Scientific modelling

Scientific modelling is the practice of constructing and using simplified representations of real-world systems to explain observations, test hypotheses, and make predictions.

Mathematical model: A representation of a system using mathematical structures and relationships. Equations make assumptions explicit and allow quantitative predictions.
Idealization (science): The deliberate omission or simplification of features in a scientific representation. Every useful model leaves out details to isolate tractable patterns.
Climate model: A mathematical representation of interactions governing Earth's climate system. Climate models project outcomes under different greenhouse-gas pathways.
Scientific theory: A well-substantiated explanatory framework that organizes evidence and generates testable claims. Theories explain broad patterns; models represent selected aspects for particular tasks.
Scenario analysis: The examination of possible future conditions using internally consistent sets of assumptions. Models turn alternative assumptions into comparable conditional futures.
Computer simulation: A computational imitation of a system’s behavior over time or under specified conditions. Simulations explore consequences that are difficult to calculate or observe directly.
Scientific hypothesis: A proposed, testable explanation for an observed phenomenon. Models give hypotheses explicit structures and derive consequences that can be tested.
Epidemiological model: A mathematical representation of how disease spreads through populations. Epidemic models compare interventions and estimate transmission dynamics.
Scientific law: A concise statement describing a regularity in nature, often expressed mathematically. Laws summarize regularities, while models represent systems and derive conditional outcomes.
Decision theory: The formal study of choosing actions under uncertainty and competing objectives. Model outputs can inform choices when outcomes and risks must be compared.

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