Phenomenological model
A phenomenological model (sometimes referred to as a statistical model) is a mathematical expression that relates several different empirical observations of phenomena to each other, in a way which is consistent with fundamental theory, but is not directly derived from theory. In other words, a phenomenological model is not derived from first principles. A phenomenological model foregoes any attempt to explain why the variables interact the way they do, and simply attempts to describe the relationship, with the assumption that the relationship extends past the measured values.[1][page needed] Regression analysis is a popular example of a phenomenological model.
Examples of Use
Phenomenological models have been characterized as being completely independent of theories,[2] though many phenomenological models, while failing to be derivable from a theory, incorporate principles and laws associated with theories.[3] The liquid drop model of the atomic nucleus, for instance, portrays the nucleus as a liquid drop and describes it as having several properties (surface tension and charge, among others) originating in different theories (hydrodynamics and electrodynamics, respectively). Certain aspects of these theories—though usually not the complete theory—are then used to determine both the static and dynamical properties of the nucleus.
References
- ^ Hilborn, Ray; Mangel, Marc (2013). The Ecological Detective Confronting Models with Data (MPB-28) (Online-Ausg. ed.). Princeton: Princeton University Press. ISBN 9781400847310.
- ^ McMullin, Ernan (1968), “What Do Physical Models Tell Us?”, in B. van Rootselaar and J. F. Staal (eds.), Logic, Methodology and Science III. Amsterdam: North Holland, 385–396.
- ^ Roman, Frigg; Hartmann, Stephan. "Models in Science". In Zalta, Edward N. (ed.). The Stanford Encyclopedia of Philosophy (Fall 2012 ed.). Retrieved 24 July 2015.