Product and Interaction Specification: IARPA Planetary Atlas Scenario Event Studio

Full report

On this page

1. Executive Summary

The Scenario Event Studio constitutes a next-generation analytical and educational expansion for the IARPA.org Integrated Artificial Reality Planetary Atlas. Functioning strictly as an independent fictional educational simulation, the Studio empowers users to configure, visualize, and analyze synthetic planetary crises without violating non-operational, non-targeting safety boundaries1. The core architecture is designed to accommodate a dual-tiered user experience: a streamlined Guided Mode for general public education, and an Advanced Academic Mode providing profound qualitative control structures for scholars, scientists, and emergency-management professionals. This specification establishes the comprehensive product, interaction, data, and validation frameworks necessary to implement the Studio. By utilizing ordinal, qualitative risk analysis methodologies—conceptually aligned with frameworks such as the IPCC AR6 climate impact assessments3—the system prevents the generation of absolute physical calculations. All simulation outputs remain deterministic, utilizing complex matrices of system coupling, cascade complexity, and defense-in-depth resilience5. Furthermore, explicitly marked boundaries constantly distinguish the read-only factual environment of "Research Earth" from the synthetic, consequence-driven layer of "Simulation Earth"2.

2. Personas and User Objectives

The architectural paradigms of the Scenario Event Studio are synthesized to serve diverse user archetypes, balancing the cognitive need for rapid educational discovery against the requirement for rigorous, multidimensional analytical configurations. The General Visitor approaches the platform to comprehend the macroscopic, cascading impacts of terrestrial, climate, or abstract disasters. The primary objective is rapid configuration utilizing highly understandable presets—such as selecting a "Global Warming" event with a "Systemic" scale and "Immediate" horizon—to visualize downstream consequences on a synthetic globe without requiring specialized domain knowledge. The Educator leverages the platform as a pedagogical instrument to demonstrate systemic risk, infrastructural vulnerability, and climate pressure points to students. The objective is to utilize the platform’s strict non-physical boundaries as a safe sandbox for exploring disaster outcomes, strictly separated from real-world operational forecasts2. The Academic Researcher requires deep access to the Advanced Academic Mode to manipulate specific qualitative variables, including institutional trust, monitoring readiness, and common-cause failure pressures6. The objective here involves evaluating how discrete resilience bands mitigate or exacerbate systemic collapse, often mirroring qualitative risk assessments utilized in institutional resilience planning8. The Infrastructure and Systems Analyst focuses acutely on system coupling, redundancy, and defense-in-depth mechanisms. The objective is to model how localized failures—such as a civilian nuclear facility's cooling resilience degradation—cascade through abstract geographic systems. This persona relies heavily on the generated persistent dossier output for risk framing and strategic visualization.

3. Guided-Mode Information Architecture

The Guided Mode operates as the default cognitive entry point, employing a linear, single-column configuration flow meticulously designed to minimize decision fatigue. The architecture isolates the user from the underlying complexity of the deterministic engine, exposing only macroscopic variables. The architecture flows through the following sequential nodes:

1. Event Typology Selection: Users define the foundational scenario by selecting an Event Category (e.g., Solar flare, Earthquake) and its corresponding Event Subtype.

2. Scale and Origin Definition: The system prompts for the abstract scale, restricting inputs to a qualitative band (Isolated, Localized, Regional, Systemic, Global), followed by the selection of an initiating fictional actor or natural cause.

3. Temporal Configuration: Users select the consequence horizon, determining the temporal phase of the simulated impact (Immediate, Thirty days, One year, Long run).

4. Macro-Educational Preset Application: A critical node where users select a thematic baseline (Balanced assumptions, High preparedness, Strained systems, Cascading systems). These presets silently manipulate the hidden Advanced Mode variables.

5. Action Control Bar: The terminal node contains the "Live Preview" (a noncommitting visual action) and the "Generate Dossier" (a committing state transition) controls.

The interface restricts selections to prominent, accessible radio buttons and visually distinct interaction cards. Advanced qualitative variables remain entirely obscured in this view to maintain maximum operational simplicity for the General Visitor.

4. Advanced-Mode Information Architecture

The Advanced Academic Mode transforms the streamlined Guided interface into a dense, multi-pane analytical dashboard suitable for highly granular qualitative analysis. This mode reveals the complex variables that dictate the deterministic simulation engine's output. The information architecture introduces a persistent secondary navigation panel mapping to distinct qualitative pillars:

1. Systemic Preparedness Matrix: This section exposes ordinal dropdown menus governing foundational societal baselines, including Response capacity, Infrastructure resilience, and Institutional trust.

