• John P. van Gigch


This chapter is dedicated to modeling, a fundamental aspect of the system design process. Modeling is explained as an iterative decision-making process which takes place in the context of a particular inquiring system. Three different paradigms are compared to show the role of representation of reality in modeling. Then the modeling relation is explained as an introduction of the formulation of models involved in the process of knowledge acquisition. Finally, soft systems methodology (SSM) is introduced as an approach by which complex organizational structures are modified, and the question of modeling about modeling, which we call metamodeling, is raised.


Problem Definition Present Text Root Definition Knowledge Acquisition Process Epistemological Concern 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    P. B. Checkland, Systems Thinking, Systems Practice (Wiley, New York, 1981).Google Scholar
  2. 2.
    G. Kampis, Syst. Res. 5, 44 (1988).CrossRefGoogle Scholar
  3. 3.
    R. Rosen, J. Theor. Biol. 36, 635 (1972).CrossRefGoogle Scholar
  4. 4.
    R. Rosen, Fundamentals of Measurement and Representation of Natural Systems (Pergamon, Oxford, 1985).Google Scholar
  5. 5.
    R. Rosen, in Complexity, Language and Life: Mathematical Approaches (J. Casti and A. Karlquist, eds.) (Springer, Berlin, 1986), pp. 174–196.CrossRefGoogle Scholar
  6. 6.
    J. Casti, Dynamical Systems and their Applications: Linear Theory (Academic Press, New York, 1977).Google Scholar
  7. 7.
    J. Casti, in Recent Developments in Spatial Data Analysis (G. Bahrenberg, M. M. Fischer and P. Nijkamp, eds.) (Gower, Aldershot, 1984), pp. 73–92.Google Scholar
  8. 8.
    R. Rosen, Fundamentals of Measurement and Representation of Natural Systems, Ref. 4.Google Scholar
  9. 9.
    A. Hatchuel, P. Agrell, and J. P. van Gigch, Syst. Res. 4, 5 (1987).Google Scholar
  10. 10.
    T. S. Kuhn, The Structure of Scientific Revolutions (Chicago Univ. Press, Chicago, 1970).Google Scholar
  11. 11.
    W. Kinston, Syst. Res. 2, 95 (1985).CrossRefGoogle Scholar
  12. 12.
    W. Kinston, Syst. Res. 5, 9 (1988).CrossRefGoogle Scholar
  13. 13.
    B. R. Gaines, Syst. Res. 1, 91 (1984).CrossRefGoogle Scholar
  14. 14.
    J. P. van Gigch, Decision Making About Decision Making: Metamodels and Metasystems (Abacus, Gordon and Breach, London, 1987), p. 67; see also Chapter 1 of this text.Google Scholar
  15. 15.
    R. Shurig, Syst. Res. 3, 9 (1986).CrossRefGoogle Scholar
  16. 16.
    M. Foucault, Les Mots et les Choses: Une Archéologie Des Sciences Humaines (Gallimard, Paris, 1967) (In French).Google Scholar
  17. 17.
    M. Foucault, L’Archéologie du Savoir (Gallimard, Paris, 1969) (in French).Google Scholar
  18. 18.
    M. Foucault, Les Mots et les Choses, Ref. 16, p. 32.Google Scholar
  19. 19.
    M. Foucault, Les Mots et les Choses, Ref. 16, p. 32.Google Scholar
  20. 20.
    J. P. van Gigch, Syst. Res. 5, 267, 357 (1988).CrossRefGoogle Scholar
  21. 21.
    P. B. Checkland and L. Davies, J. Ap. Sys. Anal. 13, 109 (1986) (emphasis in the original).Google Scholar
  22. 22.
    P. B. Checkland, New Directions in Management Science (M. C. Jackson and P. Keys, eds.) (Gower, Aldershot, 1987), pp. 87–96.Google Scholar
  23. 23.
    T. Winograd and F. Flores, Understanding Computers and Cognition. A New Foundation for Design (Addison-Wesley, Reading, MA, 1986).Google Scholar
  24. 24.
    D. S. Smyth and P. B. Checkland, J. Ap. Sys. Anal. 5, 75 (1976).Google Scholar
  25. 25.
    G. Nadler, Design Studies 1 (1980).Google Scholar
  26. 26.
    G. Nadler, J. Ap. Sys. Anal. 6, 89 (1979).Google Scholar
  27. 27.
    A. Hatchuel, P. Agrell and J. P. van Gigch, Syst. Res. 4, 5 (1987).Google Scholar
  28. 28.
    P. B. Checkland, in New Directions in Management Science, Ref. 22, p. 94.Google Scholar
  29. 29.
    P. B. Checkland and L. Davies, J. Ap. Sys. Anal. 13, 109 (1986).Google Scholar
  30. 30.
    L. J. Davies, Trans. Inst. Meas. Contr., Special Issue on Management of Complexity, 10 (1988).Google Scholar
  31. 31.
    R. L. Flood, J. Ap. Sys. Anal. 15, 87 (1988).Google Scholar
  32. 32.
    R. L. Flood and E. R. Carson, Dealing with Complexity: An Introduction to the Theory and Application of Systems Science (Plenum, New York, 1988).Google Scholar
  33. 33.
    L. J. Davies and A. T. Wood-Harper, paper presented to the Conference on Information Systems in Developing Countries, IFIP Working Group, Delhi, India, Dec. 1988.Google Scholar
  34. 34.
    B. P. Zeigler, Theory of Modelling and Simulation (Wiley, New York, 1976).Google Scholar
  35. 35.
    H. Wedde, Adequate Modeling of Systems (Springer-Verlag, Berlin and Heidelberg, 1982).Google Scholar
  36. 36.
    T. I. Oren, Sys. Anal. Model Simul. 4, 293 (1984).Google Scholar
  37. 37.
    B. Russell, Principia Mathematica, 2d ed. (A. N. Whitehead and B. Russell) (Cambridge Univ. Press, Cambridge, 1925), Vol. I.Google Scholar
  38. 38.
    C. Hampden-Turner, Maps of the Mind (Macmillan, New York, 1981).Google Scholar
  39. 39.
    I. I. Mitroff, Stakeholders of the Organizational Mind (Jossey-Bass, San Francisco, 1983).Google Scholar
  40. 40.
    I. I. Mitroff, R. O. Mason, and V. Barabba, The 1980 Census: Policy-Making Amid Turbulence (Lexington, Lexington, MA, 1983).Google Scholar
  41. 41.
    S. Toulmin, R. Rieke, and A. Janik, An Introduction to Reasoning, 2d ed. (Macmillan, New York, 1984).Google Scholar

Copyright information

© Springer Science+Business Media New York 1991

Authors and Affiliations

  • John P. van Gigch
    • 1
  1. 1.California State UniversitySacramentoUSA

Personalised recommendations