If an approximation is accurate, the model will have high predictive power; for example, it is not usually necessary to account for air resistance when determining the acceleration of a falling bowling ball, and doing so would be more complicated.
For example, the approximation of air resistance as zero was the only option before the formulation of Stokes' law allowed the calculation of drag forces.
Galileo, in his study of bodies in motion, set up experiments that assumed frictionless surfaces and spheres of perfect roundness.
An example of the use of idealization in physics is in Boyle's Gas Law: Given any x and any y, if all the molecules in y are perfectly elastic and spherical, possess equal masses and volumes, have negligible size, and exert no forces on one another except during collisions, then if x is a gas and y is a given mass of x which is trapped in a vessel of variable size and the temperature of y is kept constant, then any decrease of the volume of y increases the pressure of y proportionally, and vice versa.
It has been argued by the "Poznań School" (in Poland) that Karl Marx used idealization in the social sciences (see the works written by Leszek Nowak).
In psychology, idealization refers to a defence mechanism in which a person perceives another to be better (or have more desirable attributes) than would actually be supported by the evidence.
This leads him to the conclusion that “[t]ruly important and significant hypotheses will be found to have ‘assumptions’ that are wildly inaccurate descriptive representations of reality, and, in general, the more significant the theory, the more unrealistic the assumptions (in this sense).”[3] Consistently with this, he makes the case for seeing the assumptions of neoclassical positive economics as not importantly different from the idealizations that are employed in natural science, drawing a comparison between treating a falling body as if it were falling in a vacuum and viewing firms as if they were rational actors seeking to maximize expected returns.
[4] Against this instrumentalist conception, which judges empirical theories on the basis of their predictive success, the social theorist Jon Elster has argued that an explanation in the social sciences is more convincing when it ‘opens the black box’ — that is to say, when the explanation specifies a chain of events leading from the independent variable to the dependent variable.
[5] Relatedly, he also contends that social-scientific explanations should be formulated in terms of causal mechanisms, which he defines as “frequently occurring and easily recognizable causal patterns that are triggered under generally unknown conditions or with indeterminate consequences.”[6] All this informs Elster's disagreement with rational-choice theory in general and Friedman in particular.
[13] There is continued philosophical concern over how Galileo's idealization method assists in the description of the behavior of individuals or objects in the real world.