Work in progress IARPG-OPS-2 2.0.21-wip

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Search the operations design system

Type to search standards, missions, roles, authorities, reports, sources, pages, schemas, and UAI memory.

Scenario literacy · not a prediction engine

Model systems.
Do not mistake them for certainty.

IARPG uses systems thinking to make fictional consequences easier to inspect: what changed, why it changed, what evidence supports it, which assumptions matter, who is affected, and how a different decision could produce another branch.

This is a content method for missions, alliances, logistics, infrastructure, information environments, and institutional change. The site is not building a planetary simulator, nuclear-effects calculator, public-health model, digital twin, or live geopolitical forecast.

Direct answer

What does scenario modeling mean on iarpg.com?

It means using explicit states, relationships, assumptions, evidence, and reversible events to explain fictional consequences. A model is selected because it fits a question—not because it looks impressive. Every public model needs a purpose, truth boundary, accessible representation, uncertainty statement, validation record, and a clear list of interpretations it does not support.

Current use
Campaign state, alliance change, mission consequences, logistics, and evidence comparison
Not current use
No planetary game, real-world prediction, medical model, or weapons calculator
Machine method
Five model families and required model-card fields
Research review
Eight supplied reports, 48 bounded publication decisions

Choose the lens by the question

Five model families.
Five different kinds of answer.

No single simulation form explains everything. Each family makes some mechanisms visible and compresses others. The publication therefore records what a model is for, what it hides, and what decisions it must not support.

01

Footprint / snapshot model

What area or set of systems is affected under stated assumptions?

State
One evaluated time or event boundary
Best for
fictional outage zones · access restrictions · jurisdiction overlays
Must show
origin · assumptions · layer meaning · data date · uncertainty
Not for
target optimization · casualty calculation · weapons effects
02

Propagation / compartment model

How might state move between categories over time?

State
Time-stepped flows among explicit states
Best for
information spread · supply disruption · institutional strain
Must show
transition rules · time step · parameter ranges · sensitivity · baseline comparison
Not for
individual diagnosis · medical advice · deterministic prediction
03

Agent and network model

How can local actor rules and relationships produce system-level patterns?

State
Actors, links, local decisions, and aggregate outcomes
Best for
alliance behavior · logistics · trust and coordination
Must show
actor types · decision rules · network boundary · internal diversity · false-positive risks
Not for
identity profiling · recruitability scoring · dangerousness scoring
04

Event-sourced branch model

How did the state change, and what differs across alternative histories?

State
Immutable baseline plus ordered, reversible events
Best for
campaign turns · corrections · branch replay · institutional history
Must show
prior version · reason code · evidence · dissent · consequences · reversal conditions
Not for
rewriting source history · omniscient truth claims
05

Comparative / ensemble model

Which findings persist across different assumptions or model forms?

State
Multiple scenarios, models, or parameter sets
Best for
uncertainty communication · stress testing · policy tradeoffs
Must show
common question · model differences · range · outliers · decision relevance
Not for
averaging away disagreement · single-score certainty

Model card before model output

A credible scenario explains
how it can be wrong.

A polished map or animation is not validation. Before any scenario result appears, the publication should expose the question, data, assumptions, state rules, uncertainty, verification, validation, rights implications, and correction history.

That structure follows the same principle already used by IARPG campaign turns: the current state is not enough. Readers need the prior state, reason, evidence, dissent, consequences, and reversal conditions.

Where the method improves IARPG

System consequences become
more explainable content.

The reports are used to deepen existing pages and standards—not to launch a separate planetary game. Each application stays fictional, evidence-led, reversible where possible, and bounded against real-world profiling or targeting.

MISSION CONSEQUENCES

Mission consequences

Explain how evidence quality, access, civilian impact, and institutional response change after a fictional operation.

ALLIANCE CASCADES

Alliance cascades

Trace domain-specific cooperation, burden, dependency, dissent, correction, and reversible realignment.

INFRASTRUCTURE STRESS

Infrastructure stress

Show dependencies, service loss, restoration, and unequal effects without exposing real targets.

INFORMATION ENVIRONMENTS

Information environments

Compare rumor, correction, confidence, and trust pathways without profiling real people.

LOGISTICS AND ACCESS

Logistics and access

Describe routes, bottlenecks, jurisdiction, and recovery as fictional system states.

Attribution correction

IARPA is a real U.S. research organization.
This site is not affiliated with it.

The supplied reports repeatedly expand IARPA as “Integrated Artificial Reality Planetary Atlas.” That is a fictional phrase, not the agency’s name.

The official name is Intelligence Advanced Research Projects Activity. Its public mission is high-risk/high-payoff research for difficult Intelligence Community challenges, and the agency states that it is not operationally focused. IARPG may cite public program descriptions as research context, but it must not imply sponsorship, endorsement, partnership, or an official planetary-simulation program.

Hard boundary

No model may turn people
or places into targets.

The repository keeps the useful architecture and rejects the unsafe interpretation.

Scenario is not prediction.

Visualization is not validation.

Precision must not exceed evidence.

Generated narrative cannot own authoritative state.

Real agency names and programs require primary-source verification.

Every model needs an accessible non-visual representation.

No model may create identity-based dangerousness, recruitability, targetability, or moral scores.

Additional boundary: the preserved source on nuclear-effects simulation remains archival evidence. Current IARPG content does not reproduce weapon-effect equations, casualty calculators, fallout models, target optimization, or deployment guidance.

From shock to recovery

A scenario crosses institutions, networks, and people.

Use the World Systems sequence to connect an initial condition with authority, domestic response, regional coordination, material dependencies, evidence quality, unequal impacts, correction, and recovery. The model remains conditional and must show disagreement, sensitivity, affected communities, and reversal conditions.

Connected tools and standards

Explore the wider AI ecosystem.