Fictional Planetary Consequence Simulation: Causal Architecture and Visual Evolution

Full report

The development of a robust, reproducible, and educational planetary simulation for fictional nuclear events demands a rigorous separation of deterministic physical modeling from generative narrative synthesis. To fulfill this requirement without presenting the system as a highly classified weapons-effect calculator, the architecture relies on a neuro-symbolic design paradigm1. This framework integrates a deterministic computational core—leveraging hybrid System Dynamics (SD) and Agent-Based Modeling (ABM)3—with a constrained large language model (LLM) that translates the resulting mathematical state into qualitative educational narratives. Furthermore, the visual rendering of these consequences requires specialized WebGL and WebGPU pipelines capable of deterministic, age-driven volumetric transformations that communicate environmental and societal impacts without resorting to hyper-realistic gore or scientifically authoritative damage assessments5.

Part 1: Causal Model Overview and System Architecture

The causal architecture functions as a unidirectional pipeline, strictly isolating user interactions from the calculation of consequence states. This layered approach guarantees that the simulation state is derived exclusively from a minimal set of initiating variables interacting with enriched geospatial datasets, preventing the user from manually dictating outcomes.

1. Authoritative Input Record: This initial layer captures and locks the minimal user inputs (site selection, inherited coordinates, simulation date, scenario branch, and abstract event/weapon class). It strictly prohibits direct manipulation of consequence parameters, acting as the immutable seed for the subsequent deterministic execution.

2. Site-Context Enrichment: The system queries bounded geospatial datasets to populate the initial state with underlying population densities, infrastructure resilience, economic status, institutional robustness, and environmental biomes. Missing data is handled via regional median fallbacks to guarantee a fully populated starting graph.

3. Event-Profile Selection: The architecture translates the abstract event class (e.g., high-altitude EMP, surface detonation) into a qualitative physics profile. This establishes the relative initial intensities of blast, thermal, and radiation signals without relying on precise, classified device designs.

4. Immediate Consequence Calculation: Computes the instantaneous spatial overlaps between the event profile and the site context. Using deterministic spatial decay functions, this layer derives the immediate infrastructural destruction and abstract casualty zones.

5. Cascading System Effects: A hybrid SD and ABM engine models the multi-layer infrastructure failures3. It calculates how the loss of power cascades into water, communications, and transport networks, while agent-based logic determines population displacement vectors based on resource scarcity.

6. Time-Evolution Engine: Advances the simulation state through discrete event ticks. To prevent state desynchronization between continuous SD flows and discrete ABM events, the engine utilizes conservative time management algorithms (consistent with IEEE 1516 High Level Architecture principles)10.

7. Uncertainty and Confidence Layer: Quantifies missing data and model assumptions, propagating epistemic and aleatory uncertainty through the system in alignment with NASA-STD-7009 credibility assessment guidelines12.

8. Visual-State Derivation: Translates the quantitative, multidimensional simulation state into abstract rendering instructions (e.g., normalized intensity channels for smoke, infrastructure loss, and displacement) suitable for the client's graphics engine.

9. Narrative Explanation: Passes the deterministic state graph to a constrained LLM. The AI synthesizes causal chains and educational summaries based exclusively on the verified output state, functioning purely as a read-only explainer.

10. Audit and Replay Record: Serializes the entire execution branch—including initial inputs, external datasets utilized, random seeds, and transition logic—into a JSON-LD format using the PROV-O ontology, guaranteeing exact reproducibility and interoperability14.

Input Versus Derived-Variable Matrix

The integrity of the simulation requires a strict delineation between variables the user controls, variables inherited from the environment, and variables generated by the physics and socio-economic engines.

Consequence Domains and Interdependencies

The simulation relies on a technology-neutral consequence model evaluating interconnected socio-technical domains. Because power networks, communication systems, and transportation logistics form multi-layer interdependent networks, the failure of a single node in one layer invariably precipitates cascading failures across others9. The domains are processed sequentially in the time-evolution engine to accurately model load redistribution and subsequent failure cascades17.

Dynamic Time-Phase Model

In traditional simulations, a single, arbitrary "horizon" defines the timeline. To ensure causal accuracy, this architecture defines dynamic phase boundaries dictated by the event's scale, the regional context, and the resulting cascading failures. An event in a highly isolated region stretches the "First Hours" chaos into days due to a lack of immediate external intervention.

