Models

A model is a simplified representation of a system, object, or process that omits irrelevant detail to focus on essential features. They can be physical, such as scale models or prototypes; abstract, such as theories and formulas; or computational, such as simulations. Models are used to explain, predict, or control phenomena, balancing realism with tractability. They can be descriptive, explanatory, or predictive, depending on purpose.

Scientific and engineering models include theoretical frameworks, statistical representations, and computer simulations. In science, models are tested against observations to assess validity and to understand mechanisms. In engineering, scale models and prototypes help study performance before full-scale implementation.

In data science, models are mathematical or algorithmic constructs that infer patterns from data and generate predictions. Machine learning models—such as regression, decision trees, and neural networks—learn from examples and are evaluated on accuracy, robustness, and generalization.

For society and culture, the term model can also refer to a person who displays clothing or goods in fashion or advertising. In biology, model organisms (for example, fruit flies or mice) serve as stand-ins to study processes that are impractical in humans.

Modeling involves problem formulation, selecting a representation, constructing the model, calibrating parameters, and validating results against data. A model’s usefulness depends on its assumptions, data quality, and the alignment with its intended purpose. Limitations include simplification errors, uncertainty, overfitting, and biases; clear communication of methods and uncertainty is essential.