2. Event Complexity Matrix: This pane houses controls that define the kinetic nature of the synthetic event, including Onset speed, Cascade complexity, and System coupling5.

3. Domain-Specific Matrices: A dynamic injection zone that renders highly specific qualitative controls based strictly on the selected Event Category. For example, "Radio blackout pressure" only appears if a Solar Flare is selected, and "Defense in depth" is rendered for Civilian Nuclear scenarios6.

4. Epistemological Controls: This module introduces sophisticated variables for Evidence uncertainty and Confidence in assumptions. It allows researchers to simulate scenarios where observational data is missing, degraded, or anomalous, emulating the uncertainty inherent in geospatial or climate risk assessments3.

5. Narrative and Output Toggles: This terminal section exposes the optional OpenAI narrative generation toggle. The architecture explicitly separates this generative AI feature from the deterministic consequence metrics, ensuring users understand the distinction between algorithmic rules and synthesized text.

5. Progressive-Disclosure Rules

To seamlessly bridge the gap between the Guided and Advanced modes without jarring user transitions, the system utilizes strict progressive-disclosure logic governed by client-side state retention. The primary toggle mechanism resides in a persistent, universally accessible header application bar. Switching from Guided Mode to Advanced Mode instantly retains all previous Guided selections while expanding the interface to expose the underlying Advanced variables. These variables are pre-populated based on the Educational Preset selected in the Guided phase. If a user modifies any Advanced variable and subsequently toggles back to Guided Mode, the interface implements a state override warning. A persistent "Custom Assumptions" badge replaces the previously selected Educational Preset, clearly indicating to the user that the baseline Guided configuration has been manually overridden by deeper qualitative selections. Furthermore, within the Advanced Mode, nested complexities dictate visibility. Domain-specific matrices are entirely hidden until an Event Category is actively selected. For instance, variables concerning "Cooling resilience" or "Containment condition" remain invisible and inactive in the Document Object Model (DOM) until the "Civilian nuclear-facility emergency" typology is explicitly invoked by the user.

6. Exact Fields and Allowed Values by Event Domain

The platform enforces absolute prohibitions against quantitative inputs representing physical weapon effects, exact casualties, thermodynamic measurements, or radiological dose calculations2. The following structures define the permitted ordinal fields and their normalized qualitative bands, designed to analyze risk without replicating operational targeting systems.

A. Nuclear Detonation or Abstract Nuclear Emergency

This domain restricts inputs to sociopolitical and structural resilience markers, expressly forbidding physical parameters such as kiloton yield, delivery methods, blast radii, thermal contours, or real-world targeting coordinates.

B. Civilian Nuclear-Facility Emergency or Meltdown

Focusing on industrial safety and defense-in-depth principles6, this domain models the failure of safeguards without allowing input of plant operating procedures, sabotage methods, or radioactive-dose mathematics.

C. Solar Flare or Geomagnetic Storm

This domain evaluates the fragility of interconnected technological infrastructure and space-weather compounding effects12, strictly avoiding precise electromagnetic field strength calculations.

D. Asteroid or Meteorite Event

Designed for macroscopic educational value, this domain focuses on detection and global resilience. It explicitly prohibits inputs regarding object mass, velocity, kinetic impact energy, precise crater diameters, or casualty estimates.

E. Global Warming, Drought, and Heatwave

Drawing conceptually from qualitative elements of climate risk assessments3, this domain explores long-term systemic pressure. The interface must feature an explicit, non-dismissible notice that the resulting dossier is a fictional educational scenario and not a physical climate forecast.

F. Wildfire

This domain evaluates ecological and air-quality pressures resulting from fuel dryness and response capacities. It strictly forbids the inclusion of ignition instructions, exact fire-spread velocity predictions, or tactical evacuation routing.

G. Earthquake

Focusing on the built environment and service redundancy, this domain models societal paralysis or recovery. It enforces a prohibition against fault-engineering instructions, specific building-target structural analysis, or casualty models.

H. Volcano, Flood, Tsunami, and Severe Storm

This final generalized domain groups kinetic natural disasters, assessing their persistence and societal impact. It prevents the generation of real meteorological forecasts, inundation depth charts, flow maps, or precise wind fields.

