Embedded Simulations and Dynamic Thresholds

Binding Predictive Intelligence Directly Into Clause Logic for Foresight-Driven Execution

3.8.1 Why Simulations Must Be Embedded

Traditional rulesets operate on static thresholds, arbitrary boundaries, or historical precedent.

But in dynamic, risk-prone systems, we need governance that:

• Adapts to changing environmental or economic conditions

• Anticipates future scenarios—not just responds to past ones

• Encodes probabilistic foresight into deterministic execution

• Enables multi-domain coordination (e.g., flood + food + finance)

NSF introduces Embedded Simulations: Clauses that natively execute or call simulations as part of their logic flow, not as preconditions.

These clauses evaluate forward-looking risk at execution time and adjust their behavior accordingly.

---

3.8.2 What Is an Embedded Simulation?

An Embedded Simulation is a reference to a foresight model, declared inside clause logic using:

simulate: useModel("[email protected]") with inputBindings(...)

Or:

forecastResult = simulate("[email protected]") with {
  location: "Nairobi",
  rainfall7d: sensorData.rainfall
}
if forecastResult.riskScore > 0.85 {
  issue("AnticipatoryFinanceVC")
}

Embedded simulations:

• Are executed in secure runtimes (TEE or ZK)

• Can produce forecasts, classifications, or quantitative scores

• Are domain-specific, version-controlled, and governance-approved

• Must be included in CAC with full traceability

---

3.8.3 Simulation Model Requirements

To be embedded, a simulation model must:

• Be registered in the Simulation Layer

• Be versioned and reviewed by domain DAO(s)

• Declare its:

  • Input schema

  • Expected outputs

  • Runtime (e.g., TensorFlow, PyTorch, GAML)

  • Governance status

  • Link to data sources (EO, sensor, economic, social)

• Be reproducible and auditable

Optionally, it may also include:

• Confidence intervals

• Epistemic risk flags

• ZK-compatible outputs

• Reviewer signatures

---

3.8.4 Types of Simulations Supported

Type	Use Case
Deterministic models	Hydrology, logistics, policy logic
Stochastic simulations	Climate projections, health crises
Agent-based models	Migration, crowd dynamics
AI/ML models	Forecasting, risk scoring, classification
Bayesian inference systems	Confidence-scored causal prediction
Hybrid composites	Integrated disaster-economic-social models

Each type may support batch execution, real-time simulation, or on-demand inference.

---

3.8.5 Dynamic Thresholds in Clause Logic

Dynamic thresholds replace static “if X > Y” logic with:

threshold = simulate("RiskForecast") with {...}
if sensor.value > threshold.dynamicValue then execute(...)

These thresholds:

• May evolve based on data and model forecasts

• Are cryptographically verifiable

• Include simulation provenance and audit metadata

• Can adapt to localized or seasonal factors

Example:

simulate: [email protected] with { soilMoistureTrend, forecastRainfall }
require: soilMoisture < simulatedThreshold

---

3.8.6 Simulation Output Handling and Constraints

Simulation results inside clauses must:

• Declare output structure in the clause schema

• Include runtime, model ID, and parameter trace

• Pass domain-specific constraint checks (e.g., variance bounds, confidence ≥ 90%)

• Be recorded in CAC metadata

Sensitive models can expose only the output, with input + execution verified using:

• TEE attestation

• zkSNARKs or zkSTARKs

• Aggregated multi-signer attestations

---

3.8.7 Simulation-Linked Clause Behavior

Clause logic may adapt based on simulation outcomes:

Output	Result
riskScore > 0.9	Issue anticipatory action credential
yield forecast drops 20%	Trigger payout or crop diversification action
infectiousness curve flattens	Revoke quarantine credential
infrastructure model shows overload	Pause energy-intensive contracts
climate projection exceeds cap	Trigger emissions override clause

This makes governance data-adaptive and model-driven, not fixed and reactive.

---

3.8.8 Governance of Embedded Simulations

All embedded simulation models must be:

• Reviewed by domain-specific DAOs

• Associated with simulations of record

• Audited periodically for drift or obsolescence

• Replaceable via DAO vote or auto-deprecation triggers

A clause cannot run if its embedded simulation is:

• Expired

• Revoked

• Unavailable

• Proven to produce invalid outputs under current conditions

This ensures epistemic resilience and policy-valid simulation dependency.

---

3.8.9 ZK and TEE Execution for Simulations

Simulations may be:

• Executed inside TEEs (Intel SGX, AMD SEV, etc.)

• Transpiled into ZK circuits for verifiable execution

• Checked by multiple enclave validators

• Audited via Simulation Run Certificates ([email protected])

This provides execution attestation and input-output traceability without leaking sensitive parameters.

CACs always contain:

• Simulation ID

• Execution hash

• Output + parameter summary

• Model version and jurisdictional bindings

---

3.8.10 Embedded Simulation as Policy Foresight Infrastructure

In NSF, embedded simulation:

• Turns every clause into a risk-aware actor

• Aligns enforcement with future conditions

• Encodes uncertainty-aware logic

• Bridges science, law, and action

• Prevents static policies from governing dynamic crises

Simulations are not optional—they are:

• Bound to execution

• Anchored in governance

• Auditable across jurisdictions

• Verifiable across agents

This transforms policy into living, foresight-driven infrastructure.

---