deciding on borrowing or lending.
This type of agent‑based simulation bridges classical financial mathematics with DeFi borrowing analysis, as discussed in the post on from financial mathematics to DeFi: agent‑based interest rate simulations and borrowing analysis.

Liquidity Constraints
Some protocols impose limits on how much liquidity can be borrowed at once.
We model this by imposing a hard cap on TotalDebt relative to CollateralSupply.
When the cap is approached, the interest rate spikes, discouraging further borrowing.

Governance Dynamics
Governance actors may react to extreme borrowing costs by proposing parameter changes.
We model this as a Poisson process where proposals arrive randomly, and voting success depends on the distribution of governance tokens.
The resulting policy shifts feed back into the supply curve, altering future interest rates.

Putting It All Together: The Simulation Loop

Initialize all agents and state variables
For each time step:
    Update oracle prices
    For each borrower agent:
        Apply borrower rule → decide on new borrow requests
    For each lender agent:
        Apply lender rule → decide on new liquidity actions
    Apply governance rule if proposals triggered
    Update supply curve based on new TotalDebt
    Resolve all transactions on-chain
    Record macro metrics (interest rates, borrowing costs, liquidity levels)
End loop

This simple pseudocode hides many complexities: transaction ordering, gas fee calculations, and state updates that can happen asynchronously on real blockchains.
Nevertheless, it captures the essence of an agent‑based simulation for DeFi.

Validation Against Historical Data

Benchmarking
To ensure credibility, simulation outputs should be compared with on‑chain data from established protocols (e.g., Aave, Compound).
Key metrics include:

• Daily average borrowing rate
• Distribution of loan sizes
• Liquidity depth over time

Parameter Calibration
We use an iterative approach: start with plausible values for k, BaseRate, and agent utility thresholds.
Run the simulation and compute the root‑mean‑square error between simulated and real rates.
Adjust parameters using gradient‑free optimization (e.g., Nelder–Mead) until the error is minimized.

Sensitivity Analysis
After calibration, we perform a sensitivity sweep:

• Vary σ in the GBM to see how volatility affects borrowing costs.
• Increase the number of arbitrage bots to study the impact on market depth.
• Alter the governance voting threshold to assess policy responsiveness.

These experiments illuminate the robustness of the protocol to shocks and design choices.

Practical Tips for Building the Model

• Modularity: Separate agent logic from environment mechanics. This allows swapping in different behavioral models without rewriting the core simulation engine.
• Scalability: Use efficient data structures (hash maps for agent states) and parallelize agent updates when possible.
• Reproducibility: Store random seeds and initial conditions. Provide a configuration file that users can tweak to explore scenarios.
• Visualization: Plot interest rates, borrowing costs, and liquidity over time. Visual tools aid intuition and communicate results to stakeholders.

Extending the Framework

Incorporate Layer‑2 Scaling
Layer‑2 rollups can be modeled by adding a latency component between transaction submission and inclusion.
Borrowers may wait longer for confirmations, affecting the perceived borrowing cost.

Cross‑Chain Interactions
For protocols that support cross‑chain collateral, model the bridge cost and slippage.
This adds another layer of risk that borrowers and lenders must consider.

Risk Management Strategies
Simulate advanced risk mitigation tools such as dynamic collateral ratios, collateral‑free borrowing with insurance, or multi‑token collateral portfolios.
Analyze how these tools affect systemic risk and borrowing costs.

Real‑World Deployment Scenarios

Optimizing Liquidation Thresholds
By observing how varying liquidation thresholds affect the frequency of forced liquidations, protocol designers can set parameters that balance borrower incentives with system stability.

Stress Testing Regulatory Shocks
Simulate sudden changes in regulatory sentiment (e.g., a new compliance rule that raises borrowing costs) to evaluate protocol resilience.

Dynamic Interest Rate Caps
Protocols might impose a hard cap on borrowing rates to protect users. Using the simulation, designers can assess the impact of such caps on liquidity provisioning and user activity.

Future Research Directions

Hybrid Models
Combine agent‑based simulations with stochastic differential equations to capture both micro‑level behavior and macro‑level diffusion processes.

Learning Agents
Integrate reinforcement learning agents that adapt their strategies over time, providing a richer representation of how real traders might behave.

Blockchain‑Specific Features
Incorporate on‑chain mechanisms such as voting delay, multi‑signature governance, and reputation systems to study their influence on borrowing dynamics.

Conclusion
Agent‑based simulations bring a powerful lens to the study of DeFi interest rate dynamics and borrowing costs.
By explicitly modeling heterogeneous agents, their strategic interactions, and the stochastic environment, researchers can uncover emergent patterns that are invisible to traditional analytical tools.
These models inform protocol design, risk management, and regulatory policy, ultimately contributing to a more robust and efficient decentralized financial ecosystem.

By embracing the complexity of real‑world DeFi markets, agent‑based approaches empower stakeholders to test “what if” scenarios, optimize parameters, and anticipate systemic risks before they manifest on the blockchain.