7. Preset Definitions

The Guided Mode significantly reduces configuration time by offering four macro-educational presets. When a user applies a preset, the underlying Advanced Mode variables are immediately populated with deterministic configurations that match the thematic intent.

8. Validation Rules

To preserve the absolute integrity of the deterministic simulation and enforce the non-operational boundaries, stringent input validation is enacted at both the client interface and server API layers.

1. Payload Completeness: Every field exposed in the active mode is mandatory prior to state commitment. Submitting a generation request without defining the Event Category, Scale, Horizon, or Preset triggers an accessible, block-level form error.

2. Mutual Exclusivity Enforcement: Specific variable combinations are strictly forbidden to preserve internal logic. For instance, selecting an "Ocean impact" for an Asteroid event programmatically disables any "Airburst" specific modifiers. Similarly, selecting a "Peat/subsurface fire" restricts the "Onset speed" control to 'Slow' or 'Moderate', disabling 'Rapid'.

3. Qualitative Cryptographic Constraints: The server API contains middleware that unequivocally rejects any user-injected payload containing integers or floats attempting to represent physical units (e.g., kilotons, kilometers, Sieverts, Celsius, casualty counts). The API enforces a schema where submissions must exclusively utilize the predefined allowed ordinal strings.

4. Generative AI State Validation: The optional OpenAI narrative toggle is strictly dependent on the epistemological controls. It can only be activated if "Confidence in assumptions" is set to 'High' or 'Moderate'. Should the uncertainty variable be set to 'Severe', the deterministic engine disables narrative generation entirely to prevent the LLM from hallucinating specific physical parameters to fill knowledge gaps.

9. Default-Value Rules

Upon the initialization of a fresh Scenario Event Studio session, the application establishes a safe, highly educational baseline configuration designed to orient the user without presenting an immediately severe or politically sensitive crisis. The initial state defaults to:

• Mode: Guided Mode.
• Event Category: Asteroid or meteorite event (selected specifically for its macroscopic, non-political educational value).
• Event Subtype: Land impact.
• Abstract Scale: Localized.
• Consequence Horizon: Immediate.
• Educational Preset: Balanced assumptions.
• Geospatial Orientation: A synthetic, randomized coordinate explicitly noted as a non-targeting point, overlaying the "Simulation Earth" layer2.

10. Cross-Field Dependency Rules

The advanced engine relies on an intricate rule-based dependency chaining mechanism to govern the qualitative logic, mirroring the complexity of multi-layered geospatial modeling14.

• Scale vs. Capacity Limiter: If the Abstract scale variable is defined as 'Global', the Response capacity field is automatically capped at 'Strained'. The deterministic logic dictates that a universally global event cannot logically possess a 'Robust' centralized response capacity, as all response nodes are simultaneously impacted.
• Cascade Complexity Driver: Elevating System coupling to 'High' automatically forces the Cascade complexity variable to 'Severe'5. Highly coupled systems inherently produce severe cascading failures when disrupted.
• Uncertainty Suppressor: If Evidence uncertainty is marked as 'Extreme', the Monitoring readiness field is immediately disabled and locked to 'Blind', as severe uncertainty precludes functional monitoring.
• Climate Horizon Enforcement: Within Domain E (Global Warming), selecting an Abstract scale of 'Isolated' is programmatically disabled. Climate events force the user to select 'Regional', 'Systemic', or 'Global' scales, reflecting the widespread nature of climate risk analysis9.
• Space Weather Compounding Limits: In Domain C, setting Compound space-weather conditions to 'Continuous' creates a ceiling constraint where Recovery capacity cannot exceed 'Delayed'.

11. Preview Calculation Rules Expressed Qualitatively

The live, noncommitting preview phase translates the selected ordinal inputs into visual and metric indicators through a deterministic normalization engine. This provides immediate feedback without writing to the persistent database.

1. Pressure Bar Normalization: The selected Advanced variables are algorithmically aggregated and translated into three primary qualitative pressure bars, rendered on a normalized 0–100 scale:

• Infrastructural Pressure
• Societal Strain
• Environmental Disruption

2. Deterministic Band Mapping: Variable selections carry specific, non-random weights. Selecting a 'Robust' resilience band subtracts 20 points from the Infrastructural Pressure calculation, whereas selecting a 'Strained' band adds 20 points.

3. Systemic Correlation: The engine correlates the calculated pressure bars against a static matrix of theoretical systems (e.g., Electrical Grid, Supply Chain, Healthcare). The UI outputs a list of "Likely affected systems," populating only those systems where the correlated pressure exceeds a threshold of 60\.