• Immediate (T+0 to T+1h): Dominated by physics. Shockwaves, thermal pulses, loss of electromagnetic signal. The state graph is heavily weighted toward deterministic spatial overlap.
• First Hours (T+1h to T+12h): Realization and acute reaction. Unorganized evacuation, grid instability, first responder mobilization. ABM agent behavior dominates this phase.
• First Day (T+12h to T+24h): The boundary of local self-reliance. Medical capacity reaches its peak surge; early signs of systemic cascading failures appear as backup generator fuels deplete.
• Days 2–7 (The Cascade Phase): Water reservoirs empty, logistics freeze, and mass migration begins. For smaller events, stabilization may begin here. For major events, this phase marks complete socio-technical systemic collapse.
• Weeks 2–4 (The Institutional Phase): External aid arrives, military or federal governance supersedes local authority, and displacement centers establish permanence.
• Months 2–6 (The Stabilization Phase): Re-establishment of basic perimeters, organized decontamination efforts, and long-term economic assessment.
• Long-Term Recovery (Months 6+): Shift from crisis management to chronic adjustment. Permanent demographic shifts and infrastructure redesign become the primary simulation outputs.

Causal-Chain Contextual Examples

The deterministic model inherently diverges based on site context. The following non-operational, qualitative examples demonstrate how identical abstract event classes yield radically different simulated outcomes:

1. Dense Urban Region vs. Remote Region: An event over a megacity triggers a massive ABM displacement wave, causing multi-state logistical gridlock and instantly collapsing regional health systems. The same event in a remote region causes minimal displacement but features a prolonged environmental persistence phase due to a lack of accessible cleanup infrastructure.

2. Strong Infrastructure vs. Fragile Infrastructure: A metropolis with buried power and distributed smart-grid topology contains cascading power failures within a 50 km radius. A city with fragile, highly centralized infrastructure experiences a total cascading grid failure spanning 500 km, leading to a secondary crisis of water purification.

3. Coastal vs. Inland Context: A coastal event contaminates port logistics, halting global maritime shipping lanes and accelerating regional economic disruption. An inland event isolates the contamination but relies heavily on highly vulnerable highway choke points for mass evacuation.

4. Isolated Event vs. Preexisting Conflict: An event in a politically stable zone prompts rapid, unified diplomatic aid. An event in a conflict zone triggers rapid military escalation, as fragmented command structures interpret the event as a preemptive strike, actively blocking humanitarian corridors.

5. Single Event vs. Broader Exchange: A single event results in targeted national resource allocation to the affected zone. A broader exchange exhausts national resources instantly, forcing local institutional continuity to operate entirely independently for months.

6. Robust Institutions vs. Fragmented Governance: A region with high institutional governance manages structured evacuations, reducing panic and maintaining information integrity. Fragmented governance collapses entirely, allowing misinformation to drive chaotic, fatal displacement vectors into uninhabitable biomes.

Deterministic Core and AI Explanation Boundary

The simulation enforces a strict neuro-symbolic separation to mitigate AI hallucination in mission-critical education systems1. Generative models predict the most statistically likely linguistic output, which introduces unacceptable stochastic variance into mathematical consequence modeling. Therefore, the architecture dictates that the LLM functions purely as a downstream interpreter. The Deterministic Engine Owns: All state generation, variable calculation, spatial decay, timing, routing, casualty estimation, and multi-layer infrastructure network logic. The engine produces a finalized JSON-LD state graph14. The Optional Language Model May:

• Summarize the derived JSON-LD output graph into natural prose.
• Explain the causal chains explicitly present in the data (e.g., "The water supply failed on Day 3 because the power grid cascade removed pumping capacity").
• Identify sources of uncertainty by reading the assumption ledger.
• Compare divergent branches side-by-side using the data structures provided.

The Optional Language Model Must Not:

• Invent authoritative states, numerical estimates, or geographic events not present in the JSON-LD payload.
• Change computed values to enhance narrative drama.
• Override safety boundaries or generate operational targeting, blast, or weapons-effect data.
• Silently alter the simulation's underlying assumptions.

Fallback Behavior: If the LLM provider experiences an outage, high latency, or returns output failing structural verification, the UI defaults to rendering the deterministic state via standard, templated string components. The visual and numerical simulation remains fully operational without the AI narrative layer.

Uncertainty Model and Assumption Ledger

Because the simulation relies on incomplete real-world data and qualitative approximations, transparently conveying uncertainty is paramount. Grounded in the NASA-STD-7009 guidelines for Models and Simulations12, the uncertainty model tracks multiple dimensions of credibility:

• Missing Data: Flagged when geospatial enrichment fails (e.g., unknown regional hospital capacity), triggering median fallback values.
• Model Uncertainty (Epistemic): Inherent limitations in the rulesets (e.g., abstract blast radius formulas replacing fluid dynamics).
• Scenario Uncertainty (Aleatory): Natural variability in population distribution (e.g., day vs. night populations at the time of the event).
• Confidence Ranges: Outputs are generated as qualitative bounds (e.g., "Displacement: High") rather than precise integers, preventing false precision.

