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
Frequently Asked Questions
How do you use Monte Carlo simulations to forecast emissions and sell pressure?
What agent-based behaviors do you model (treasury, stakers, consumers)?
Which inputs are required to start (supply, vesting, utilities, fees)?
What deliverables do we receive (dashboards, stress tests, recommendations)?
Can you validate an existing model and iterate on parameters?
Do simulations cover listing scenarios, liquidity depth, and market shocks?
How accurate are price projections vs organic value signals?
Can simulations remain private and shareable with stakeholders?
What is a typical timeline from setup to final report?
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