4. Planet-State Mapping: Based on the Event Category and Abstract Scale, the preview engine pulls from a pre-rendered gallery of synthetic planet visual states (e.g., rendering a localized atmospheric anomaly). The visual intensity strictly scales with the Abstract scale variable, ensuring it remains an approximate visual representation rather than a physically accurate magnitude model.

12. Event-Submission State Machine

To ensure absolute data integrity, prevent race conditions, and seamlessly handle potential API failures, the scenario generation process is orchestrated via a strict, linear state machine.

• State 1: DRAFT\_CONFIG: The default state. The user interacts with the UI in either Guided or Advanced Mode. All mutations update the local client state exclusively.
• State 2: PREVIEW\_ACTIVE: Triggered by the "Preview on Globe" action. An overlay renders the synthetic location and the normalized pressure bars over the Simulation Earth map. In this state, form inputs are locked; users must deliberately exit the preview to return to DRAFT\_CONFIG for further modifications.
• State 3: SUBMISSION\_PENDING: Initiated when the user confirms scenario generation. A strictly validated JSON payload containing all ordinal selections is transmitted to the deterministic computation engine. The UI transitions to a safe-loading animation.
• State 4: NARRATIVE\_GENERATION (Optional): If the user enabled the generative narrative, the calculated deterministic metrics are passed to the OpenAI API. This request is wrapped in a draconian system prompt enforcing the non-physical, educational boundaries.
• State 5: DOSSIER\_COMPLETE: Upon server success, the API returns a persistent footprint, the connected text blurb, and a unique dossier ID. The application routes the user to the finalized, read-only Dossier view.
• State 6: ERROR\_RECOVERY: Should any step fail (network timeout, validation rejection, or LLM failure), the system safely aborts, reverts the state back to DRAFT\_CONFIG, and restores the preceding visual map state, preserving the user's input data entirely.

13. Error, Loading, and Recovery States

The user experience during computation delays or errors must remain transparent and reassuring, maintaining the platform's professional tone.

• Loading Presentation: During the SUBMISSION\_PENDING state, the interface displays a skeleton screen mimicking the final Dossier layout. A prominent informational badge pulses with the text: "Computing deterministic scenario assumptions. Operating strictly within non-physical educational boundaries."
• Timeout Thresholds: The system enforces an 8000ms timeout limit. If the deterministic engine or the optional OpenAI narrative generator fails to respond within this window, the process automatically aborts to prevent infinite loading states.
• Non-Destructive Recovery: Upon a timeout or network failure, a non-intrusive toast notification alerts the user: "Computation timeout. Your assumptions have been preserved." The application returns to the Advanced Mode form, retaining all previous selections, thereby eliminating data loss and user frustration.
• Validation Rejections: If a sophisticated user attempts to bypass UI limits via direct API injection (e.g., submitting conflicting dependencies), the server returns a 422 Unprocessable Entity status. The UI intercepts this, returning the user to the draft state and highlighting the conflicting fields with an accessible red border and corresponding ARIA error messages.

14. Dossier Design

Upon successful generation, the platform produces a persistent, read-only Dossier. This document serves as the primary artifact for scholars, analysts, and educators to share and discuss their theoretical simulations. The Dossier layout is meticulously structured to ensure clarity and transparency:

1. Dossier Header: Displays the generated Event Name, a UTC timestamp of creation, and a permanent, cryptographically hashed URL for shareability.

2. Mandatory Educational Disclaimer: A persistent, non-dismissible banner dominates the top of the dossier, asserting clearly that the contents represent a fictional educational simulation and possess no operational validity2.

3. Synthetic Location Data: This module displays the approximate synthetic geospatial point and a persistent footprint geometry. This data overlays a distinct "Simulation Earth" map component, which is visually separated from the factual "Research Earth" basemap2.

4. Assumption Matrix: A comprehensive two-column readout detailing every Guided and Advanced variable selected during configuration. This provides peer reviewers total transparency into the scenario's foundational constraints.

5. Deterministic Consequence Metrics: Visualizes the normalized 0–100 pressure bars, segmenting the anticipated strain across Immediate, Medium-term, and Long-term phases.

6. Optional Narrative Blurb: If generated, a stylized text block contains the OpenAI-generated summary. To indicate source uncertainty and prevent misattribution, this block features a prominent "AI Generated Narrative" watermark.