These factors populate a user-facing Assumption Ledger. This UI component lists all deterministic assumptions utilized during the run (e.g., "Assumed weather: clear," "Assumed governance response: nominal"). Users can inspect this ledger to understand the qualitative confidence of the current simulation branch, aligning with the "Input Pedigree" and "Results Robustness" criteria of NASA's Credibility Assessment Scale13.

Reproducibility Contract and Determinism

Simulations must be historically replayable and scientifically consistent. Given the cross-platform nature of web applications, hardware differences—specifically IEEE-754 floating-point standard variations in JavaScript and the presence or absence of Fused Multiply-Add (FMA) instructions—can lead to butterfly-effect divergences in ABM engines over thousands of simulation ticks21. To guarantee an ironclad reproducibility contract:

1. Strict Seed Management: All stochastic processes rely on a seeded pseudo-random number generator (PRNG), such as Mulberry32 or PCG32, initialized with the branch's unique hash23. Math.random() is strictly prohibited throughout the codebase.

2. Floating-Point Determinism: The engine utilizes fixed-point arithmetic or strict software-defined math libraries for critical calculations (e.g., agent distances, decay rates) to prevent platform-specific rounding deviations across operating systems26.

3. Model Versioning: The exact semantic version of the rule engine and the underlying dataset hash are stored in the state. Older branches execute using their original logic versions, ensuring historic replays do not drift when the engine is upgraded.

4. Narrative Isolation: Provider-generated narrative differences are excluded from the state hash. The narrative is a downstream derivative; a change in LLM wording does not constitute a new simulation state and does not break branch reproducibility.

Result Schema (PROV-O/JSON-LD)

The output relies on a JSON-LD data structure leveraging the W3C PROV-O ontology to maintain a strict, interoperable provenance graph14.

JSON { "@context": \["https://schema.org/", "http://www.w3.org/ns/prov\#"\], "@type": "prov:Entity", "eventIdentity": "UUID-9876-5432", "branchIdentifier": "branch-omega", "replayIdentifier": "seed-mulberry-42", "provenance": { "@type": "prov:Activity", "modelVersion": "2.1.0", "datasetHash": "sha256-abc123xyz", "executionDate": "2026-07-31T06:00:00Z" }, "inputFacts": { "site": {"lat": 48.8566, "lon": 2.3522}, "date": "2026-11-01T12:00:00Z", "eventClass": "abstract-surface-yield" }, "assumptions": \[ {"domain": "weather", "value": "nominal-clear", "confidence": "high"}, {"domain": "infrastructure", "value": "fragile", "confidence": "medium"} \], "phaseTimeline": { "immediate": {"endTime": 1}, "firstDay": {"endTime": 24}, "cascadePhase": {"endTime": 168} }, "causalOutputs": { "infrastructure": {"powerGridLoss": "severe", "commsLoss": "moderate"}, "population": {"displacementVolume": "high", "displacementVectors": \[0.4, 0.8\]} }, "uncertainty": { "overallConfidence": "moderate", "contestedVariables": \["hospitalCapacity"\] }, "visualEffectInstructions": { "plumeIntensity": 0.85, "surfaceStressRadius": 65.0 }, "explanation": { "summary": "LLM-generated contextual summary of the cascading failure...", "llmVersion": "gpt-4-0613" } }

Validation Framework

To validate qualitative models that lack complete real-world ground truth (and where producing such truth is ethically impossible), the architecture employs a rigorous multi-tiered validation methodology:

1. Unit-Level Rule Testing: Isolated tests verifying individual SD equations and ABM agent logic.

2. Conservation and Boundedness Checks: Asserts that displaced persons do not exceed the underlying population, and that infrastructure cannot degrade below 0% functionality.

3. Monotonicity Tests: Ensures that destructive physical effects (e.g., radiation, thermal heat) decay over distance and time, never spontaneously intensifying without a secondary causal trigger.

4. Scenario Invariants: Tests that a zero-yield or null-class event produces exactly zero consequences across all domains.

5. Expert Review: Structured walkthroughs with subject matter experts to validate the plausibility of derived causal chains.

6. Sensitivity Analysis: Perturbations applied to input variables to ensure the model does not exhibit volatile, non-linear collapse over trivial input changes (avoiding chaotic attractors in the SD model).

