# Geospatial Propagation and Cascade Simulation Patterns

## Status

Current repository artifact for IARPG-OPS-2 2.0.18-wip. Review status: **reviewed with limitations for scenario literacy and public explanation**. The preserved source file remains individually addressable as provenance. This report is not an official IARPA publication.

## Purpose

Extract safe, reusable systems-thinking lessons from the supplied geospatial propagation report for IARPG content, scenario comparison, and campaign explanation without implementing a cyberattack or takeover simulator.

## Scope

This canonical report covers the user-supplied file `AI Takeover Simulation Map.md`. It is authoritative for repository provenance, the bounded synthesis below, and IARPG content-routing decisions. It does not establish official agency affiliation, scientific model validity, public-health guidance, weapons effects, real-world geopolitical prediction, or a current software implementation.

## Executive Summary

The source contrasts instantaneous footprint models with chronological propagation models and then extends those patterns into a competitive AI scenario. The reusable value is architectural and editorial: choose a model family that matches the question, separate authoritative state from visualization, expose time and uncertainty, compare interventions side by side, and keep generated narrative subordinate to deterministic records. The repository rejects the source's offensive cyber framing, takeover premise, exploit-oriented examples, and implementation snippets as public design guidance.

## Evidence Reviewed

- [Preserved source file](../../source-files/planetary-systems-and-scenario-modeling/AI%20Takeover%20Simulation%20Map.md)
- Source collection: `planetary-systems-and-scenario-modeling`
- Source SHA-256: `658092e6a3bec8e57a8340f128465dc29d20ec15320b63940ce51c3d05e67e31`
- Source identity: user-supplied Markdown described as IARPA-related material; not authenticated as an official IARPA publication.
- [IARPA mission](https://www.iarpa.gov/who-we-are/history/our-mission) — official agency identity, research mission, and non-operational boundary (accessed 2026-07-22).
- [IARPA research programs](https://www.iarpa.gov/research-programs) — official program directory (accessed 2026-07-22).
- [GAO infectious disease modeling practices](https://www.gao.gov/assets/gao-20-372.pdf) — communication, model description, verification, and validation (accessed 2026-07-22).
- [NIST digital-twin credibility work](https://www.nist.gov/programs-projects/digital-twins-advanced-manufacturing) — verification, validation, and uncertainty considerations (accessed 2026-07-22).

## Reviewed Synthesis

### Publication Decision

Retain the source as preserved research evidence. Reuse only the bounded statements in this section and the repository-authored findings below. Do not treat the original title, acronym expansion, code, quantitative claims, named technology stack, or scenario outputs as current fact without a new review.

### Claim Dispositions

| Claim ID | Topic | Disposition | Current bounded statement |
|---|---|---|---|
| `PSR-OPS2-218-01-01` | model taxonomy | **retained-method-guidance** | Retain the distinction between instantaneous footprint models and time-stepped propagation models. |
| `PSR-OPS2-218-01-02` | AI takeover framing | **rejected-for-public-reuse** | Do not present a helpful-versus-harmful AI takeover scenario as a real forecast, official IARPA project, or current product direction. |
| `PSR-OPS2-218-01-03` | cyber implementation detail | **safety-restricted** | Do not publish exploit logic, breach propagation code, target selection, or operational cyberattack guidance. |
| `PSR-OPS2-218-01-04` | state authority | **retained-method-guidance** | Keep authoritative state deterministic and treat generative text as a bounded explanatory layer. |
| `PSR-OPS2-218-01-05` | current software claims | **requires-current-verification** | Re-check runtime, framework, library, and performance claims against current primary documentation before reuse. |
| `PSR-OPS2-218-01-06` | map interpretation | **bounded** | Describe maps as scenario visualizations with assumptions and uncertainty, not observed truth or prediction. |

### Attribution, Fairness, and Safety Boundary

IARPA is the **Intelligence Advanced Research Projects Activity**, a U.S. research organization that invests in high-risk/high-payoff research for Intelligence Community challenges and is not itself operationally focused. The phrase “Integrated Artificial Reality Planetary Atlas” is a fictional construction in the supplied material and must never be presented as the agency’s name, product, endorsement, or program. Apply equal evidence standards, disclose uncertainty, include rights and affected-community consequences, and keep all reuse non-actionable.

### Reuse Rule

Use the smallest applicable bounded statement above. Re-check current agency programs, laws, software, vendors, performance, market data, and scientific claims against current primary or authoritative sources before public factual reuse.

## Findings

- A footprint model answers “what area is affected under stated assumptions?” while a propagation model answers “how might state change over time?”; they should not be presented as interchangeable.
- Map layers should communicate model output rather than masquerade as ground truth. Inputs, scale, time step, confidence, and omitted variables must remain visible.
- Competitive or multi-process scenarios are better represented as domain-specific state transitions than as a single global control score.
- Narrative-generation systems may explain or label events, but they must not author authoritative state, evidence, permissions, or outcomes.
- IARPG can reuse cascade concepts for rumor spread, alliance stress, infrastructure dependency, and access loss without simulating real cyber intrusion or “AI takeover.”

## Decisions or Recommendations

- Route public readers to the [Scenario Modeling and Systems Thinking](/scenario-modeling) page rather than to implementation snippets in the preserved source.
- Label every model family with purpose, inputs, assumptions, state variables, outputs, uncertainty, verification, validation, affected communities, and prohibited interpretations.
- Prefer side-by-side scenario comparison, reversible state history, and accessible tables over spectacular but unexplained visualization.
- Keep real IARPA references confined to accurate, sourced descriptions of the agency and its public research programs.
- Preserve the original source unchanged in the source archive, but do not duplicate unsafe or misleading implementation detail into current public guidance.

## Risks and Limitations

- The preserved source contains unsupported, time-sensitive, implementation-specific, and sometimes hazardous detail.
- This review is editorial and architectural; it is not scientific peer review, model accreditation, public-health review, legal review, weapons-effects validation, or security certification.
- Official program pages can change after the research cutoff; future releases must re-check time-sensitive references.
- A scenario can improve reasoning while still embedding bad assumptions. Visible structure does not prove real-world validity.

## Validation Performed

- Verified the preserved source SHA-256 and one-to-one canonical mapping.
- Compared agency identity and selected program descriptions against official IARPA pages on 2026-07-22.
- Compared model-credibility language with GAO and NIST guidance.
- Reviewed the synthesis for attribution, safety, accessibility, international fairness, uncertainty, and non-prediction boundaries.
- Verified repository-relative links and required report sections through the local memory and source-integrity validators.

## Memory References

- [`.uai/planetary-systems.uai`](../../../.uai/planetary-systems.uai)
- [`.uai/simulation.uai`](../../../.uai/simulation.uai)
- [`.uai/scenario-comparison.uai`](../../../.uai/scenario-comparison.uai)
- [`.uai/ai-source-validation.uai`](../../../.uai/ai-source-validation.uai)

## Related Durable Documents

- [Planetary Systems Research Integration and Scenario Modeling Report](planetary-systems-research-integration-and-scenario-modeling-report.md#findings)
- [Rapid Deep Research Methodology and Guidance](rapid-deep-research-methodology-and-guidance.md#findings)
- [Equal Standards Across Unequal Records](equal-standards-across-unequal-records-international-institutional-fairness-rights-and-evidence-audit.md#mandatory-analytical-distinctions-and-rights-safeguards)

## Supersession Status

Current for IARPG-OPS-2 2.0.18-wip until superseded by a later reviewed synthesis. The preserved source remains archival provenance and does not override active `.uai` memory or verified implementation.