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name: revnet-modeler description: | Revnet simulation and planning tool for modeling token dynamics. Use when: (1) planning revnet parameters before deployment, (2) visualizing treasury/token dynamics over time, (3) comparing different scenarios (loans, cash-outs, investments), (4) understanding chart outputs, (5) explaining simulation results. Covers stage configuration, event sequences, and all chart types.

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:

Lever	Description	Effect
Stage Start Day	When this stage begins	Defines stage transitions
Initial Issuance Rate	Tokens minted per $	Higher = more tokens per payment
Issuance Cut %	% reduction per period	Creates supply scarcity over time
Issuance Cut Frequency	Days between cuts	Controls cut rate (7, 14, 28 days)
Split %	% of minted tokens to splits	Team/reserved allocation
Cash-Out Tax Rate	Bonding curve tax (0-100%)	Higher = more treasury retention
Auto-Issuances	Automatic token mints	Pre-scheduled distributions

Event Types

The modeler supports these event types:

Event	Description	Treasury Effect
investment	External payment	+ backing, + supply
revenue	Operating revenue	+ backing, + supply
loan	Take loan against tokens	- backing (net of fees)
payback-loan	Repay loan	+ backing
cashout	Redeem tokens	- backing, - supply

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

Available Charts

Treasury & Value Charts

Chart	Shows	Key Insight
Treasury Backing	Total backing over time	Overall treasury health
Cash Out Value	Per-token redemption value	Floor price dynamics
Issuance Price	Token mint cost	Ceiling price with cuts
Cash Flows	Inflows/outflows by day	Event impact on treasury

Token Charts

Chart	Shows	Key Insight
Token Distribution	Tokens by holder (liquid + locked)	Who holds what
Ownership %	Percentage ownership over time	Dilution visualization
Token Valuations	Dollar value of holdings	Participant wealth
Token Performance	ROI % by participant	Investment returns

Loan Charts

Chart	Shows	Key Insight
Loan Potential	Max borrowable by holder	Available liquidity
Loan Status	Outstanding loan amounts	Current debt
Outstanding Loans	Loan values over time	Debt trajectory
Tokens Backing Loans %	% of tokens as collateral	Leverage exposure

Fee Charts

Chart	Shows	Key Insight
Fee Flows	Internal vs external fees	Fee destination breakdown

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:

Scenario	Description
conservative-growth	Steady investment, gradual expansion
hypergrowth	Rapid investment, high volatility
bootstrap-scale	Small start, then scale-up
vc-fueled	Large early investment, then revenue
community-driven	Many small investments
boom-bust	Growth followed by cash-outs

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