7. Counterfactual Comparison: Automated generation of parallel branches altering a single variable (e.g., dense vs. sparse population) to ensure relative outcomes match expected theoretical behavior.

8. Regression Fixtures: A comprehensive suite of historical outputs that must match byte-for-byte upon any engine refactor.

9. Deterministic Replay: Unit tests enforcing that identical seeds yield bit-for-bit identical JSON-LD outputs across differing CPU architectures (ARM vs. x86).

10. Visual-Output Consistency: Automated headless rendering checks (e.g., via Puppeteer) comparing output pixels against baseline regression fixtures using Structural Similarity Index Measure (SSIM).

Acceptance Criteria (Architecture)

The following criteria govern the successful implementation of the causal architecture.

Part 2: Time-Evolving Nuclear Consequence Visuals and Globe Transitions

Visualizing planetary consequences requires a delicate balance between scientific representation and qualitative educational abstraction. The planet's imagery must evolve based strictly on simulated time, rather than wall-clock animation. To achieve performance and accessibility, the rendering architecture utilizes multi-layer compositing, volumetric impostors, and precomputed atmospheric scattering, deliberately avoiding hyper-realistic gore or precise weapons-effect visualizations in favor of abstract consequence layers.

Visual-Effect Taxonomy

The globe compositing engine separates consequence effects into distinct, deterministically rendered layers. Each layer corresponds to specific variables within the visualEffectInstructions of the JSON-LD payload.

Simulated-Day Storyboard

The globe's appearance derives directly from the authoritative simulation date. The transition of layers tells the educational story of the event.

Transition Curves

Animations driven by continuous wall-clock time break deterministic replay. Instead, all visual states are calculated as a mathematical function of the event's age ([Figure omitted from source export]).

• Fade-in / Flash: Dirac delta function or extremely steep exponential decay ([Figure omitted from source export]) for rapid onset.
• Radius Growth: Logarithmic curve ([Figure omitted from source export]), growing rapidly initially and plateauing as kinetic energy dissipates.
• Plume Rise & Drift: Linear functions of [Figure omitted from source export] for position, coupled with exponential decay for volumetric density.
• Edge Diffusion: Gaussian blur radius increases proportionally to [Figure omitted from source export], softening boundaries over time to represent atmospheric mixing.
• Decay & Recovery: Sigmoid (logistic) curves. Recovery remains at nearly [Figure omitted from source export] until a threshold time is reached, then follows an S-curve back toward baseline, allowing gradual, natural-looking transitions rather than abrupt opacity toggles.

Why Opacity Alone is Insufficient: Changing opacity alone is mathematically lazy and visually inaccurate; a smoke plume fading out while remaining perfectly still looks like a fading photograph, not a physical phenomenon. To convey physical dispersion, the plume must simultaneously increase its spatial scale, lower its relative altitude, soften its texture boundaries via edge diffusion, and drift laterally as its opacity drops.

Event-Profile Differences

While the simulation is strictly qualitative, visual profiles must vary to reflect different scenarios based on the user's initial selection:

• Surface-Level Event: Characterized by a large permanent surface mark, a dense local cloud corridor containing heavy particulate matter, and high desaturation of the regional stress layer.
• High-Altitude Event: Minimal surface mark. Massive, immediate EMP blackout of night-lights spanning a massive radius. Widespread, thin atmospheric haze rather than a dense local plume.
• Underground Event: Highly localized surface mark. Almost no atmospheric plume or global haze. No cloud corridor is generated.
• Underwater Event: No permanent surface scar on the terrain. A massive, rapidly dispersing cloud corridor (representing steam/water vapor). Coastline regional stress applied due to modeled tsunami effects.
• Broader Exchange (Multi-event): Planetary dimming (LUT grading) aggressively triggers. The aggregate atmosphere turns noticeably gray/brown. Massive, contiguous regional stress masks envelop entire continents.

Surface-Image Transitions

Base Earth imagery cannot abruptly swap textures when transitioning from pristine to damaged without jarring the user and breaking immersion. The architecture mandates:

1. Multi-resolution Texture Blending: Base textures are preserved. A secondary, grayscale "damage" texture is mapped globally but driven by a localized shader mask (a spatial radial gradient centered on the event).

2. Shader-Based Tinting: Instead of loading heavy new textures, fragment shaders desaturate and tint the existing diffuse map based on the event's local intensity channel.

3. Affected-Area Decals: For the immediate local mark, spherical decals (correcting for WGS84 distortion)28 are projected onto the globe's geometry, utilizing alpha-blending with Perlin noise to create organic, irregular edges.

