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Information about Scientific Modeling

Scientific modelling is the process of generating abstract or conceptual models. Science offers a growing collection of methods, and theory about all kinds of specialized scientific modelling. Some general theory about scientific modelling is offered by the philosophy of science, systems theory, and new fields like knowledge visualization.

Overview

Modelling is an essential and inseparable part of all scientific activity. The professional modeller brings special skills and techniques to bear in order to produce results that are insightful, reliable, and useful. The techniques include sophisticated statistical methods, computer simulation, system identification, and sensitivity analysis are valuable tools. They however are not as important as the ability to understand the underlying dynamics of a complex system. These insights are needed to assess whether the assumptions of a model are correct and complete. The modeller must be able to recognise whether a model reflects reality, and to identify and deal with divergences between theory and data.[1]

One of the main aims of scientific modelling is to apply quantitative reasoning to observations about the world, in the hope of seeing aspects that may have escaped the notice of others. Now there are many specific techniques that modellers use, which enable us to discover aspect of reality that may not be obvious to everyone. One of the essentials is the understanding of the role that assumptions play in the development of the model. The usual approach to modeldevelopment is to characterise the system, make some assumptions about how it works and translate these into equations and a simulation program. After simulation one of the final steps is the validation. The question if we can trust the data the model presented..[1]

Scientific modelling topics

Scientific model

A model is a physical, mathematical, or logical representation of a system entity, phenomenon, or process. A simulation is the implementation of a model over time. A simulation brings a model to life and shows how a particular object or phenomenon will behave. It is useful for testing, analysis or training where real-world systems or concepts can be represented by a model. [2]

For the scientist a model is also a way in which the human though processes can be amplified. This method often takes the form of models that can be programmed into computers. At no point, however, the scientist intend to loose control of the situation because of the computer does some of his thinking for him. The scientist controls the basic assumptions and the computer only derives some of the more complicated implications.[3]

The process of 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 in which the difference is more than just a simple renaming. This may be due to differing requirements of the model's end users or to conceptual or esthetic differences by the modellers and decisions made during the modelling process. Esthetic considerations that may influence the structure of a model might be the modeller's preference for a reduced ontology, preferences regarding probabilistic models vis-a-vis deterministic ones, discrete vs continuous time etc. For this reason users of a model need to understand the model's original purpose and the assumptions of its validity. Models are basically known to generate creativity from chaos.

The process of evaluating a model

A model is evaluated using a utility function to yield estimates of several values:
  • Ability to explain past observations
  • Ability to predict future observations
  • Ability to control events
  • 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

Scientific modelling applications

Modelling and simulation have a spectrum of applications both in practice, from concept analysis, through disposal analysis, programs may use hundreds of different simulations, simulators and model analysis tools.



The figure shows how modelling and simulation is used in Defenses as an integrated program, that affect all functions of the acquisition process.[2]

Forms of scientific modelling

Business process modelling

Enlarge picture
Abstraction for Business process modelling [4]
In business process modeling the enterprise process model is often referred to as the business process model. Process models are core concepts in the discipline of process engineering. Process models are:
  • Processes of the same nature that are classified together into a model.
  • A description of a process at the type level.
  • Since the process model is at the type level, a process is an instantiation of it.
The same process model is used repeatedly for the development of many applications and thus, has many instantiations.

One possible use of a process model is to prescribe how things must/should/could be done in contrast to the process itself which is really what happens. A process model is roughly an anticipation of what the process will look like. What the process shall be will be determined during actual system development.[5]

Other forms

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 modeling in the philosophy-of-science literature. A selection:
  • Churchman, C. West (1968), The Systems Approach, New York: Dell Publishing.
  • Silvert, William (2001), "Modelling as a Discipline", in: Int. J. General Systems Vol. 30(3), pp. 261-282.
  • Frigg, Roman and Hartmann, Stephan (2006), Models in Science, in: Stanford Encyclopedia of Philosophy, 2006.
  • Hegselmann, Rainer, Ulrich Müller and Klaus Troitzsch (eds.) (1996), Modelling and Simulation in the Social Sciences from the Philosophy of Science Point of View. Theory and Decision Library. Dordrecht: Kluwer.
  • Humphreys, Paul (2004), Extending Ourselves: Computational Science, Empiricism, and Scientific Method. Oxford: Oxford University Press.
  • Rohrlich, Fritz (1991) “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.
  • Schnell, Rainer (1990), “Computersimulation und Theoriebildung in den Sozialwissenschaften”, Kölner Zeitschrift für Soziologie und Sozialpsychologie 1, 109-128.
  • Sismondo, Sergio and Snait Gissis (eds.) (1999), Modeling and Simulation. Special Issue of Science in Context 12.
  • Winsberg, Eric (2001), “Simulations, Models and Theories: Complex Physical Systems and their Representations”, Philosophy of Science 68 (Proceedings): 442-454.
  • Winsberg, Eric (2003), “Simulated Experiments: Methodology for a Virtual World”, Philosophy of Science 70: 105–125.

