Abstract
The methodology involved in the modeling and simulation of physical, life and social science systems is viewed in perspective. A critical factor determining the validity of a model is the extent to which it can be derived from basic laws and insights into the internal structure of the system using deductive methods, rather than relying upon observations and measurements of the system input and outputs. Accordingly, the mathematical models as they arise in various application disciplines are arranged along a spectrum according to the relative amount of deduction and induction involved in their construction. This provides an insight into the ultimate validity of simulations and to what use they can properly be put.
Keywords
- Medium Circuit
- Lump Parameter Model
- Social Science Discipline
- Distribute Parameter Model
- Application Discipline
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Reprinted from Mathematics and Computers in Simulation, v. 19, Walter J. Karplus, The Spectrum of Mathematical Modeling and Systems Simulation, pp. 3–10, Copyright (1977), with permission from Elsevier Science. Research in Modeling at the University of California was supported by the National Science Foundation under Grant GK 42774.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
T.S. Kuhn. The Structure of Scientific Revolutions. University of Chicago Press, Chicago, 1962.
D. Bohm. Causality and Chance in Modern Physics. Routledge and Kegan Paul Ltd., London, 1957.
M. Born. Natural Philosophy of Cause and Chance. Clarendon Press, London, 1949.
P. Frank. Philosophy of Science. Prentice-Hall, Engle wood Cliffs, New Jersey, 1957.
Y. Chu. Digital Simulation of Continuous Systems. McGraw-Hill Inc., New York, 1969.
Proc., IBM Scientific Computing Symposium. “Digital Simulation of Continuous Systems”. International Business Machines Corporation, White Plains, New York, 1967.
W. Jentsch. Digitale Simulation Kontinuierlicher System. R. Oldenbourg Verlag, Munich, 1969 (German).
W. Karplus. Analog Simulation: Solution of Field Problems. McGraw-Hill Inc., New York, 1958.
G. Vansteeenkiste (Editor). Proc., IFIP Working Conference on Computer Simulation of Water Resources Systems. North-Holland Publishing, Amsterdam, 1974.
G. Flemming. Computer Simulation Techniques in Hydrology. Elsevier, New York, 1975.
D.D. Sworder. “Systems and Simulation in the Service of Society”. Proc. of Simulation Councils, 1 (1971).
G. Vansteenkiste (Editor). Proc., IFIP Working Conference on Biosystems Simulation in Water Resources Systems. North-Holland Publishing, Amsterdam, 1975.
G.S. Innis (Editor). “Simulation Application in System Ecology”. Society for Computer Simulation Proc., 5, 1975.
J.W. Forrester. Urban Dynamics. MIT Press, Cambridge, Mass, 1969.
G. Gordon. System Simulation. Prentice Hall, Engle wood Cliffs, New Jersey, 1969.
H. Maisei and G. Gnugoli. Simulation of Discrete Stochastic Systems. Science Research Associates Inc., Chicago, 1972.
P. Rivett. Principles of Model Building. John Wiley and Sons, London, 1972
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer Science+Business Media New York
About this chapter
Cite this chapter
Karplus, W.J. (2003). The Spectrum of Mathematical Modeling and Systems Simulation. In: Bekey, G.A., Kogan, B.Y. (eds) Modeling and Simulation: Theory and Practice. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-0235-7_7
Download citation
DOI: https://doi.org/10.1007/978-1-4615-0235-7_7
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-4979-2
Online ISBN: 978-1-4615-0235-7
eBook Packages: Springer Book Archive