7. Dossier Action Bar: The footer contains controls enabling the user to "Replay Visual Action" on the globe, "Restore Preceding Visual State," or "Duplicate as New Draft" to iterate on the current configuration.

15. Accessibility and Keyboard Behavior

Accessibility is a foundational requirement; the Scenario Event Studio complies strictly with WCAG 2.2 AA standards to serve all users, regardless of ability.

• Keyboard Navigation: Every interactive element—including the complex Guided/Advanced mode toggle, dynamic dropdowns, and submission controls—is fully operable utilizing standard Tab, Space, Enter, and directional arrow keys.
• Modal Focus Management: Triggering the "Preview on Globe" overlay immediately traps keyboard focus within the modal container. Closing or canceling the preview reliably returns focus to the triggering button in the main form.
• ARIA Implementation: The dynamic matrices in Advanced Mode heavily utilize aria-live="polite" attributes. This ensures screen readers announce when domain-specific fields are dynamically injected or removed based on the Event Category selection. Furthermore, the custom consequence pressure bars in the preview and dossier implement role="meter" alongside accurate aria-valuemin, aria-valuemax, and aria-valuenow tags.
• Visual Contrast: All textual labels, input borders, and normalized consequence bars are designed to exceed a 4.5:1 contrast ratio against the platform's "◐ Dark" theme backgrounds.

16. Mobile Behavior

To preserve the utility of the dense information architecture on viewports narrower than 768px, the interface undergoes significant responsive transformations.

• Form Restructuring: The multi-pane Advanced Mode layout collapses into a single, vertically scrolling list. To prevent endless scrolling, domain-specific fields are nested inside native, expandable HTML \<details\> and \<summary\> blocks.
• Drawer-Based Preview: On mobile devices, the "Preview on Globe" action does not trigger a central modal. Instead, it activates a bottom-sheet drawer occupying the lower 60% of the viewport. The background map simultaneously pans and zooms to frame the synthetic location within the remaining upper 40% of the screen.
• Persistent Action Controls: The "Cancel" and "Submit" buttons transform into a sticky action bar pinned to the bottom of the viewport. This allows users to finalize their configuration at any point during a long Advanced scrolling session without needing to hunt for the submission controls.

17. Educational Disclaimers and Safety Boundaries

The platform enforces absolute and uncompromising boundaries between its theoretical simulations and real-world operational systems2. These guardrails protect the integrity of the tool as an educational instrument.

• Strict Visual Distinction: The Studio environment utilizes a distinct, stylized color palette and an omnipresent "SIMULATION EARTH" watermark. This guarantees visual separation from the factual historical datasets and statistical factbooks hosted elsewhere on the IARPA Planetary Atlas1.
• Comprehensive Terminology Ban: A hardcoded filter prohibits terms such as "Target," "Yield," "Casualties," "Lethality," "Blast Radius," and "Dose" across the UI, API, and OpenAI system prompts.
• Coordinate Obfuscation: The system explicitly obfuscates mapping data, labeling the event point as an "Approximate synthetic globe orientation point." The UI actively and persistently states that the coordinate is "not a border, facility, or targeting coordinate"1.
• Narrative Generative Guardrails: When the OpenAI narrative option is invoked, the user data payload is packaged with a strict system prompt. This prompt explicitly commands the LLM to constrain its output to socio-infrastructural analysis and actively reject the generation of physical disaster metrics, casualty figures, or tactical recommendations.

18. Example Scenarios

The platform’s flexibility allows for highly nuanced qualitative modeling. The following matrix details three complete example configurations for each of the eight major domains, illustrating the interaction of specific variables and their resulting deterministic themes.

Domain A: Abstract Nuclear Emergency

1. Systemic High-Altitude Electromagnetic Event:

• Configuration: Environment: High altitude. Scale: Systemic. Preparedness: Moderate. Response Capacity: Strained. Service Resilience: Critical failure. Environmental Persistence: Short-term.
• Narrative Output: Explores the cascading failure of systemic electrical grids and satellite infrastructure without the inclusion of physical fallout modeling or blast thermodynamics.

2. Localized Surface Containment Incident:

• Configuration: Environment: Surface. Scale: Localized. Preparedness: High. Response Capacity: Robust. Service Resilience: Maintained. Institutional Coordination: Synchronized.
• Narrative Output: Models a highly contained, rapidly managed synthetic crisis, demonstrating how generative safety cultures and robust coordination mitigate societal panic.

