Simulation-Generated Governance Proposals

How Predictive Models Initiate, Shape, and Justify DAO Policy Actions in the NSF Governance Cycle

7.8.1 From Forecast to Governance: The Missing Link

In conventional governance models, policy follows intuition or reactive interpretation of delayed data.

In NSF, governance is not only reactive—it is simulation-initiated.

This means:

• A verified simulation output can propose a policy change

• Clause logic can initiate a DAO governance motion

• Systemic risks can auto-generate pre-drafted proposals

• DAOs can vote on machine-suggested responses, preconditioned by risk

This transforms NSF into a foresight-driven governance engine—where simulations don’t just inform decisions; they trigger them.

---

7.8.2 Simulation-Triggered Governance Workflows

Each SimulationRunVC can include:

• A proposal_suggestion block

• Triggered automatically when conditions are met

• Contains proposed action, rationale, affected clauses, and fallback logic

Example output:

{
  "simulation_id": "SimRun#0x89c4...",
  "risk_condition_met": true,
  "proposal_suggestion": {
    "title": "Increase Emergency Fund Cap",
    "affected_clause": "[email protected]",
    "suggested_action": "raise cap to 30M CHF",
    "justification": "Forecasted crop yield < 60% threshold for 3+ jurisdictions",
    "required_DAOs": ["FinanceDAO", "SimDAO-EastAfrica"]
  }
}

---

7.8.3 Autogenerated Proposal Format

NSF includes a Proposal Schema for simulation-triggered governance:

Field	Description
proposal_id	Unique identifier
trigger_source	Simulation ID and clause link
proposed_by	Auto-generated or simulation agent
required_quorum	Minimum DAO participation
affected_objects	Clauses, VCs, budgets, treaty logic
fallback_path	Alternative logic if rejected
simulation_context	Data, thresholds, model lineage

These are published to DAO dashboards, notification APIs, and governance queues.

---

7.8.4 DAO Handling of Simulation-Proposed Governance

DAOs receive flagged proposals through:

• Governance dashboards

• Smart notification channels (e.g., Slack, Matrix, Discord)

• Multi-sig proposal feeds (e.g., Aragon, Safe, DAOstack-compatible)

• Simulation trace viewers for justification replay

DAO delegates vote, escalate, reject, or fork proposals based on simulation backing and institutional consensus.

---

7.8.5 Use Cases for Simulation-Generated Proposals

Domain	Example
Climate	“Drought index exceeds critical threshold in 4 nations → propose water sharing clause activation”
Health	“ICU capacity risk forecast > 90% → propose temporary lockdown credential activation”
Finance	“Market volatility crosses 2σ → propose freeze on cross-border remittance clause”
Migration	“Forecasted displacement exceeds infrastructure capacity → propose refugee facility deployment”
Treaty Governance	“Conflict simulation shows systemic risk → propose treaty clause suspension”

---

7.8.6 Clause-Initiated Proposals

Active clauses may include logic to auto-draft governance actions when new risk evidence is registered:

if flood_risk > 0.85 and budget_pool < 20M
then propose("Increase pool to 40M", domain="FinanceDAO", rationale="forecast shortfall in 3 regions")

These are executed by clause-bound governance agents and must be DAO-reviewed within a timeout window.

---

7.8.7 Simulation Forks and Proposal Divergence

In the case of multiple forecast paths:

• A simulation may generate multiple governance options

• Each is presented with projected impact deltas

• DAOs may choose, combine, or defer execution

• Conflicting proposals trigger quorum-based arbitration (see Ch. 6)

Forks are stored as Proposal Lineage Trees, signed by SimDAOs and hashed into the Audit Layer.

---

7.8.8 Policy Simulation Before Proposal Voting

Before voting, DAOs may:

• Re-run simulations with new inputs

• Stress-test clause execution paths

• Simulate counterfactual policies

• Attach alternative forecasts to proposals

Voting UIs display comparative impact curves, budget differentials, and jurisdictional outcomes.

---

7.8.9 Treating Simulation Proposals as Institutional Memory

All simulation-generated proposals are:

• Signed with cryptographic attestations

• Archived in DAO governance logs

• Indexed by affected clauses, risk domains, jurisdictions

• Used in future model training for scenario learning

This builds a machine-readable record of foresight-backed institutional behavior.

---

7.8.10 AI and Simulation as Institutional Policy Agents

Simulation-generated proposals mark a shift where:

• Machine reasoning augments policy ideation

• Forecasts are natively understood by governance engines

• Risk no longer waits for politics—it is translated into executable options

NSF’s simulation-driven proposal infrastructure enables governments, DAOs, and treaties to act on risk in real time, grounded in machine-verifiable, explainable, and auditable foresight.