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
Translate the supplied epidemiological-modeling material into safe public guidance on model literacy, comparative scenarios, state machines, uncertainty, and validation.
Scope
This canonical report covers the user-supplied file Building Epidemiological Models.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 describes browser-based agent models, compartmental models, finite-state transitions, numerical solvers, and visual teaching techniques. For IARPG, these ideas support explanation of local actor behavior, system-level outcomes, and side-by-side scenario comparison. The repository does not treat the source as medical guidance, an outbreak forecast, or a validated epidemiological model.
Evidence Reviewed
- Preserved source file
- Source collection:
planetary-systems-and-scenario-modeling - Source SHA-256:
7f937df97e28cf1704f5cbd0d455ad6f8475e198d277359c39217ac14dd3a264 - Source identity: user-supplied Markdown described as IARPA-related material; not authenticated as an official IARPA publication.
- IARPA mission — official agency identity, research mission, and non-operational boundary (accessed 2026-07-22).
- IARPA research programs — official program directory (accessed 2026-07-22).
- GAO infectious disease modeling practices — communication, model description, verification, and validation (accessed 2026-07-22).
- NIST digital-twin credibility work — 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-OPS2-218-02-01 |
agent and compartmental models | retained-method-guidance | Retain the distinction between local-agent simulation and aggregate compartmental flow models. |
PSR-OPS2-218-02-02 |
public-health prediction | rejected-for-public-reuse | Do not present the source code or outputs as medical advice, outbreak prediction, or policy recommendation. |
PSR-OPS2-218-02-03 |
causal communication | retained-method-guidance | Prefer parallel baseline and intervention scenarios with the changed assumptions stated explicitly. |
PSR-OPS2-218-02-04 |
numerical implementation | requires-domain-validation | Treat numerical methods and parameter choices as illustrative until independently verified by qualified modelers. |
PSR-OPS2-218-02-05 |
population profiling | safety-restricted | Do not infer individual dangerousness, compliance, health status, or identity from aggregate scenario models. |
PSR-OPS2-218-02-06 |
uncertainty | bounded | Publish ranges, sensitivities, omissions, and validation limits beside any model output. |
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
- Agent-based models make local rules visible; compartmental models summarize population-level flows. Each reveals different information and hides different detail.
- State definitions and transition rules should be explicit, versioned, and inspectable before visual animation is treated as evidence.
- Side-by-side baseline and intervention scenarios communicate causality more honestly than changing several variables in one animated run.
- Sensitivity analysis and uncertainty ranges are more useful than one precise-looking output when parameters are weakly known.
- IARPG can adapt these lessons to fictional influence, logistics, trust, and institutional response while avoiding medical claims or real-person risk scoring.
Decisions or Recommendations
- Route public readers to the Scenario Modeling and Systems Thinking 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/simulation.uai.uai/scenario-comparison.uai.uai/representation-safety.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.