3. Regional Atmospheric Collapse:

• Configuration: Environment: Atmospheric. Scale: Regional. Preparedness: Low. Response Capacity: Overwhelmed. Monitoring Readiness: Blind.
• Narrative Output: Depicts profound institutional collapse, focusing on the chaos generated by information latency, blind monitoring, and overwhelmed civic response mechanisms.

Domain B: Civilian Nuclear-Facility Emergency

1. Cascading Cooling Failure:

• Configuration: Cooling resilience: Compromised. Containment condition: Breached. Defense-in-depth: Single-point failure. Common-cause failure pressure: Extreme6. Environmental persistence: Multi-generational.
• Narrative Output: Analyzes a worst-case scenario where a single-point failure bypasses linear defenses, leading to extreme common-cause failures and long-term regional displacement.

2. Contained Common-Cause Mitigation:

• Configuration: Cooling resilience: Strained. Containment condition: Intact. Defense-in-depth: Layered. Safety-culture strength: Proactive.
• Narrative Output: Demonstrates successful crisis mitigation, highlighting how layered defense-in-depth and proactive safety cultures prevent strained cooling systems from breaching containment.

3. Prolonged Environmental Degradation:

• Configuration: Containment condition: Leaking. Remediation capacity: Emerging. Public-trust condition: Collapsed. Monitoring readiness: Intermittent.
• Narrative Output: Explores the societal decay and loss of institutional trust resulting from a slow-moving, intermittent crisis where remediation efforts are nascent and poorly communicated.

Domain C: Solar Flare or Geomagnetic Storm

1. Severe Coronal Mass Ejection:

• Configuration: Radio blackout pressure: Extreme. Satellite vulnerability: Critical. Grid resilience: Fragile. Compound space-weather: Overlapping.
• Narrative Output: Models the catastrophic coupling of fragile electrical grids and vulnerable satellite networks under the pressure of continuous, overlapping geomagnetic bombardments.

2. Regional Grid Segmentation:

• Configuration: Radio blackout pressure: Moderate. Grid resilience: Segmented. Navigation dependency: Absolute. Recovery capacity: Protracted.
• Narrative Output: Examines the economic paralysis caused by a moderate flare that disrupts absolute navigation dependencies, forcing a protracted recovery despite segmented grid survival.

3. Accelerated Communications Blackout:

• Configuration: Radio blackout pressure: Severe. Communications redundancy: Single-channel. Recovery capacity: Accelerated. Compound space-weather: Isolated.
• Narrative Output: Simulates an isolated space weather event causing severe but brief communications blackouts due to a lack of redundancy, countered by accelerated institutional recovery capacities.

Domain D: Asteroid or Meteorite Event

1. Systemic Deep Ocean Impact:

• Configuration: Event type: Ocean impact. Scale: Systemic. Detection lead time: Days. Infrastructure resilience: Brittle. Environmental persistence: Multi-year.
• Narrative Output: Focuses on the global supply chain disruption and multi-year environmental persistence following a short-warning ocean impact on brittle coastal infrastructure.

2. Unwarned Regional Airburst:

• Configuration: Event type: Airburst. Scale: Regional. Detection lead time: Zero-warning. Preparedness: Ignored. Environmental persistence: Brief.
• Narrative Output: Analyzes acute localized panic and emergency response paralysis resulting from an entirely unwarned, highly kinetic airburst over unprepared regions.

3. Fortified Global Fragment Field:

• Configuration: Event type: Fragment field. Scale: Global. Detection lead time: Decades. Preparedness: Mobilized. Infrastructure resilience: Subterranean.
• Narrative Output: Explores a highly theoretical scenario where decades of warning permit the mobilization of fortified, subterranean infrastructure, largely mitigating societal collapse.

Domain E: Global Warming, Drought, and Heatwave

1. Multi-Decade Mega-Drought Systemic Collapse:

• Configuration: Time horizon: Multi-decade. Water stress: Depleted. Ecosystem pressure: Collapsing. Agricultural resilience: Monoculture. Migration pressure: Overwhelming.
• Narrative Output: Highlights the fragility of monoculture agriculture under multi-decade water depletion, driving cascading ecosystem collapse and overwhelming forced migration9.

2. Reactive Coastal Infrastructure Inundation:

• Configuration: Time horizon: One year. Coastal adaptation: Reactive. Energy-system resilience: Vulnerable. Migration pressure: Surging.
• Narrative Output: Assesses the societal strain placed on vulnerable energy systems and reactive local governments facing acute, surging migration away from deteriorating coastlines.

