Computer Modelling of Theory: Explanation for the 21st Century

  • Thomas K. Burch
Chapter
Part of the Methodos Series book series (METH, volume 1)

Abstract

The words theory, model, and explanation are used in different ways by different writers. Complete agreement on their meanings among natural scientists, social scientists, philosophers of science, engineers and others seems unlikely, since meaning depends partly on context and on discipline-specific conventions. Accepted meanings of these words often depend on subject matter, and on the purposes of research. In practice, a theory, model, or explanation—or a good theory, model, or explanation—for a physicist or chemist may differ in some respects from a theory, model, or explanation for a biologist, a meteorologist, or a demographer. These differences may appear all the greater if one looks at the use of models and theories in practical decision making, as in engineering or policy formation.

Keywords

Migration Europe Income Coherence Posit 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abbott, A. (1988). Transcending general linear reality. Sociological Theory, 6, 169–186.CrossRefGoogle Scholar
  2. Ashby, W. R. (1963). Introduction to cybernetics. London: Chapman & Hall.Google Scholar
  3. Berlinski, D. (1976). On systems analysis: an essay concerning the limitations of some mathematical methods in the social, political, and biological sciences. Cambridge, MA: MIT Press.Google Scholar
  4. Burch, T. K. (1996). Icons, strawmen and precision: reflections on demographic theories of fertility decline. The Sociological Quarterly, 37, 59–81.CrossRefGoogle Scholar
  5. Burch, T. K. (1997a). Curriculum needs: perspectives from North America. In D.J. Bogue (Ed.), Defining a new demography: curriculum needs for the 1990’s and beyond (pp. 47–56). Chicago: Social Development Center.Google Scholar
  6. Burch, T. K. (1997b). Fertility decline theories: towards a synthetic computer model. Discussion Paper 97–7, Population Studies Centre, University of Western Ontario.Google Scholar
  7. Cartwright, N. D. (1983). How the laws of physics lie. Oxford: Clarendon Press.CrossRefGoogle Scholar
  8. Cartwright, N. D. (1999). The dappled world; a study of the boundaries of science. New York: Cambridge University Press.CrossRefGoogle Scholar
  9. Coale, A. J. (1973). The demographic transition. In IUSSP (Ed.), International Population Conference, Liège (pp. 53–72). Liège, Belgium: IUSSP.Google Scholar
  10. Ekeland, I. (1988). Mathematics and the unexpected. Chicago: University of Chicago Press.Google Scholar
  11. Forrester, J. W. (1969). Urban dynamics. Cambridge, MA: MIT Press.Google Scholar
  12. Friedman, D., Hechter, M., & Kanmazawa, S. (1994). A theory of the value of children. Demography, 31, 375–402.CrossRefGoogle Scholar
  13. Friedman, M. (1953). Essays in positive economics. Chicago: University of Chicago Press.Google Scholar
  14. Giere, R. N. (1988). Explaining science: a cognitive approach. Chicago: University of Chicago Press.CrossRefGoogle Scholar
  15. Giere, R. N. (1999). Science without laws. Chicago: University of Chicago Press.Google Scholar
  16. Hammel, E. A. (1990). Socsim II. Working Paper No.29, Department of Demography, University of California, Berkeley.Google Scholar
  17. Hanneman, R. A. (1988). Computer-assisted theory building: modeling dynamic social systems. Newbury Park, CA: Sage Publications.Google Scholar
  18. Hannon, B., & Ruth, M. (1994). Dynamic modeling. New York: Springer-Verlag.CrossRefGoogle Scholar
  19. Hanson, B., & Appenzeller, T. (1995). Computers ’95: Fluid Dynamics. Science, 269, 1353.CrossRefGoogle Scholar
  20. High Performance Systems, (1996). Stella: an introduction to systems thinking. Hanover, NH: High Performance Systems.Google Scholar
  21. Homans, G. C. (1967). The nature of social science. New York: Harcourt, Brace, & World.Google Scholar
