What We Learned Modeling a Regen Token Economy

Done

Agent-based modeling research exploring token economy dynamics, mint/burn mechanisms, and governance parameter optimization for ecological impact.

Max Semenchuk

Overview

This project applies agent-based modeling (ABM) to the REGEN token economy, simulating how different parameter choices affect inflation, liquidity, and ecological credit retirement over time.

Key Findings

  • Higher burn share reduces net inflation and shortens retirement lag
  • Maintaining liquidity depth during shocks reduces slippage
  • Parameter regions exist where retirements grow faster than issuance without harming validator security

Policy Takeaways

  1. Cap regrowth rate within a defined band
  2. Route a share of rewards to AMM depth
  3. Maintain validator APR within a floor range
  4. Use efficient-frontier plot for governance choices

Methodology

The model uses agent-based simulation with multiple actor types (validators, credit buyers, speculators, ecological projects) interacting through on-chain mechanisms. Key variables include mint rate, burn share, liquidity pool depth, and credit retirement velocity.

Implications

These findings directly inform the Fixed Cap, Dynamic Supply proposal and provide the quantitative foundation for governance parameter recommendations.