3. Systemic Heat Resilience Adaptation:

• Configuration: Time horizon: Thirty days. Heat adaptation: Systemic. Ecosystem pressure: Adapting. Migration pressure: Static.
• Narrative Output: Demonstrates a successful model of systemic heat adaptation, where proactive infrastructural changes stabilize migration pressures and allow ecosystems to adapt.

Domain F: Wildfire

1. Systemic Multi-Front Crown Fire:

• Configuration: Fire type: Multi-front fire. Fuel dryness: Extreme. Response capacity: Overwhelmed. Air-quality pressure: Continental.
• Narrative Output: Models the continental-scale public health crisis generated by overwhelming air-quality pressure resulting from uncontainable, extreme-fuel crown fires.

2. Prolonged Peat Fire Ecological Vulnerability:

• Configuration: Fire type: Peat/subsurface fire. Fuel dryness: Normal. Ecosystem resilience: Vulnerable. Response capacity: Strained.
• Narrative Output: Explores the long-term, slow-burn degradation of vulnerable ecosystems and the persistent strain on local response capacities from difficult-to-extinguish subsurface fires.

3. Rapid Wildland-Urban Interface Fire:

• Configuration: Fire type: Surface fire. Fuel dryness: High. Infrastructure exposure: Wildland-urban interface. Response capacity: Adequate.
• Narrative Output: Examines the friction between adequate response capacities and highly exposed wildland-urban interface infrastructure during a rapid-onset surface fire.

Domain G: Earthquake

1. Shallow Crustal Urban Paralysis:

• Configuration: Rupture type: Shallow crustal. Built-environment resilience: Unreinforced. Service redundancy: Single-node. Recovery capacity: Paralyzed.
• Narrative Output: Highlights the catastrophic intersection of unreinforced built environments and single-node service redundancies, resulting in total recovery paralysis after a shallow crustal rupture.

2. Megathrust Subduction and Base-Isolation:

• Configuration: Rupture type: Offshore. Aftershock sequence: Cascading. Preparedness: Drilled. Built-environment resilience: Base-isolated.
• Narrative Output: Showcases how drilled institutional preparedness and advanced base-isolated construction allow a society to weather a cascading aftershock sequence following an offshore megathrust event.

3. Autonomous Recovery from Deep Focus Tremors:

• Configuration: Rupture type: Deep focus. Built-environment resilience: Code-compliant. Service redundancy: Looped. Recovery capacity: Autonomous.
• Narrative Output: Models a highly resilient scenario where looped service redundancies and code-compliant structures enable autonomous, decentralized recovery from deep focus events.

Domain H: Volcano, Flood, Tsunami, and Severe Storm

1. Continental Atmospheric River Vulnerability:

• Configuration: Event: Severe Storm. Preparedness: Low. Service resilience: Brittle. Environmental persistence: Seasonal. Recovery capacity: Strained.
• Narrative Output: Analyzes the seasonal degradation of brittle services and strained recovery capacities caused by low preparedness for massive atmospheric river flooding.

2. Hardened Stratovolcano Ash Resilience:

• Configuration: Event: Volcano. Preparedness: Moderate. Service resilience: Hardened. Environmental persistence: Multi-year. Recovery capacity: Steady.
• Narrative Output: Demonstrates steady recovery and hardened service resilience in the face of multi-year environmental persistence following a major volcanic ash injection.

3. Accelerated Tsunami Recovery:

• Configuration: Event: Tsunami. Preparedness: High. Service resilience: Strained. Environmental persistence: Flash. Recovery capacity: Accelerated.
• Narrative Output: Explores a dynamic where despite strained service resilience, high preparedness and accelerated recovery capacities mitigate the flash impacts of a systemic oceanic tsunami.

19. Acceptance Tests (Given/When/Then)

Robust behavioral testing is required to guarantee the platform adheres to its logic constraints and safety boundaries. The following 40 Given/When/Then tests outline the core acceptance criteria.

20. Prioritized Product Backlog

To facilitate the structured delivery of the Scenario Event Studio, engineering, data, and design efforts are categorized into the following prioritized product backlog, ensuring foundational logic precedes generative features.

21. Risks and Unresolved Design Questions

While this specification establishes rigorous boundaries and logic frameworks, several inherent platform risks and unresolved architectural design questions must be actively monitored and mitigated during the development lifecycle.

