The Validity and Credibility of Models for Badly Defined Systems

  • Peter Young


If the dictionary definition were the sole criterion, a model would be considered valid if it was found to be well grounded, sound, cogent, logical, and incontestable. Similarly, a model would be deemed credible if it was deserving of or entitled to belief, or if it was plausible, tenable, or reasonable. All of these characteristics are, of course, desirable in a mathematical model of a physical system; but when used as the basis for the definition of model adequacy, they are clearly too subjective to provide useful and rigorous criteria for model evaluation.


Continuous Stir Tank Reactor Monte Carlo Analysis Recursive Estimation Applied System Analysis Random Simulation 
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.


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  1. Ackerman, B.A., Ackerman, S.R., Sawyer, J.W., and Henderson, D.W. (1974). The Uncertain Search for Environmental Quality. The Free Press, New York.Google Scholar
  2. Barrett, J.F., Coales, J.F., Ledwich, M.A., Naughton, J.J., and Young, P.C. (1973). Macro-economic modeling: a critical appraisal. Proceedings of the IFAC/IFORS Conference on Dynamic Modelling and Control of National Economies. IEE, London.Google Scholar
  3. Beck, M.B. and Young, P.C. (1975). A dynamic model for DO–BOD relationships in a non-tidal stream. Water Research, 9: 769–776.CrossRefGoogle Scholar
  4. Beer, T. and Young, P.C. (1980). On the Characterization of Longitudinal Dispersion in Natural Streams. Report No. AS/R42. Centre for Resource and Environmental Studies, Australian National University, Canberra.Google Scholar
  5. Beer, T., Young, P.C., and Humphries, R.B. (1980). Murrumbidgee Water Quality Study: Report on CRES Contribution to the Study. Report No. AS/R41. Centre for Resource and Environmental Studies, Australian National University, Canberra.Google Scholar
  6. Berlinski, D. (1976). On Systems Analysis. MIT Press, Cambridge, Massachusetts.Google Scholar
  7. Bertrand, J. (1855). Méthode des Moindres Carrés, by K.F. Gauss. Translated into French by J. Bertrand. Mallet-Bachelur, Imprimeur-Libraire de L’Ecole Polytechnique, Paris.Google Scholar
  8. Brewer, G.C. (1973). Politicians, Bureaucrats and the Consultant. Basic Books, New York.Google Scholar
  9. Buffham, B.A. and Gibilaro, L.G. (1970). A unified time delay model for dispersion in flowing media. Chemical Engineering Journal, 1: 31–35.CrossRefGoogle Scholar
  10. Ellis, J., Kanamori, S., and Laird, P.G. (1977). Water pollution studies on Lake Illawarra. Australian Journal of Marine and Freshwater Research, 28: 467–477.CrossRefGoogle Scholar
  11. Fischer, H.B. (1966). A note on the one dimensional dispersion model. Air and Water Pollution, International Journal, 10: 443–452.Google Scholar
  12. Fischer, H.B. (1968). Dispersion prediction in natural streams. Journal of Sanitation Engineering, ASCE, 94: 927–944.Google Scholar
  13. Holling, C.S. (Editor) (1978). Adaptive Environmental Assessment and Management. Wiley, Chichester.Google Scholar
  14. Hoos, I.R. (1972). Systems Analysis in Public Policy. University of California Press, Berkeley and Los Angeles.Google Scholar
  15. Humphries, R.B., Young, P.C., and Beer, T. (1980). Systems Analysis of an Estuary; Report of the CRES Contribution to the Peel–Harvey Estuary Study. Bulletin No. 100, Western Australian Department of Conservation and Environment, Perth, Western Australia.Google Scholar
  16. Jakeman, A.J. and Young, P.C. (1979a). Refined instrumental variable methods of recursive time series analysis. Part II: multivariable systems. International Journal of Control, 29: 621–644.CrossRefGoogle Scholar
