# Scenario Modeling Framework

#### **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.

***

#### **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.

***

#### **7.1.3 Simulation Typologies**

NSF categorizes simulations as follows:

| Type                      | Example Use                                                                    |
| ------------------------- | ------------------------------------------------------------------------------ |
| **Point Forecast**        | `Rainfall > 250mm` triggers `FloodRelief@3.2`                                  |
| **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.

***

#### **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:

```json
{
  "simulation_id": "sim-0x88bc...",
  "model": "FloodSim@3.4",
  "jurisdiction": "BD",
  "horizon": "2025-10-01T00:00Z",
  "bound_clause": "FloodRelief@3.2"
}
```

***

#### **7.1.5 Clause-Backed Simulation Requirements**

Each clause that requires simulation must include:

* Model requirement (e.g., `FloodSim@3.4`)
* Minimum run parameters
* Threshold definition for trigger
* Forecast horizon
* Accepted error bounds
* Required SimDAO endorsement level
* Execution hash for comparison

For example:

```scl
execute only if simulation("FloodSim@3.4").risk_score > 0.85
```

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

***

#### **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.

***

#### **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.

***

#### **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.

***

#### **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

***

#### **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.**


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