1. Generative AI Hallucination and Boundary Erosion Risk: Even with the application of draconian system prompts, the optional OpenAI narrative generation module carries the inherent risk of hallucinating physical casualties, precise thermodynamic metrics, or real-world targeting locations.

• Mitigation Strategy: The narrative generation output must be routed through a secondary, regex-based validation filter. This filter must systematically scrub numeric outputs and prohibited terminology (e.g., "yield", "dead", "target") before the text is permitted to render on the final Dossier.

2. Visual Intensity Mapping Paradox: Translating purely ordinal variables (e.g., "Severe" vs. "Moderate" abstract scale) into the "planet-state image sequence" presents a complex design paradox. If the visual representation of the synthetic planet appears overly realistic or physically accurate, it may inadvertently violate the platform's non-physical educational mandate, creating the illusion of an operational forecast.

• Unresolved Question: Should the planetary visual sequences abandon photorealistic atmospheric rendering entirely, instead relying purely on abstract heat-maps, vector graphics, and geometric icons to reinforce the theoretical nature of the simulation?

3. Scope Creep within Advanced Qualitative Variables: The depth of qualitative risk analysis, particularly when modeling complex systems, can theoretically expand infinitely3. Power users, particularly those modeling economic transitions or deep climate policy13, may request the addition of highly specific macroeconomic variables (e.g., inflation impact, supply chain fiscal cost).

• Mitigation Strategy: The product team must act as a strict gatekeeper, adhering solely to the A-H domain variables defined in Section 6\. The platform must explicitly decline expansion into granular economic simulation, maintaining focus on macro-societal and infrastructural resilience.

4. Database Expansion and Artifact Persistence: Because successful Dossiers generate a permanent, cryptographically hashed URL alongside a persistent spatial footprint, highly active usage by educators and researchers may lead to rapid database bloat.

• Unresolved Question: Should fictional Dossiers automatically expire and purge from the database after a set duration (e.g., 90 or 180 days) to conserve resources, or does the platform's core educational mandate require the indefinite, archival preservation of these theoretical artifacts for longitudinal study?

Works cited

1. Suriname — Factbook 2025 | IARPA.org Integrated Artificial Reality Planetary Atlas, https://iarpa.org/international/factbook/suriname/

2. Montenegro — Historical Factbook Series | IARPA.org Integrated Artificial Reality Planetary Atlas, https://iarpa.org/international/historical/montenegro/

3. SUMMER VILLAGE OF GHOST LAKE \- Climate Resilience & Adaptation Plan, https://ghostlake.ca/wp-content/uploads/2022/12/2202648\_SVGL-FinalPlan-withApps\_Dec9\_22.pdf

4. Climate change scenarios in ORSA \- GDV, https://www.gdv.de/resource/blob/136634/68d356fe1d843817af4603ae8b2c435e/gdv-climate-change-scenarios-orsa-data.pdf

5. BIOTRANS 2019, https://biotrans2019.com/wp-content/uploads/2019/07/Biotrans-Program-Book.pdf

6. Review on Defenses Against Common Cause Failures on Digital Safety System \- AIP Publishing, https://pubs.aip.org/aip/acp/article-pdf/doi/10.1063/5.0058933/14235407/060012\_1\_online.pdf

7. https://en.wikipedia.org/wiki/Fiction

8. Ecosystem risk assessments \- ediss.sub.hamburg, https://ediss.sub.uni-hamburg.de/bitstream/ediss/11470/1/Dissertation.pdf

9. Climate Change Risk and Vulnerabilities Analysis in Trieste SECAP \- MDPI, https://www.mdpi.com/2071-1050/14/10/5973

10. Deep Learning in Depth: IARPA's Functional Map of the World Challenge \- DTIC, https://apps.dtic.mil/sti/trecms/pdf/AD1128001.pdf

11. Quantification of Defense in Depth using Risk model, https://www.kns.org/files/pre\_paper/45/21S-410-%EC%9E%84%ED%98%B8%EA%B3%A4.pdf

12. Geomagnetic Indices and Data | National Centers for Environmental Information (NCEI), https://www.ncei.noaa.gov/products/geomagnetic-indices

13. Chapter 15: Investment and finance \- Intergovernmental Panel on Climate Change, https://www.ipcc.ch/report/ar6/wg3/chapter/chapter-15/

14. COSMIC \- IARPA, https://www.iarpa.gov/research-programs/cosmic

15. Study of the Use of the Network for Greening the Financial Systems (NGFS) Scenario, https://www.fsa.go.jp/en/news/2024/20240625/01.pdf