4. Zoom-Level Behavior: At macro levels, procedural shaders apply tinting. As the camera zooms in, level-of-detail (LOD) systems selectively fade in high-resolution decals to maintain texture fidelity.

Multi-Event Composition

When multiple events are triggered on the same globe, the graphics engine must composite them without generating visual chaos or shader artifacting.

• Maximum Active Effect Contributors: The shader limits localized calculations (e.g., volumetric raymarching for specific plumes) to the most recent 16 events to maintain performance budgets. Older events are baked into a global persistent damage texture cache.
• Per-Channel Saturation Limits: Damage cannot exceed 100%. Overlapping regional stress masks use a maximum-blend mode (e.g., max(stressA, stressB)) to prevent overlapping semi-transparent black layers from creating "black hole" artifacts.
• Deterministic Draw Order: Shaders render decals and alpha layers sorted strictly by the event timestamp, then by unique Event ID, ensuring z-fighting does not cause flickering across subsequent replays.
• Aggregate Atmosphere: Individual hazes are summed into a global scalar variable, which drives a single Bruneton atmospheric scattering parameter, ensuring performance and preventing the atmosphere from blowing out to pure white.

Consequence-Layer Monitor (UI)

To provide educational context, the UI includes a Consequence-Layer Monitor—a compact, draggable panel on desktop, or a bottom sheet on mobile.

• Active Event & Phase: Clearly displays the current state (e.g., "Event Alpha — Phase: Days 2-7").
• Simulated Age: "Time since event: 142 hours."
• Playing/Paused State: Clear iconography indicating if the simulation clock is actively advancing.
• Strongest Active Channels: A dynamic bar chart illustrating the driving variables (e.g., "Displacement: 85%", "Atmospheric Haze: 40%").
• Visual Legend: Color swatches matching the WCAG-compliant Okabe-Ito accessible color palette30 (e.g., Orange \= Infrastructure loss, Sky Blue \= Recovery) for all illustrative dispersions.
• Explanation: A brief LLM-derived or rule-based string explaining why the layer looks the way it does (e.g., "Haze is dispersing globally as particulates settle").

Time Controls

The graphics engine is a slave to the authoritative scenario date; it does not maintain its own internal physics clock.

• Play/Pause: Halts the advancement of the simulation date. All particle systems, plume drift, and visual maturity freeze instantly.
• Scrub / Jump / Reverse: Because the visual state is a mathematical function of [Figure omitted from source export], jumping to any date calculates the exact visual state deterministically. There is no requirement to "simulate forward" to reach a future visual state.
• Reduced Motion: Complies with accessibility standards by pausing cloud drift, pulsing indicators, and camera shakes, while preserving the static consequence visualization.

Performance Architecture

To maintain 60 FPS across diverse devices, the rendering engine must degrade gracefully based on hardware capabilities:

• Discrete GPU (Desktop): Full raymarched volumetric clouds5, real-time Bruneton atmospheric scattering7, and high-resolution multi-layered decals.
• Integrated GPU (Laptop): Replaces expensive raymarching with volumetric impostors (pre-rendered billboards dynamically aligned to the camera)29. Lowers atmospheric scattering sample counts.
• Mobile Browser: Uses simple sprite-based particle systems for plumes. Atmospheric scattering is replaced with a standard vertex-shader skybox tint. Decal counts are strictly capped.
• Reduced-Power Mode: Caps framerate at 30 FPS, disables offscreen caching to save memory, and relies purely on 2D texture blending.
• WebXR: Targets 90 FPS stereoscopic rendering by aggressively culling off-screen decals and locking all volumetric effects to low-sample impostors.

Visual Validation Plan

Since visual output must be reproducible and stable across updates:

1. Image-Difference Testing: Headless browsers (Puppeteer) execute specific branches and dates, rendering the canvas and performing a pixel-by-pixel diff against a known baseline. A structural similarity index (SSIM) of \>0.99 is required.

2. Deterministic Date Fixtures: A test suite forces the clock to exact timestamps (e.g., T+86400 seconds) to verify that mathematical interpolation curves output exact opacity floats to the shaders.

3. Layer-Isolation Tests: Renders each consequence domain independently to ensure opacity blending functions do not improperly multiply or hide critical information.

4. Pause and Replay Tests: Verifies that a simulation paused at Day 5, scrubbed to Day 10, and scrubbed back to Day 5 produces a bit-for-bit identical frame buffer.

5. Accessibility Checks: Validates contrast ratios of the Consequence-Layer Monitor and ensures the Okabe-Ito palette is strictly enforced via automated linter checks.

Acceptance Criteria (Visuals)

The following criteria govern the successful implementation of the visual and rendering architecture.

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