  17. Jakeman, A.J. and Young, P.C. (1979b). Time-series methods in biological and medical data analysis. In R. Isermann (Editor), Identification and System Parameter Estimation. Pergamon Press, Oxford.Google Scholar
  18. Jakeman, A.J. and Young, P.C. (1980). Towards optimal modeling of translocation data from tracer studies. Proceedings of the Biennial Conference of the Simulation Society of Australia, 4th, pp. 248–253.Google Scholar
  19. Jakeman, A.J., Steele, L.P., and Young, P.C. (1980). Instrumental variable algorithms for multiple input systems described by multiple transfer functions. IEEE Transactions, Systems, Man, and Cybernetics, SMC-10: 593–602.CrossRefGoogle Scholar
  20. Johnston, J. (1963). Econometric Methods. McGraw-Hill, New York.Google Scholar
  21. Kaldor, J.M. (1978). The Estimation of Parametric Change in Time-Series Models. M.A. Thesis, Australian National University, Canberra.Google Scholar
  22. Kaiman, R.E. (1960). A new approach to linear filtering and prediction problems. ASME Transactions, Journal of Basic Engineering, 83D: 95–108.Google Scholar
  23. Kendall, M.G. and Stuart, A. (1961). The Advanced Theory of Statistics, Volume 2. Griffin, London.Google Scholar
  24. Kittler, J. and Young, P.C. (1973). A new approach to feature selection based on the Karhunen–Loeve expansion. Pattern Recognition, 5: 335–352.CrossRefGoogle Scholar
  25. Miller, D.R., Butler, G., and Bramall, C. (1976). Validation of ecological system models. Journal of Environmental Management, 4:383–401.Google Scholar
  26. Philip, J.R. (1975). Some remarks on science and catchment prediction. In T.G. Chapman and F.X. Dunin (Editors), Prediction in Catchment Hydrology. Australian Academy of Science, Canberra.Google Scholar
  27. Plackett, R.L. (1950). On some theorems in least squares. Biometrika, 37: 149–157.Google Scholar
  28. Popper, K.R. (1959). The Logic of Scientific Discovery. Hutchinson, London.Google Scholar
  29. Rademaker, O. (1973). On understanding complicated models: simple methods. Presented at the American/Soviet Conference on Methodological Aspects of Social Systems Simulation, Sukhumi (USSR), October 24–26.Google Scholar
  30. Rigler, F.H (1976). Review of “Systems Analysis and Simulation in Ecology”, Volume 3. B.C. Patten (Editor), Limnology and Oceanography, 21 (3): 481–483.Google Scholar
  31. Salmon, M. and Young, P.C. (1978). Control methods and quantitative economic policy. In S. Holly, B. Rustem, and M. Zarrop (Editors), Optimal Control for Econometric Models: An Approach to Economic Policy Formation. MacMillan, London.Google Scholar
  32. Smith, R. (1980). Buoyancy effects upon longitudinal dispersion in wide well-mixed estuaries. Philosophical Transactions of the Royal Society, Series A, 296:467–496.CrossRefGoogle Scholar
  33. Spear, R.C. (1970). Application of Kolmogorov–Renyi statistics to problems of parameter uncertainty in systems design. International Journal of Control, 11: 771–778.CrossRefGoogle Scholar
  34. Spear, R.C. and Hornberger, G.M. (1978). Eutrophication in Peel Inlet: an analysis of behaviour and sensitivity of a poorly defined system. Report No. AS/R18, Centre for Resource and Environmental Studies, Australian National University, Canberra.Google Scholar
  35. Sprott, D.A. (1977). Gauss’s Contributions to Statistics. Presented at the Gauss Bicentennial, Toronto. (D.A. Sprott is with the University of Waterloo, Ontario, Canada.)Google Scholar
  36. Steele, L.P. (1981). Recursive Estimation in the Identification of Air Pollution Models. Ph.D. Thesis, Australian National University, Canberra.Google Scholar
  37. Taylor, G.I. (1954). The dispersion of matter in turbulent flow through a pipe. Proceedings of the Royal Society, Series A, 223: 446–468.CrossRefGoogle Scholar
  38. Thissen, W. (1978). Investigations into the World 3 model: overall behaviour and policy conclusions. IEEE Transactions, Systems, Man, and Cybernetics, SMC-8: 172–182.CrossRefGoogle Scholar
