idealisations

Idealisations are deliberate simplifications or abstractions used in reasoning, explanation, and scientific modeling to focus on essential features of a system. An idealisation may replace complex real-world details with a simpler representation and may also make assumptions that do not hold in reality, yet are useful for analysis. In the philosophy of science, idealisations are distinguished from mere everyday fictions by their role in generating general laws, predictions, and mechanistic explanations. They are common across disciplines and often accompany the formulation of theories.

Types of idealisations include mathematical idealisations, such as a frictionless plane or a point mass, empirical idealisations which replace noisy data with averaged or smooth representations, and theoretical or ceteris paribus idealisations where many variables are held fixed to isolate a mechanism. Idealised models enable tractable mathematics, clearer causal insight, and the testing of core hypotheses. They are typically understood as tools whose adequacy is judged by the robustness of predictions under more realistic conditions, rather than by exact empirical accuracy.

Critics worry that excessive or inappropriate idealisation can obscure important constraints and mislead policy or practice. Proponents reply that idealisations are often necessary to identify underlying regularities and to build cumulative knowledge, provided the limits of applicability are acknowledged. In ongoing practice, scientists distinguish between the idealized core of a model and the real-world perturbations that may affect outcomes, using robustness checks and iterative refinement to bridge ideal and actual conditions.