Scientific modelling is a scientific activity, the aim of which is to make a
particular part or feature of the world easier to understand, define, quantify,
visualize, or simulate by referencing it to existing and usually commonly
accepted knowledge. It requires selecting and identifying relevant
aspects of a situation in the real world and then using different types of models
for different aims, such as conceptual models to better understand, operational
models to operationalize, mathematical models to quantify, and graphical models
to visualize the subject. Modelling is an essential and inseparable part of
scientific activity, and many scientific disciplines have their own ideas about
specific types of modelling. There is also an increasing attention to
scientific modelling in fields such as science education, philosophy of
science, systems theory, and knowledge visualization. There is growing
collection of methods, techniques and meta-theory about all kinds of
specialized scientific modelling. Overview
A scientific model seeks to represent empirical objects, phenomena, and
physical processes in a logical and objective way. All models are in
simulacra, that is, simplified reflections of reality that, despite
being approximations, can be extremely useful. Building and disputing models is
fundamental to the scientific enterprise. Complete and true
representation may be impossible, but scientific debate often concerns which
is the better model for a given task, e.g., which is the more accurate climate
model for seasonal forecasting. Attempts to formalize the principles of
the empirical sciences use an interpretation to model reality, in the
same way logicians axiomatize the principles of logic. The aim of these
attempts is to construct a formal system that will not produce theoretical
consequences that are contrary to what is found in reality. Predictions or
other statements drawn from such a formal system mirror or map the real
world only insofar as these scientific models are true.
For the scientist, a model is also a way in which the human thought processes can
be amplified. For instance, models that are rendered in software allow
scientists to leverage computational power to simulate, visualize, manipulate
and gain intuition about the entity, phenomenon, or process being
represented. Such computer models are in silico. Other types of scientific model
are in vivo and in vitro. Basics of scientific modelling
= Modelling as a substitute for direct measurement and experimentation =
Models are typically used when it is either impossible or impractical to
create experimental conditions in which scientists can directly measure
outcomes. Direct measurement of outcomes under controlled conditions will always
be more reliable than modelled estimates of outcomes.
= Simulation = A simulation is the implementation of a
model. A steady state simulation provides information about the system at
a specific instant in time. A dynamic simulation provides information over
time. A simulation brings a model to life and shows how a particular object
or phenomenon will behave. Such a simulation can be useful for testing,
analysis, or training in those cases where real-world systems or concepts can
be represented by models. = Structure =
Structure is a fundamental and sometimes intangible notion covering the
recognition, observation, nature, and stability of patterns and relationships
of entities. From a child's verbal description of a snowflake, to the
detailed scientific analysis of the properties of magnetic fields, the
concept of structure is an essential foundation of nearly every mode of
inquiry and discovery in science, philosophy, and art.
= Systems = A system is a set of interacting or
interdependent entities, real or abstract, forming an integrated whole.
In general, a system is a construct or collection of different elements that
together can produce results not obtainable by the elements alone. The
concept of an 'integrated whole' can also be stated in terms of a system
embodying a set of relationships which are differentiated from relationships of
the set to other elements, and from relationships between an element of the
set and elements not a part of the relational regime. There are two types
of system models: 1) discrete in which the variables change instantaneously at
separate points in time and, 2) continuous where the state variables
change continuously with respect to time.
= Generating a model = Modelling refers to the process of
generating a model as a conceptual representation of some phenomenon.
Typically a model will refer only to some aspects of the phenomenon in
question, and two models of the same phenomenon may be essentially different,
that is to say that the differences between them comprise more than just a
simple renaming of components. Such differences may be due to differing
requirements of the model's end users, or to conceptual or aesthetic
differences among the modellers and to contingent decisions made during the
modelling process. Considerations that may influence the structure of a model
might be the modeller's preference for a reduced ontology, preferences regarding
statistical models versus deterministic models, discrete versus continuous time,
etc. In any case, users of a model need to understand the assumptions made that
are pertinent to its validity for a given use.
Building a model requires abstraction. Assumptions are used in modelling in
order to specify the domain of application of the model. For example,
the special theory of relativity assumes an inertial frame of reference. This
assumption was contextualized and further explained by the general theory
of relativity. A model makes accurate predictions when its assumptions are
valid, and might well not make accurate predictions when its assumptions do not
hold. Such assumptions are often the point with which older theories are
succeeded by new ones. The term "assumption" is actually
broader than its standard use, etymologically speaking. The Oxford
English Dictionary and online Wiktionary indicate its Latin source as assumere,
which is a conjunction of ad- and sumere. The root survives, with shifted
meanings, in the Italian sumere and Spanish sumir. In the OED, "assume" has
the senses of “investing oneself with, ” “to undertake”, “to take to oneself in
appearance only, to pretend to possess,” and “to suppose a thing to be.” Thus,
"assumption" connotes other associations than the contemporary standard sense of
“that which is assumed or taken for granted; a supposition, postulate,” and
deserves a broader analysis in the philosophy of science.
= Evaluating a model = A model is evaluated first and foremost
by its consistency to empirical data; any model inconsistent with reproducible
observations must be modified or rejected. One way to modify the model is
by restricting the domain over which it is credited with having high validity. A
case in point is Newtonian physics, which is highly useful except for the
very small, the very fast, and the very massive phenomena of the universe.
However, a fit to empirical data alone is not sufficient for a model to be
accepted as valid. Other factors important in evaluating a model include:
Ability to explain past observations Ability to predict future observations
Cost of use, especially in combination with other models
Refutability, enabling estimation of the degree of confidence in the model
Simplicity, or even aesthetic appeal People may attempt to quantify the
evaluation of a model using a utility function.