References

1. ^ William Silvert (2001), Modelling as a Discipline, in: Int. J. General Systems Vol. 30(3), pp. 261.
2. ^ Systems Engineering Fundamentals. Defense Acquisition University Press, 2001.
3. ^ C. West Churchman, The Systems Approach, New York: Dell publishing, 1968, p.61
4. ^ C. Rolland, Modeling the Requirements Engineering Process, 3rd European-Japanese Seminar on Information Modelling and Knowledge Bases, Budapest, Hungary, June 1993.
5. ^ C. Rolland and C. Thanos Pernici, A Comprehensive View of Process Engineering. Proceedings of the 10th International Conference CAiSE'98, B. Lecture Notes in Computer Science 1413, Pisa, Italy, Springer, June 1998.

See also

External links

For other uses, see Model.


An abstract model (or conceptual model) is a theoretical construct that represents something, with a set of variables and a set of logical and quantitative relationships between them.
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Scientific method is a body of techniques for investigating phenomena, acquiring new knowledge, or correcting and integrating previous knowledge. It is based on gathering observable, empirical and measurable evidence subject to specific principles of reasoning,[1]
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The word theory has a number of distinct meanings in different fields of knowledge, depending on their methodologies and the context of discussion.

In common usage, people often use the word theory to signify a conjecture, an opinion, or a speculation.
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Philosophy of science is the study of assumptions, foundations, and implications of science. The philosophy of science may be divided into two areas: Epistemology of science and metaphysics of science.
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Systems theory is an interdisciplinary field of science. It studies the nature of complex systems in nature, society, and science. More specificially, it is a framework by which one can analyze and/or describe any group of objects that work in concert to produce some result.
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Knowledge Visualization aims to facilitate the creation and communication of knowledge through the use of computer and non-computer-based, complementary, graphic representation techniques.
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Science (from the Latin scientia, 'knowledge'), in the broadest sense, refers to any systematic knowledge or practice.[1] Examples of the broader use included political science and computer science, which are not incorrectly named, but rather named according to
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computer simulation, a computer model or a computational model is a computer program that attempts to simulate an abstract model of a particular system. Computer simulations have become a useful part of mathematical modelling of many natural systems in physics
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System identification is a general term to describe mathematical tools and algorithms that build dynamical models from measured data. A dynamical mathematical model in this context is a mathematical description of the dynamic behavior of a system or process.
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dynamical system concept is a mathematical formalization for any fixed "rule" which describes the time dependence of a point's position in its ambient space. Examples include the mathematical models that describe the swinging of a clock pendulum, the flow of water in a pipe, and
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Complex system is a system comprised of interconnected simple parts, that together exhibit a high degree of complexity from which emerges a higher order behavior. Examples of complex systems include ant-hills, ants themselves, human economies, climate, nervous systems, cells and
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A quantitative attribute is one that exists in a range of magnitudes, and can therefore be measured. Measurements of any particular quantitative property are expressed as a specific quantity, referred to as a unit, multiplied by a number.
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Reasoning is the mental (cognitive) process of looking for reasons for beliefs, conclusions, actions or feelings.[1] Humans have the ability to engage in reasoning about their own reasoning using introspection.
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Observation is an activity of a sapient or sentient living being (e.g. humans), which senses and assimilates the knowledge of a phenomenon in its framework of previous knowledge and ideas.
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An assumption is a proposition that is taken for granted, in other words, that is treated for the sake of a given discussion as if it were known to be true.
  • In logic, more specifically in the context of natural deduction systems, an assumption

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System (from Latin systēma, in turn from Greek σύστημα systēma) is a set of entities, real or abstract, where each entity interacts with, or is related to, at least one other
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simulation is an imitation of some real thing, state of affairs, or process. The act of simulating something generally entails representing certain key characteristics or behaviours of a selected physical or abstract system.
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The word validation has several uses:
  • In general, validation is the process of checking if something satisfies a certain criterion. Examples would include checking if a statement is true (validity), if an appliance works as intended, if a computer system is secure, or if

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Representation can refer to:
  • Representation (politics), one's ability to influence the political process
  • Representative democracy
  • Representation (arts), the depiction and ethical concerns of construction in visual arts and literature.

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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
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Ontology is a study of conceptions of reality and the nature of being. In philosophy, ontology (from the Greek ὤν, genitive ὄντος: of being (part.
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In economics, utility is a measure of the relative satisfaction or desiredness from consumption of goods. Given this measure, one may speak meaningfully of increasing or decreasing utility, and thereby explain economic behavior in terms of attempts to increase one's utility.
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The term process model is used in different contexts. For example, in Business process modeling the enterprise process model is often referred to as the business process model. Process models are core concepts in the discipline of Process Engineering.
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Process (lat. processus - movement) is a naturally occurring or designed sequence of changes of properties or attributes of an object or system[1][2][3][4][5][6][7].
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Climate models use quantitative methods to simulate the interactions of the atmosphere, oceans, land surface, and ice. They are used for a variety of purposes from study of the dynamics of the weather and climate system to projections of future climate.
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In computer science, data modeling is the process of creating a data model by applying a data model theory to create a data model instance. A data model theory is a formal data model description. See database model for a list of current data model theories.
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Ecosystem models, or ecological models, are mathematical representations of ecosystems. Typically they simplify complex foodwebs down to their major components or trophic levels, and quantify these as either numbers of organisms, biomass or the inventory/concentration of
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Futures studies reflects on how today’s changes (and continuities) become tomorrow’s reality. It includes attempts to analyze the sources, patterns, and causes of change and stability in order to develop foresight and to map alternative futures.
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