  39. Whitehead, P.G. and Young, P.C. (1975). A dynamic-stochastic model for water quality in part of the Bedford Ouse river system. In G.C. Vansteenkiste (Editor), Computer Simulation of Water Resources Systems. North-Holland, Amsterdam, pp. 417–438.Google Scholar
  40. Whitehead, P.G. and Young, P.C. (1979). Water quality in river systems — Monte Carlo analysis. Water Resources Research, 15: 451–459.CrossRefGoogle Scholar
  41. Whitehead, P.G., Young, P.C., and Hornberger, G.H. (1979). A systems model of streamflow and water quality in the Bedford-Ouse river, I: streamflow modelling. Water Research, 13:1155–1169.CrossRefGoogle Scholar
  42. Young, P.C. (1974). Recursive approaches to time series analysis. Bulletin of the Institute of Mathematics and its Application, 10: 209–224.Google Scholar
  43. Young, P.C. (1976a). Some observations on instrumental variable methods of time series analysis. International Journal of Control, 23: 593–612.CrossRefGoogle Scholar
  44. Young, P.C. (1976b). Optimization in the presence of noise: a guided tour. In L.C.R. Dixon (Editor), Optimization in Action. Academic Press, London, pp. 517–573.Google Scholar
  45. Young, P.C. (1977). A general theory of modeling for badly defined systems. Report No. AS/R9, Centre for Resource and Environmental Studies, Australian National University, Canberra. Also published in G.C. Vansteenkiste (Editor), Modeling, Identification, and Control in Environmental Systems. North-Holland, Amsterdam/American Elsevier, New York, pp. 103–135.Google Scholar
  46. Young, P.C. (1980). Mining and the Natural Environment — Systems Analysis and Mathematical Modeling. In S.F. Harris (Editor), Social and Environmental Choice: The Impact of Uranium Mining in the Northern Territory. Centre for Resource and Environmental Studies, Australian National University, Canberra, pp. 64–78.Google Scholar
  47. Young, P.C. (1981). Parameter estimation for continuous-time models — a survey. Automatica, 17: 23–29.CrossRefGoogle Scholar
  48. Young, P.C. (1982). An Introduction to Recursive Estimation. Lecture Notes Series, Springer, Berlin, in press.Google Scholar
  49. Young, P.C. and Beck, M.B. (1974). The modeling and control of water quality in a river system. Automatica, 10:455–468.CrossRefGoogle Scholar
  50. Young, P.C. and Jakeman, A.J. (1979). Refined instrumental variable methods of recursive time series analysis, Part I: single input–single output systems. International Journal of Control, 29: 1–30.CrossRefGoogle Scholar
  51. Young, P.C. and Jakeman, A.J. (1980). Refined instrumental variable methods of recursive time series analysis, Part III: extensions. International Journal of Control, 31:741–764.CrossRefGoogle Scholar
  52. Young, P.C. and Kaldor, J.M. (1978). Recursive estimation: a methodological tool for investigating climatic change. Report No. AS/R14, Centre for Resource and Environmental Studies, Australian National University, Canberra (to be revised).Google Scholar
  53. Young, P.C., Naughton, J.J., Neethling, C.G., and Shellswell, S.H. (1973). Macro-economic modeling: a case study. In P. Eykhoff (Editor), Identification and System Parameter Estimation. North-Holland, Amsterdam/American Elsevier, New York.Google Scholar
  54. Young, P.C., Jakeman, A.J., and McMurtrie, R.E. (1980). An instrumental variable method for model structure identification. Automatica, 16: 281–294.CrossRefGoogle Scholar

Copyright information

© International Institute for Applied Systems Analysis, Laxenburg/Austria 1983

Authors and Affiliations

  • Peter Young
    • 1
  1. 1.Centre for Resource and Environmental StudiesAustralian National UniversityCanberraAustralia

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