He Scientific model Is an abstract representation of phenomena and processes to explain them. Through the introduction of data in the model allows to study the final result.
To make a model it is necessary to raise certain hypotheses, so that the representation of the result that we want to obtain is as exact as possible, as well as simple to be easily manipulated.
There are several types of methods, techniques and theories for the shaping of scientific models. And in practice, each branch of science has its own method for making scientific models, although it may include models from other branches to verify its explanation.
The principles of modeling allow the creation of models serving the branch of science that they try to explain.
The way to construct models of analysis is studied in the philosophy of science, the general theory of systems and in the scientific visualization.
In almost all explanations of phenomena one can apply one model or another, but it is necessary to adjust the model to use, so that the result is as exact as possible.
You may be interested The 6 Steps of the Scientific Method and What They Consist .
General parts of a scientific model
Rules of representation
For the creation of a model a series of data and an organization of the same are needed. From a set of input data, the model will provide a series of output data with the result of the hypotheses raised
Internal structure
The internal structure of each model will depend on the type of model we are proposing. Normally, it defines the correspondence between the input and the output.
The models can be deterministic when each input corresponds to the same output, or also, non-deterministic, when the same input corresponds to different outputs.
Types of models
Models are distinguished by the form of representation of their internal structure. And from there we can establish a classification.
Physical models
Within the physical models we can distinguish between theoretical and practical models. The most commonly used types of model are models and prototypes.
They are a representation or copy of the object or phenomenon to study, that allows to study the behavior of the same in different situations.
It is not necessary that this representation of the phenomenon be carried out on the same scale, but are designed in such a way that the resulting data can be extrapolated to the original phenomenon taking into account its size.
In the case of theoretical physical models, they are considered models when the internal dynamics is not known.
These models seek to reproduce the phenomenon studied, but not knowing how to reproduce it include hypotheses and variables to try to get the explanation of why that result is obtained. It is applied in all variants of physics, except in theoretical physics.
Mathematical models
Within the mathematical models is sought to represent the phenomena through a mathematical formulation. This term is also used to designate geometric models. They can be divided into other models.
The deterministic model is one in which it is assumed that the data are known, and that the mathematical formulas used are accurate to determine the result at any time, within the observable limits.
Stochastic or probabilistic models are those in which the result is not exact, but a probability. And in which there is an uncertainty as to whether the model's approach is correct.
Numerical models, on the other hand, are those that through numeric sets represent the initial conditions of the model. These models are the ones that allow simulations of the model by changing the initial data to know how the model would behave if it had other data.
In general, mathematical models can also be classified depending on the type of inputs they work with. They can be heuristic models where explanations are sought for the cause of the phenomenon being observed.
Or they may be empirical models, where the results of the model can be verified through the outputs obtained from observation.
And finally, they can also be classified according to the goal they want to achieve. They can be simulation models that try to predict the results of the phenomenon being observed.
They can be optimization models, in these the operation of the model is considered and an attempt is made to find the point that can be improved to optimize the result of the phenomenon.
Finally, they can be control models, where they try to control the variables to control the result obtained and to be able to modify it if necessary.
Graphic models
A representation of data is performed through graphic resources. These models are usually lines or vectors. These models facilitate the vision of the phenomenon represented through tables and graphs.
Analog model
It is the material representation of an object or process. It is used to validate certain hypotheses that would otherwise be impossible to contrast. This model is successful when it is possible to provoke the same phenomenon that we are observing, in its analogue
Conceptual models
They are maps of abstract concepts that represent the phenomena to study including assumptions that allow to glimpse the result of the model and can be adjusted to him.
They have a high level of abstraction to explain the model. They are the scientific models per se, where the conceptual representation of the processes manage to explain the phenomenon to be observed.
Representation of models
Conceptual type
The model factors are measured through an organization of the qualitative descriptions of the variables to be studied within the model.
Of mathematical type
Through a mathematical formulation the models of representation are established. It is not necessary that they be numbers, but the mathematical representation can be algebraic or mathematical graphs
Of physical type
When establishing prototypes or models that try to reproduce the phenomenon to study. In general they are used to reduce the scale necessary for the reproduction of the phenomenon that is being studied.
References
- BOX, George EP. Robustness in the Strategy of Scientific Model Building. Robustness in Statistics, 1979, vol. 1 p. 201-236.
- BOX, George EP; HUNTER, William Gordon; HUNTER, J. Stuart.Statistics for experimenters: an introduction to design, data analysis, and model building. New York: Wiley, 1978.
- VALDÉS-PEREZ, Raúl E.; ZYTKOW, Jan M.; SIMON, Herbert A. Scientific model-building as search in matrix spaces. EnAAAI. 1993. p. 472-478.
- HECKMAN, James J. 1. The Scientific Model of Causality.Sociological methodology, 2005, vol. 35, no 1, p. 1-97.
- KRAJCIK, Joseph; MERRITT, Joi. Engaging Students in Scientific Practices: What Does Constructing and Revising Models Look Like in the Science Classroom?.The Science Teacher, 2012, vol. 79, No 3, p. 38.
- ADÚRIZ-BRAVO, Agustín; IZQUIERDO-AYMERICH, Mercè. A model of a scientific model for the teaching of natural sciences. Electronic journal of research in science education, 2009, no ESP, p. 40-49.
- GALAGOVSKY, Lydia R.; ADVENT-BRAVO, Agustín. Models and analogies in the teaching of the natural sciences. The concept of analogical didactic model. Teaching of Sciences, 2001, vol. 19, no. 2, p. 231-242.