Revnet Modeler

Revnet Modeler: Simulation Tool

Problem

Planning revnet parameters requires understanding how different configurations affect treasury dynamics, token distribution, and participant outcomes over time. The modeler simulates these dynamics before deployment.

Context / Trigger Conditions

• Planning a new revnet deployment
• Comparing different parameter configurations
• Understanding how events (investments, loans, cash-outs) affect the system
• Visualizing treasury and token dynamics
• Explaining chart outputs to users

Solution

Tool Location

https://github.com/mejango/rev-sim/index.html

Open in browser to use the interactive modeler.

Seven Economic Levers (Per Stage)

Each stage can configure:

Event Types

The modeler supports these event types:

Events are labeled by participant (e.g., "Team", "Investor A", "Customer").

Available Charts

Treasury & Value Charts

Token Charts

Loan Charts

Fee Charts

State Machine Calculations

The modeler uses a state machine (StateMachine.getStateAtDay(day)) that tracks:

{
  day: number,
  revnetBacking: number,      // Treasury balance
  totalSupply: number,        // Total token supply
  tokensByLabel: {            // Tokens held by each participant
    "Team": 1000,
    "Investor A": 500,
    ...
  },
  dayLabeledInvestorLoans: {  // Outstanding loan amounts by participant
    "Team": 50000,
    ...
  },
  loanHistory: {              // Detailed loan records
    "Team": [
      { amount: 50000, remainingTokens: 100, ... }
    ]
  }
}

Key Formulas

Cash-Out Value (Bonding Curve)

calculateCashOutValueForEvent(tokensToCash, totalSupply, backing, cashOutTax) {
  const proportionalShare = backing * tokensToCash / totalSupply
  const taxMultiplier = (1 - cashOutTax) + (tokensToCash * cashOutTax / totalSupply)
  return proportionalShare * taxMultiplier
}

Loan Fees

// Internal fee (to treasury)
const internalFee = loanAmount * 0.025  // 2.5%

// External fee (to protocol)
const externalFee = loanAmount * 0.035  // 3.5%

// Interest (after grace period)
const annualInterest = 0.05  // 5% after 6-month grace period

Pre-Built Scenarios

The modeler includes pre-configured scenarios:

Each has variants: -with-loans, -with-exits

Interpreting Results

Treasury Health

• Healthy: Backing grows over time, floor price increases
• Warning: Backing flat or declining, many cash-outs
• Critical: Large loan defaults, negative treasury trajectory

Token Distribution

• Balanced: No single holder > 50%
• Concentrated: Few holders control majority
• Diluted: Early holders significantly diluted

Loan Exposure

• Safe: < 20% of tokens backing loans
• Moderate: 20-50% collateralized
• High: > 50% collateralized (systemic risk)

Using for Planning

1. Set stages matching your fundraising/growth plan
2. Add events representing expected investments, revenue, exits
3. Run simulation and review charts
4. Iterate on parameters until dynamics match goals
5. Compare multiple scenarios to stress-test

Verification

1. Verify cash-out calculations match bonding curve formula
2. Check loan fees sum to expected percentages
3. Confirm token distribution adds to total supply
4. Validate treasury balance equals sum of inflows - outflows

Example

Planning a revnet with team allocation and investor entry:

Stage 1 (Days 0-90):
  - Issuance: 1,000,000 tokens/$
  - Split: 30% to Team
  - Cash-out tax: 10%

Events:
  Day 1: Team invests $10,000
  Day 30: Investor A invests $50,000
  Day 60: Revenue $20,000
  Day 90: Team takes loan (50% of tokens)

Run simulation → Review:
  - Token Distribution: Team 30%, Investor A 50%, Revenue recipients 20%
  - Team's loan potential and actual loan
  - Treasury backing trajectory
  - Cash-out value for each participant

Notes

• Modeler uses simplified fee model (may differ from exact contract implementation)
• Simulations are deterministic given same inputs
• Charts update automatically when parameters change
• Export scenarios for comparison and documentation
• The modeler runs entirely client-side (no data sent externally)

References

• Tool: https://github.com/mejango/rev-sim/index.html
• State machine: https://github.com/mejango/rev-sim/js/state.js
• Charts: https://github.com/mejango/rev-sim/js/chartManager.js
• Academic validation: /revnet-economics skill