Scientific_modeling

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

Contents 1 Overview 2 Scientific modelling topics 2.1 Scientific model 2.2 The process of generating a model 2.3 The process of evaluating a model 3 Applications 3.1 Modeling and Simulation 4 Forms of scientific modelling 4.1 Business process modelling 4.2 Other forms 5 Further Reading 6 References 7 See also 8 External links

Overview

Modeling 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. William Silvert (2001), Modelling as a Discipline, in: Int. J. General Systems Vol. 30(3), pp. 261.

One of the main aims of scientific modeling 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 model development 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..

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. Systems Engineering Fundamentals. Defense Acquisition University Press, 2001.

For the scientist, a model is also a way in which the human thought processes can be amplified. C. West Churchman, The Systems Approach, New York: Dell publishing, 1968, p.61 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.

The process of generating a model

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Modeling 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

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A model is evaluated first and foremost by its consistency to empirical data; any model inconsistent with reproducible observations must be modified or rejected. 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
• 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

People may attempt to quantify the evaluation of a model using a utility function.

Applications

Modeling and Simulation

One application of scientific modelling is the field of "Modeling and Simulation", generally referred to as "M&S".Due to the fact that "Modeling and Simulation" is frequently taught in male dominated undergraduate environments, this field of application is deliberately named "Modeling and Simulation", rather than "Simulation and Modeling", to avoid distractions which may arise due to any possible association with the negative connotations of S&M.^{[citation needed]} 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.

Example of the integrated use of Modelling and Simulation in Defence life cycle management. The modelling and simulation in this image is represented in the center of the image with the three containers.

The figure shows how Modelling and Simulation is used as a central part of an integrated program in a Defence capability development process.

Forms of scientific modelling

Business process modelling

Abstraction for Business process modelling C. Rolland, Modeling the Requirements Engineering Process, 3rd European-Japanese Seminar on Information Modelling and Knowledge Bases, Budapest, Hungary, June 1993.

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. 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.

Other forms

Analogical modeling Assembly modeling Catastrophe modeling Choice Modeling Climate model Continuous modeling Data modeling Document modeling Discrete modeling Ecosystem model Economic models (Category) Empirical modeling

Enterprise modeling Futures studies Geologic modeling Goal Modeling Graphical modeling (Category) Homology modeling Hydrography Hydrologic modeling Hydrogeology Informative Modeling Mathematical biology Mathematical modeling NLP Modeling

Metabolic network modeling Modeling in Epidemiology Molecular modeling Predictive modeling Simulation Software modeling Solid modeling Statistics Stochastic modeling System dynamics

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 (1990) “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

See also

• Cartography
• List of computer graphics and descriptive geometry topics
• List of graphical methods
• Model (abstract)
• Modelling language
• Scientific visualization
• Simulation
• Systems Engineering
• Informative Modeling

External links

• Models in Science. Entry in the Stanford Encyclopedia of Philosophy
• Research in simulation and modeling of various physical systems

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