REAL-WORLD INTERPRETIVE
One-sentence brief
A model that reproduces one historical curve may still fail elsewhere or for the wrong reasons. Validation and sensitivity prevent a fit from becoming false authority.
REAL-WORLD INTERPRETIVE
SIMULATION LITERACY
Calibration, Validation, and Sensitivity
How to tune a model, test it against evidence, and reveal which assumptions drive its conclusions.
LEVEL 1
ORIENTATION
Why this matters
REAL-WORLD INTERPRETIVE
Three key points
- Calibration selects parameters; validation tests performance on evidence not used for tuning.
- Sensitivity asks how conclusions change when assumptions change.
- Passing a bounded test is evidence within scope, not proof of reality or production readiness.
LEVEL 2
WORKING BRIEF
Evidence, context, and limits
REAL-WORLD INTERPRETIVE
Calibration
Calibration estimates or selects parameters so model outputs align with chosen data or constraints. It should document targets, loss functions, search ranges, prior assumptions, and overfitting risk.
- Separate calibrated from fixed parameters.
- Use data quality and uncertainty in the objective.
- Do not hide manual adjustments.
REAL-WORLD INTERPRETIVE
Validation
Validation compares the model with observations, holdout periods, alternative datasets, or known accounting identities. A model may be useful for one purpose and invalid for another.
- Define success before testing.
- Use out-of-sample or forward evaluation where possible.
- Report failures and subgroup gaps.
REAL-WORLD INTERPRETIVE
Sensitivity analysis
Sensitivity analysis varies inputs, structures, and rules to identify what controls the result. Local sensitivity tests small changes; global sensitivity explores combinations and interactions.
- Prioritize assumptions with high impact and low evidence.
- Show direction and magnitude of change.
- Do not present one parameter set as inevitable.
REAL-WORLD INTERPRETIVE
Identifiability and equifinality
Different parameter combinations or mechanisms can produce similar outputs. A good fit does not automatically identify the true causal process.
- Compare multiple plausible explanations.
- Use diagnostic observations, not only aggregate fit.
- Mark parameters that cannot be estimated from available data.
REAL-WORLD INTERPRETIVE
External validity
A model calibrated in one period or jurisdiction may fail under different institutions, mobility, climate, access, reporting, or behavior. Transfer requires new evidence and review.
- Recalibrate or bound transfer claims.
- Do not use one country as the universal baseline.
- Publish where the model has not been tested.
REAL-WORLD INTERPRETIVE
Release decision
Automated validation can support a release gate, but it does not replace legal, ethical, accessibility, scientific, regional, or lived-experience approval.
- Keep human approval state explicit.
- Rerun after material changes.
- Preserve failed baselines as regression evidence.
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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IARPA SimEarth Game ArchitectureProtected 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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