= Visualization = Visualization is any technique for
creating images, diagrams, or animations to communicate a message. Visualization
through visual imagery has been an effective way to communicate both
abstract and concrete ideas since the dawn of man. Examples from history
include cave paintings, Egyptian hieroglyphs, Greek geometry, and
Leonardo da Vinci's revolutionary methods of technical drawing for
engineering and scientific purposes. Types of scientific modelling
Applications = Modelling and Simulation =
One application of scientific modelling is the field of "Modelling and
Simulation", generally referred to as "M&S". M&S has a spectrum of
applications which range from concept development and analysis, through
experimentation, measurement and verification, to disposal analysis.
Projects and programs may use hundreds of different simulations, simulators and
model analysis tools. The figure shows how Modelling and
Simulation is used as a central part of an integrated program in a Defence
capability development process. = Model Based Learning in Education =
Model Based learning in education, particularly in relation to learning
science involves students creating models for scientific concepts in order
to - Gain insight of the scientific idea(s)
Acquire deeper understanding of the subject through visualization of the
model Improve student engagement in the course
Different types of model based learning techniques include -
Physical macrocosms Representational systems
Syntactic models Emergent models
Model making in education, is an iterative exercise with students
refining, developing and evaluating their models over time. This shifts
learning from the rigidity and monotony of traditional curriculum to an exercise
of students' creativity and curiosity. This approach utilizes the constructive
strategy of social collaboration and learning scaffold theory. Model based
learning includes cognitive reasoning skills where existing models can be
improved upon by construction of newer models using the old models as a basis.
"Model based learning entails determining target models and a learning
pathway that provide realistic chances of understanding." Model making can
also incorporate blended learning strategies by using web based tools and
simulators, thereby allowing students to Familiarize themselves with on-line or
digital resources Create different models with various
virtual materials at little or no cost Practice model making activity any time
and any place Refine existing models
"A well-designed simulation simplifies a real world system while heightening
awareness of the complexity of the system. Students can participate in the
simplified system and learn how the real system operates without spending days,
weeks or years it would take to undergo this experience in the real world."
The teacher's role in the overall teaching and learning process is
primarily that of a facilitator and arranger of the learning experience. He
or she would assign the students, a model making activity for a particular
concept and provide relevant information or support for the activity. For virtual
model making activities, the teacher can also provide information on the usage of
the digital tool and render troubleshooting support in case of
glitches while using the same. The teacher can also arrange the group
discussion activity between the students and provide the platform necessary for
students to share their observations and knowledge extracted from the model
making activity. Model based learning evaluation could
include the use of rubrics that assess the ingenuity and creativity of the
student in the model construction and also the overall classroom participation
of the student vis-a-vis the knowledge constructed through the activity.
It is important however to give due consideration to the following for
successful model based learning to occur Use of right tool at right time for a
particular concept Provision within the educational setup
for model making activity e.g. computer room with internet facility or software
installed to access simulator or digital tool
See also Heuristic
Grey box completion and validation Scientific visualization
Simulation Statistical model
Systems engineering Toy model
References Further reading
Nowadays there are some 40 magazines about scientific modelling which offer
all kinds of international forums. Since the 1960s there is a strong growing
amount of books and magazines about specific forms of scientific modelling.
There is also a lot of discussion about scientific modelling in the
philosophy-of-science literature. A selection:
Rainer Hegselmann, Ulrich Müller and Klaus Troitzsch. Modelling and
Simulation in the Social Sciences from the Philosophy of Science Point of View.
Theory and Decision Library. Dordrecht: Kluwer.
Paul Humphreys. Extending Ourselves: Computational Science, Empiricism, and
Scientific Method. Oxford: Oxford University Press.
Johannes Lenhard, Günter Küppers and Terry Shinn "Simulation: Pragmatic
Constructions of Reality", Springer Berlin.
Tom Ritchey. "Outline for a Morphology of Modelling Methods: Contribution to a
General Theory of Modelling". In: Acta Morphologica Generalis, Vol 1. No 1. pp.
1-20. Fritz Rohrlich. "Computer Simulations in
the Physical Sciences". In: Proceedings of the Philosophy of Science
Association, Vol. 2, edited by Arthur Fine et al., 507-518. East Lansing: The
Philosophy of Science Association. Rainer Schnell. "Computersimulation und
Theoriebildung in den Sozialwissenschaften". In: Kölner
Zeitschrift für Soziologie und Sozialpsychologie 1, 109-128.
Sergio Sismondo and Snait Gissis. Modeling and Simulation. Special Issue
of Science in Context 12. Eric Winsberg. "Simulations, Models and
Theories: Complex Physical Systems and their Representations". In: Philosophy
of Science 68: 442-454. Eric Winsberg. "Simulated Experiments:
Methodology for a Virtual World". In: Philosophy of Science 70: 105–125.
Tomáš Helikar, Jim A Rogers. "ChemChains: a platform for simulation
and analysis of biochemical networks aimed to laboratory scientists". BioMed
Central. External links
Models. Entry in the Internet Encyclopedia of Philosophy
Models in Science. Entry in the Stanford Encyclopedia of Philosophy
The World as a Process: Simulations in the Natural and Social Sciences, in: R.
Hegselmann et al., Modelling and Simulation in the Social Sciences from
the Philosophy of Science Point of View, Theory and Decision Library. Dordrecht:
Kluwer 1996, 77-100. Research in simulation and modelling of
various physical systems Modelling Water Quality Information
Center, U.S. Department of Agriculture Ecotoxicology & Models
A Morphology of Modelling Methods. Acta Morphologica Generalis, Vol 1. No 1. pp.
1-20. Template:Computer modelling