{
  "$schema": "https://iarpg.com/scenario-modeling-methods.schema.json",
  "id": "https://iarpg.com/scenario-modeling-methods.json",
  "release": "IARPG-OPS-2",
  "version": "2.0.21-wip",
  "status": "work-in-progress",
  "title": "IARPG Scenario Modeling and Systems Thinking Methods",
  "truth_boundary": "This is a content and model-literacy framework for fictional IARPG scenarios. It is not a scientific forecast, operational intelligence product, medical model, weapons-effects calculator, digital twin, or live simulation service.",
  "model_families": [
    {
      "id": "footprint",
      "label": "Footprint / snapshot model",
      "question": "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"
      ]
    },
    {
      "id": "propagation",
      "label": "Propagation / compartment model",
      "question": "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"
      ]
    },
    {
      "id": "agent-network",
      "label": "Agent and network model",
      "question": "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"
      ]
    },
    {
      "id": "event-sourced",
      "label": "Event-sourced branch model",
      "question": "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"
      ]
    },
    {
      "id": "ensemble",
      "label": "Comparative / ensemble model",
      "question": "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"
      ]
    }
  ],
  "required_model_card_fields": [
    "purpose",
    "decision question",
    "scope",
    "inputs and dates",
    "state variables",
    "transition rules",
    "assumptions",
    "outputs",
    "uncertainty",
    "verification",
    "validation",
    "sensitivity",
    "affected communities",
    "rights and privacy",
    "prohibited interpretations",
    "correction history"
  ],
  "content_applications": [
    {
      "id": "mission-consequences",
      "label": "Mission consequences",
      "description": "Explain how evidence quality, access, civilian impact, and institutional response change after a fictional operation."
    },
    {
      "id": "alliance-cascades",
      "label": "Alliance cascades",
      "description": "Trace domain-specific cooperation, burden, dependency, dissent, correction, and reversible realignment."
    },
    {
      "id": "infrastructure",
      "label": "Infrastructure stress",
      "description": "Show dependencies, service loss, restoration, and unequal effects without exposing real targets."
    },
    {
      "id": "information",
      "label": "Information environments",
      "description": "Compare rumor, correction, confidence, and trust pathways without profiling real people."
    },
    {
      "id": "logistics",
      "label": "Logistics and access",
      "description": "Describe routes, bottlenecks, jurisdiction, and recovery as fictional system states."
    }
  ],
  "publication_rules": [
    "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."
  ]
}