Max SemenchukThis research uses an agent-based model of the $REGEN ecosystem to test how issuance and scarcity parameters shape ecological outcomes. Heterogeneous agents—project developers, verifiers, buyers, market makers, and stewards—interact through issuance, verification, trading, and retirement of eco certificates. We systematically vary policy levers such as mint rate and caps, bonding curve parameters, time-based decay or demurrage, staking or lockup requirements, burn mechanics, and fee structures. Key outcome metrics include total issued versus retired certificates, retirement rate and lag, issuance-to-retirement ratio, market liquidity, and volatility. By running scenario sweeps and sensitivity analyses, the study identifies which parameter combinations most reliably increase timely retirements of eco credits relative to issuance, indicating stronger ecological impact.
The research could help advancing this proposal: https://forum.regen.network/t/fixed-cap-dynamic-supply/34
Key Question: “For each $REGEN spent by the network, what activity generates the highest marginal ecological + economic benefit?” // which network activities, if subsidized, create the strongest ecological + economic outcomes?
- Rewards for eco assets / activities / outcomes
- Collaterize eco projects finance / insure eco outcomes (derisk the project)
- Liquidity enhanced by $REGEN for eco assets (when you preselling or whatever)
Additionally: If people need cash to spend for the project, if we use $REGEN for collateral – there would be dumping. What is the benefit for the person investing or is doing projects. Compare with other competitors.
$REGEN token ABM Specs
This research uses an agent-based model of the $REGEM ecosystem to test how issuance and scarcity parameters shape ecological outcomes. Heterogeneous agents—project developers, verifiers, buyers, market makers, and stewards—interact through issuance, verification, trading, and retirement of eco certificates. We systematically vary policy levers such as mint rate and caps, bonding curve parameters, time-based decay or demurrage, staking or lockup requirements, burn mechanics, and fee structures. Key outcome metrics include total issued versus retired certificates, retirement rate and lag, issuance-to-retirement ratio, market liquidity, and volatility. By running scenario sweeps and sensitivity analyses, the study identifies which parameter combinations most reliably increase timely retirements of eco credits relative to issuance, indicating stronger ecological impact.
The research could help advancing this proposal: https://forum.regen.network/t/fixed-cap-dynamic-supply/34
1. Research Question & Objectives
- What is the primary question?
- What secondary questions matter?
- How do liquidity, staking ratios, or validator incentives shift under different supply models?
- What are the main activities to be subsidized by the network? (e.g. liquidity provision, credit curation, creation etc) ~Funding Distribution
- What outcomes are we measuring?
Which issuance/scarcity parameters best align token supply with ecological outcomes?
Token price stability, eco-credit issuance/retirement, adoption, validator security, treasury sustainability, etc.
2. Conceptual Framework
- What ecological metaphor anchors the model?
- Which economic mechanisms are in scope?
- Which narratives are essential to test?
(Fixed cap = carrying capacity; regrowth = ecosystem resilience; burns = consumption/retirement)
(Staking, burns on eco-credit retirement, regrowth formula, validator rewards, treasury inflation, etc.)
(Scarcity story, ethical capital formation, adaptive issuance, regen-alignment.)
3. Model Scope & Assumptions
- Which agent types exist?
- Validators
- token holders
- credit issuers
- liquidity providers
- Speculators
- project treasury
- What behaviors/rules do they follow?
- Which external factors are modeled?
- What simplifying assumptions are acceptable for the first version?
- Ignore governance complexity
- Assume rational agents
- Ignore cross-chain arbitrage at first
(E.g. buy/sell triggers, staking preference, issuance based on eco-credit demand, burn on retirements.)
(Market demand for credits, validator participation, carbon market prices, cross-chain integrations.)
4. Parameters & Variables
- Core issuance/scarcity levers:
- Fixed cap C (upper bound supply)
- Regrowth rate r (how fast supply refills toward capacity)
- Burn functions B[t] (driven by eco-credit retirements/fees)
- Secondary variables:
- Staking ratio
- Validator rewards
- Liquidity depth
- Eco-credit sales & retirements
- What ranges of parameters will be explored?
(e.g. r ∈ [0.01, 0.15], burn share ∈ [1%, 50%])
5. Metrics & Evaluation
- Ecological outcomes:
- Number/value of eco-credits issued & retired
- Linkage between eco-credit demand and token supply
- Economic outcomes:
- Token stability (price volatility proxy)
- Circulating vs staked supply
- Liquidity depth & market health
- Governance outcomes:
- Distribution of ownership (decentralization proxy)
- Validator alignment/security
6. Methodology & Simulation Design
- Which simulation approach is used?
- How will runs be structured?
- Monte Carlo with randomized parameters
- Stress tests (extreme demand shock, validator exodus)
- What baseline scenarios will be defined?
- Low eco-credit demand, high eco-credit demand, speculative bubble, validator drop.
- How will sensitivity analysis be run?
(Agent-based with Mesa/cadCAD; system dynamics; hybrid?)
(Vary one parameter at a time vs factorial design.)
7. Validation & Calibration
- What real-world data anchors the model?
- Regen Registry eco-credit sales/retirements
- On-chain staking & validator data
- Market price histories
- How to check that the model produces plausible dynamics?
(Compare with past crypto systems: ETH post-EIP1559, Cosmos staking inflation, etc.)
8. Governance & Community Process
- How will results be fed back to the community?
- What design questions will validators, tokenholders, and issuers engage with?
- How does this connect to on-chain governance proposals?
(Dashboards, workshops, proposals, explainer docs.)
(e.g. acceptable regrowth rate, share of eco-credit fees burned, validator reward splits.)
9. Deliverables
- Simulation code & dashboard.
- Parameter exploration report.
- Stress test results.
- Governance-ready proposal with auditable assumptions.
- Educational materials (infographics, explainer post).
10. Risks & Limitations
- Market maturity risk (low liquidity, low distribution).
- Model risk (agents oversimplified, ecological data delayed).
- Governance timing (premature implementation vs sequencing with growth).