Tokenomics Simulations for Web3 Economies
Test emissions, liquidity, and incentives via Monte Carlo and agent-based models with decision-ready reports.
We model your token economy under real-world conditions to reveal how it behaves before it ever hits the market.
Simulate. Validate. Launch with confidence.
Most tokenomics are built on assumptions: about user growth, liquidity depth, staking rates, or price dynamics.
Our simulation framework turns those assumptions into data-driven insights.
By recreating how your token interacts with users, investors, and markets, we help you understand key outcomes like volatility, supply shocks, sustainability, and value capture.
Simulation Methods: Monte Carlo & Agent-Based
Method
Monte Carlo: Parameter sweeps for emissions, demand shocks, sell-pressure scenarios
Agent-based: Roles (treasury, stakers, providers, consumers) with incentive-driven behaviors
Inputs
Distributions over KPIs: inflation, runway, liquidity
Scenario toggles: buybacks/burn, staking rewards, fees
Outputs
Probability bands & sensitivity charts
Risk flags & recommended parameter ranges
Monte Carlo Simulation
Probability bands showing token value projections across different scenarios
What We Simulate
| Domain | Example Metrics | Scenario Toggles |
|---|---|---|
| Supply & Emissions | Emissions curves, vesting unlocks, circulating supply | Inflation rate, emission schedule, vesting periods |
| Liquidity & Markets | Liquidity depth, slippage, AMM parameters | Market shock scenarios, stress tests, pool sizes |
| Utility & Incentives | Staking, fee sharing, sinks/sources | Buyback/burn policies, reward mechanisms |
Inputs Required to Start
Mandatory
Token supply plan: max, emissions, allocations
Vesting schedules and unlock periods
Launch liquidity assumptions
Nice-to-have
Utilities & fees: staking, revenue share
Burns/buyback policies and frequency
Market maker rules and thresholds
Deliverables
Notebook
•Simulation notebook & parameter sheet
•Configurable scenarios
•Versioned runs & iterations
Dashboard
•Interactive dashboards
•Real-time parameter adjustments
•Visual KPI tracking
Report
•Stress-test report with risks
•KPI sensitivity matrix
•Mitigation recommendations
Accuracy & Organic Price
We distinguish speculative market price from organic price drivers:
Organic drivers: Utilities, vesting schedules, demand from real usage
Our approach: Simulations approximate organic dynamics using probability bands, not point forecasts
Note: Simulations model fundamental value signals, not speculative market behavior
Process & Timeline
1
Week 1-2
Model Setup
Data intake & requirements gathering
Baseline model configuration
Initial simulation runs
2
Week 3-4
Scenario Design
Monte Carlo simulations
Agent-based modeling
Stress test scenarios
3
Week 5-6
Validation & Delivery
Iteration & parameter refinement
Final report preparation
Executive deck & presentation
Privacy & Sharing
Privacy Mode
Default mode: All simulations are private by default
Access: Only you and authorized team members can view
Security: End-to-end encrypted storage
No-Code vs. Custom Code
No-Code
Quick prototyping & iterations
Visual model building
Stakeholder-friendly interface
Fast turnaround time
No technical expertise required
Python / cadCAD
Advanced scenario modeling
cadCAD-style state machines
Custom agent behaviors
Complex economic models
Full Python flexibility
Use Cases
DeFi
Liquidity incentives & fee-share stability
GameFi
Sinks/sources balancing, reward decay
L1/L2
Emissions, staking APR bands, governance effects