  22. Jacobsen, C, & Bronson, R. (1995). Computer simulations and empirical testing of sociological theory. Sociological Methods and Research, 23, 479–506.CrossRefGoogle Scholar
  23. Jandel Scientific. (1989). TableCurve 2-D: automated curve fitting and equation discovery. San Rafael, CA: Jandel Scientific.Google Scholar
  24. Jasso, G. (1988). Principles of theoretical analysis. Sociological Theory, 6, 1–20.CrossRefGoogle Scholar
  25. Lesthaeghe, R., & Vanderhoeft, C. (1997). Ready, willing and able: a conceptualization of transitions to new behavioral forms. IPD Working Paper, Interface Demography, Vrije Universiteit Brüssel.Google Scholar
  26. Lieberson, S. (1985). Making it count: the improvement of social research and theory. Berkeley: University of California Press.Google Scholar
  27. Meadows, D. H., Meadows, D. L., Randers, J., & Behrens, W. W. (1972). The limits to growth. New York: Universe Books.Google Scholar
  28. Meehan, E. J. (1968). Explanation in social science: a system paradigm. Homewood, ILL: The Dorsey Press.Google Scholar
  29. Meehan, E. J. (1981). Reasoned argument in social science; linking research to policy. Westport, CO: Greenwood Press.Google Scholar
  30. Miller, R. W. (1987). Fact and method: explanation, confirmation and reality in the natural and social sciences. Princeton: Princeton University Press.Google Scholar
  31. Péli, G., Bruggeman, J., Masuch, M., & Ónualláin, B. (1994). A logical approach to formalizing organizational ecology. American Sociological Review, 59, 571–593.CrossRefGoogle Scholar
  32. Piatt, J. R. (1964, October 16). Strong inference. Science, 146, 347–353.CrossRefGoogle Scholar
  33. Popper, K. R. (1959). The logic of scientific discovery. London: Hutchinson & Co.Google Scholar
  34. R.A.K. (1997). Model gets it right—without fudge factors. Science, 276, 1041.CrossRefGoogle Scholar
  35. Richardson, G. P., & Pugh, A. L. (1981). Introduction to system dynamics modeling with Dynamo. Cambridge, MA: Productivity Press.Google Scholar
  36. Roberts, N. et al. (1983). Introduction to computer simulation: the systems dynamics approach. Reading, MA: Addison-Wesley.Google Scholar
  37. Rowe, G. W. (1994). Theoretical models in biology: the origin of life, the immune system, and the brain. Oxford: Oxford University Press.Google Scholar
  38. Tanford, C. (1978, June 2). The hydrophobic effect and the organization of living matter. Science, 200, 1012.CrossRefGoogle Scholar
  39. Timpone, R. J., & Taber, C. S. (1998). Simulation: analytic and algorithmic analyses of Condorcet’s Paradox—variations on a classical theme. Social Science Computer Review, 16, 72–95.CrossRefGoogle Scholar
  40. Wachter, K. W. (1987). Microsimulation of household cycles. In J. Bongaarts, T. K. Burch, & K. W. Wachter (Eds.), Family Demography: Methods and Their Applications (pp. 215–227). Oxford: Clarendon Press.Google Scholar
  41. Wachter, K. W. (1997). Kinship resources for the elderly. Phil. Trans. R. Soc. Lond. B, 352, 1811–1817.CrossRefGoogle Scholar
  42. Wachter, K. W., Blackwell, D., & Hammel, E. (1997). Testing the validity of kinship microsimulation. Journal of Mathematical and Computer Modeling, 26, 89–104.CrossRefGoogle Scholar
  43. Waldrop, M. M. (1992). Complexity: the emerging science at the edge of order and chaos. New York: Simon and Schuster.Google Scholar
  44. Weinberg, S. (1980, December 12). Conceptual foundations of the unified theory of weak and electromagnetic interactions. Science, 210, 1212.CrossRefGoogle Scholar
  45. Wunsch, G. (1995). “God has chosen to give the easy problems to the physicists”: or why demographers need theory. Working Paper No. 179. Institut de Démographie, Université catholique de Louvain.Google Scholar

Copyright information

© Springer Science+Business Media New York 2002

Authors and Affiliations

  • Thomas K. Burch

There are no affiliations available

Personalised recommendations