REAL-WORLD INTERPRETIVE
One-sentence brief
No single model family is universally realistic. Understanding what each family aggregates is essential before interpreting its output.
REAL-WORLD INTERPRETIVE
SIMULATION LITERACY
Agent-Based, Compartmental, Cellular, and Network Models
A comparative guide to four common model families and the kinds of heterogeneity, interaction, and uncertainty each preserves.
LEVEL 1
ORIENTATION
Why this matters
REAL-WORLD INTERPRETIVE
Three key points
- Agents preserve individual variation but require many assumptions.
- Compartments expose aggregate flows but hide pathways.
- Cells and networks make local structure visible while inheriting grid or topology uncertainty.
LEVEL 2
WORKING BRIEF
Evidence, context, and limits
REAL-WORLD INTERPRETIVE
Agent-based models
Agent-based models represent many entities with states, attributes, and rules. They can explore heterogeneity, local adaptation, and emergent patterns, but their apparent richness can conceal weak calibration or arbitrary behavioral rules.
- Document every agent state and transition.
- Run multiple seeds and distributions.
- Never encode protected identity as inherent risk or compliance.
REAL-WORLD INTERPRETIVE
Compartmental models
Compartmental models group entities into states and model flows between them. They are often interpretable and computationally efficient, but they assume meaningful aggregation and may miss network clustering or individual pathways.
- Publish flow equations conceptually without presenting them as universal truth.
- Test alternative compartment definitions.
- Check conservation and stock-flow consistency.
REAL-WORLD INTERPRETIVE
Cellular models
Cellular models divide space into cells whose states change using local rules. They make adjacency and diffusion visible, but the chosen grid, neighborhood, and update order can materially shape the outcome.
- Test grid orientation and resolution.
- Record synchronous or asynchronous update rules.
- Avoid treating empty data cells as empty populations.
REAL-WORLD INTERPRETIVE
Network models
Network models represent nodes and connections such as contact, trade, transport, information, or dependency. They can reveal centrality and cascades, but edge meaning and missing links require careful provenance.
- Define what an edge means.
- Separate observed from inferred connections.
- Do not convert centrality into guilt, importance, or target value.
REAL-WORLD INTERPRETIVE
Compare structural assumptions
Use the same scenario across more than one plausible model family when structural uncertainty is high. Agreement can increase confidence only when the models and data are genuinely independent.
- Trace shared assumptions.
- Do not count near-duplicate models as independent corroboration.
- Explain disagreement rather than averaging it away.
REAL-WORLD INTERPRETIVE
Human review remains necessary
A model can pass automated tests while still misrepresenting people, cultures, access conditions, or legal rights. Domain, accessibility, regional, and lived-experience review should challenge the model's categories and consequences.
- Review categories before tuning parameters.
- Invite correction from affected communities.
- Preserve dissent and unresolved questions.
LEVEL 3
COMPLETE DOSSIER
Limitations, game links, and review context
DISPUTED / MULTIPLE ACCOUNTS
Known limitations and gaps
- This is a non-operational educational transformation: it does not build or implement a game, executable simulator, forecasting service, emergency tool, or decision system.
- This page is an educational transformation of supplied research leads; it does not authenticate every citation, equation, product claim, or institutional attribution in those files.
- A scenario, model run, or map is not an observation, forecast, legal finding, public-health instruction, emergency warning, or proof of future behavior.
- No source code, nuclear-effects formula, casualty calculation, target-selection method, cyber-intrusion procedure, exploit chain, or instruction for bypassing safeguards is published.
- Geography, nationality, ethnicity, religion, language, migration, disability, health, poverty, or political identity are not inherent danger, compliance, intelligence, competence, or worth variables.
- Specialist scientific, public-health, accessibility, legal, regional, ethics, security, and lived-experience review remains pending; the page stays open to correction.
GAME MECHANIC
RogueIntelligence.org connections
- Evidence and uncertainty methodUse provenance, confidence, alternatives, and correction before interpreting a model.
- Synthetic-cohort fairness gatesSee a bounded repository example where a failed baseline remains visible and human approval stays separate.
- International comparison methodApply equal analytical method without making one country or data environment the default.
REAL-WORLD INTERPRETIVE
Glossary cross-reference
Terms are defined for this site’s evidence method, not as universal legal or clinical definitions.
LEVEL 4
RESEARCH EDITION
Sources, methods, and stable links
REAL-WORLD INTERPRETIVE
Linked reports
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Building Epidemiological ModelsProtected research reference; no public report route in this release.
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AI Takeover Simulation MapProtected research reference; no public report route in this release.
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Planetary Simulation Game ArchitectureProtected research reference; no public report route in this release.
REAL-WORLD VERIFIED
Method and corrections
This page follows the public method for provenance, confidence, source independence, alternative accounts, limitations, review state, and visible correction.
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CONTINUE
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