Scenario Modeling Framework

A Unified Methodology for Forecasting Policy Outcomes and Risk Dynamics Across Domains

7.1.1 Purpose of Scenario Modeling in NSF

The Simulation and Foresight Layer is not just an analytical backend—it is a formal execution precondition for:

• Clause activation

• DAO upgrades

• Parametric insurance disbursement

• Treaty and governance triggers

• Multi-agent coordination in crisis contexts

To serve this role, NSF implements a Scenario Modeling Framework (SMF)—a modular, multi-domain system for simulating future states based on:

• Risk data inputs

• Clause logic constraints

• Policy levers and governance variables

• Credential access and role deployment

• Jurisdictional variations

Every simulation in NSF is signed, anchored, and auditable—it is not a dashboard visualization, but a computational proof layer for multilateral action.

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7.1.2 Core Components of the NSF SMF

The Scenario Modeling Framework consists of:

Component	Role
Model Ontology	Standardized classification of all model types, from epidemiology to macroeconomics
Input Schema Engine	Parses structured and semi-structured inputs (e.g., JSON, GeoTIFF, netCDF, sensor streams)
Clause Binding Interface	Maps simulation outputs to clause threshold triggers
Execution Sandbox	Runs simulations inside verifiable compute (TEEs or zkVMs)
Forecast Packaging	Generates attestation packages (SimulationRunVC) for DAO and CAC consumption
Backtest & Monitoring Unit	Compares forecast accuracy against real outcomes over time
Dispute Trace Engine	Allows rollback, validation, and regeneration of forecasts on request

These components are composable, meaning they support rapid integration of domain-specific models while maintaining global auditability and governance compatibility.

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7.1.3 Simulation Typologies

NSF categorizes simulations as follows:

Type	Example Use
Point Forecast	Rainfall > 250mm triggers [email protected]
Distribution Forecast	Prob(risk > 0.8) = 92% leads to adaptive insurance disbursement
Threshold-Driven Sim	Trigger EmergencyClause if ICU > 85% sustained for 72 hours
Agent-Based Sim	Policy scenario modeling in dynamic networks (e.g., trade, disease, migration)
Hybrid Linked Sim	Climate impact ↔ Trade disruption ↔ Food insecurity ↔ Civil unrest

Every simulation run is hashed, verified, and signed by a SimDAO before it can activate a clause or credential.

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7.1.4 Forecast Units and Execution Contexts

Simulations may be scoped to:

• Jurisdiction (e.g., India@Karnataka, EU@Alpine)

• Time horizon (e.g., now+7d, Q3 2025)

• Model granularity (e.g., household, national, regional)

• Policy dependencies (e.g., “if ClauseX passes, simulate ClauseY impact”)

These are formalized as:

{
  "simulation_id": "sim-0x88bc...",
  "model": "[email protected]",
  "jurisdiction": "BD",
  "horizon": "2025-10-01T00:00Z",
  "bound_clause": "[email protected]"
}

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7.1.5 Clause-Backed Simulation Requirements

Each clause that requires simulation must include:

• Model requirement (e.g., [email protected])

• Minimum run parameters

• Threshold definition for trigger

• Forecast horizon

• Accepted error bounds

• Required SimDAO endorsement level

• Execution hash for comparison

For example:

execute only if simulation("[email protected]").risk_score > 0.85

Clause approval depends on prior simulation passing all verification and attestation steps.

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7.1.6 Forecast Attestation Workflow

Every simulation run includes:

• Signed model hash

• Input provenance attestation

• Compute proof (TEE enclave or zkVM execution trace)

• SimDAO endorsement signatures

• Clause compatibility report

• Hash commitment to the Audit Layer

This package is published as a SimulationRunVC and referenced by clauses, DAOs, and CACs.

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7.1.7 Integration with Credential and Clause Layers

Simulation outcomes can:

• Issue time-bound or conditional Operational VCs (e.g., “valid only if risk remains above 0.8”)

• Trigger or delay clauses based on multivariate inputs

• Affect tiered DAO membership (e.g., elevate DisasterCoordinatorVC if assigned region exceeds threshold)

• Inform parameter updates for financial instruments and smart clauses

Simulation outputs are fully composable with the credential and governance engines defined in Chapters 5 and 6.

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7.1.8 Versioning and Simulation Forks

Model versions are tightly controlled:

• Forks require SimDAO governance

• All clause-binding simulations must reference a frozen version

• Backward-incompatible model changes invalidate active clause bindings

• Historical simulations are archived and retrievable for future dispute or delta modeling

Version control is mandatory for cross-jurisdiction interoperability.

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7.1.9 Synthetic vs Empirical Models

Simulations may be:

• Empirical (data-driven, statistically derived, e.g., Gaussian process forecast)

• Synthetic (rule-based or agent-based, e.g., policy simulation using behavioral agents)

• Hybrid (e.g., ML forecast + policy agent stress test)

Each model type must declare:

• Training data provenance

• Simulation integrity proof

• Discriminative vs generative role in clause triggering

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7.1.10 The Simulation Layer as Global Risk Foresight Infrastructure

NSF’s simulation engine is not for analysis—it is for governance.

It provides:

• Clause legitimacy

• Policy validation

• Execution eligibility

• Treaty preconditions

• Risk modeling

• Institutional coordination

…and it does so verifiably, reproducibly, and cryptographically, creating digital foresight infrastructure for machine-executable institutions.