Knowledge in Society

, Volume 3, Issue 1, pp 46–61 | Cite as

Artificial intelligence and metaphor making: Some philosophic considerations

  • Harold D. Carrier
Feature Articles


This article argues that the metaphors presently employed in describing artificial intelligence represent the use of personification and anthropomorphism. They attempt to develop an isomorphic relationship between the human mind and a computer’s logic. It is suggested that an analogic metaphor is more appropriate in describing this relationship and is more epistemologically correct.


Expert System Machine Intelligence Human Intelligence Strong Artificial Intelligence Human Cognitive Capacity 
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. Bernstein, R.J. (1971).Praxis and action. Philadelphia, PA.: University of Pennsylvania Press.Google Scholar
  2. Boden, M.A. (1979). The computational metaphor in psychology. In N. Bolton (Ed.),Philosophical problems in psychology. New York: Methuen.Google Scholar
  3. Boyd, R. (1979). Metaphor and theory change: What is “metaphor” a metaphor for. In A. Ortony (Ed.),Metaphor and thought.Cambridge:Cambridge University Press.Google Scholar
  4. Brown, R.R. (1977).A poetic for sociology. Cambridge: Cambridge University Press.Google Scholar
  5. Burrell, G. & Morgan, G. (1979).Sociological paradigms and organizational analysis: Elements of the sociology of corporate life. London: Heineman.Google Scholar
  6. Campbell, J. (1982).Grammatical man: Information, entropy, language, and life. New York: Simon & Schuster.Google Scholar
  7. Carrier, H.D., & Wallace, W.A. (1990). A philosophic comparison of decision aid techniques for the policy analyst.Evaluation and Program Planning (in press).Google Scholar
  8. Chomsky, N. (1957).Syntactic structures. The Hague: Mouton.Google Scholar
  9. Churchman C.W. (1961).Prediction and optimal decision: Philosophical issues of a science of values. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  10. Churchman, C.W. (1971).The design of inquiring systems. New York: Basic Books.Google Scholar
  11. Dennett, D.C. (1981). Intentional systems. In J. Haugeland (Ed.),Mind design: Philosophy, psychology, artificial intelligence. Cambridge MA: MIT Press.Google Scholar
  12. Dreyfus, H.L. From micro-worlds to knowledge representation: AI at an impasse. In J. Haugeland (Ed.),Mind design Philosophy, psychology, artificial intelligence. Cambridge MA: MIT Press.Google Scholar
  13. Dreyfus, H.L. & Dreyfus, S.E. (1986).Mind over machine: The power of human intuition and expertise in the era of the computer. New York: Free Press.Google Scholar
  14. Descartes, R. (1979).Meditations on first philosophy. Indianapolis, IN: Hackett Publishing Co.Google Scholar
  15. Fodor, J.A. (1975).Language and thought. New York: Thomas Y. Crowell.Google Scholar
  16. Harmon, P. & King, D. (1985).Expert systems: Artificial intelligence in business. New York: Wiley and Sons.Google Scholar
  17. Hilton, M.A. (1963).Logic, computing machines, and automation. New York: The World Publishing Company.Google Scholar
  18. Hirschhiem, R.A. (1985). Information systems epistemology: An historic perspective. In E. Mumford, R.A. Hirschheim, G. FitzGerald, & T. Wood-Harper (Eds.),research methods in information systems. North-Holland.Google Scholar
  19. Hofstadter, D.R. (1979).Godel, escher, bach: An eternal golden braid. New York: Basic Books.Google Scholar
  20. Ihde, D. (1979).Technics and praxis. Boston, MA: D. Reidel.Google Scholar
  21. Jung, C.G. (1922).Psychological types. London: Rutledge and Kegan Paul.Google Scholar
  22. Kuhn, T. (1970).The structure of scientific revolutions (2d Ed.). Chicago, IL: University of Chicago Press.Google Scholar
  23. Lackoff, G., & Johnson, M. (1980).Metaphors we live by. Chicago, IL: University of Chicago Press.Google Scholar
  24. Lighthill, J. (1973). Artificial intelligence: A general survey. InArtificial Intelligence: A Paper Symposium. London: Great Britain Research Council.Google Scholar
  25. Lucas, J. (1963). Minds, machines and godel. In K. Sayre & F. Crosson (Eds.),The modeling of mind. Notre Dame, IN: University of Notre Dame Press.Google Scholar
  26. McCarthy, J. (1977). Epistemological problems of artificial intelligence.Proceedings of the 5th International Joint Conference on Artificial Intelligence. Cambridge, MA: Massachusetts Institute of Technology.Google Scholar
  27. McDermott, D. (1981). Artificial intelligence meets natural stupidity. In J. Haugeland (Ed.),Mind design: Philosophy, psychology, artificial intelligence. Cambridge MA: MIT Press.Google Scholar
  28. Majone, G. (1980). Policies as theories.OMEGA: The International Journal of Management Science, 8, 151–162.CrossRefGoogle Scholar
  29. Mason, M.O. & Mitroff, I.I. (1973). A program for research on management information systems.Management Science, 19, 475–87.Google Scholar
  30. Mitroff, I.I. & Betz, F. (1972). Dialectical decision theory: A meta-theory of decision-making.Management Science, 19, 11–24.Google Scholar
  31. Mitroff, I.I., Kilmann, R.H. (1982). On evaluating scientific research: The contribution of the psychology of science.Journal of Technological Forecasting and Social Change, 6, 389–402.CrossRefGoogle Scholar
  32. Morris, W.T. (1963).Management science in action. Homewood, IL: Richard D. Irwin.Google Scholar
  33. Morris, W.T. (1967). On the art of modeling.Management Science, 13, 707–716.CrossRefGoogle Scholar
  34. Morris, W.T. (1972).Management for action: Psycho-technical decision making. Reston, VA: Reston Publishing.Google Scholar
  35. Munevar, G. (1981).Radical knowledge: A philosophical inquiry into the nature and limits of science. Indianapolis, IN: Hackett Publishing.Google Scholar
  36. Neumann, J. von (1966). Lecture on redundancy at University of Illinois. In W. Burks (Ed.),Theory of self-reproducing automata. Urbana, IL: University of Illinois Press.Google Scholar
  37. Newell, A. & Simon, H. (1972).Human problem solving. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  38. Polanyi, M. (1966).The tacit dimension. Garden City, NY: Doubleday.Google Scholar
  39. Putnam, H. (1967). The mental life of some machines. In H. Castaneda (Ed.),Intentionality, Minds and perception. Detroit, MI: Wayne State University Press.Google Scholar
  40. Pylyshyn, Z. (1981). Complexity and the study of artificial intelligence. In J. Haugeland (Ed.)Mind design: Philosophy, psychology, artificial intelligence. Cambridge MA: MIT Press.Google Scholar
  41. Searle, J.R. (1981). Minds, brains, and programs. In J. Haugeland (Ed.),Mind design: Philosophy, psychology, artificial intelligence. Cambidge MA: MIT Press.Google Scholar
  42. Simon, H.A. (1954). Some strategic considerations in the construction of social science models. In P. Lazarsfeld (Ed.),Mathematical thinking in the social sciences. Glencoe, IL: The Free Press.Google Scholar
  43. Sutherland, J.W. (1986). Assessing the artificial intelligence contribution to decision technology. IEEE Transactions on Systems, Man, and Cybernetics, SMC-16, No. 1.Google Scholar
  44. Torrance, S.B. (1984).The mind and the machine New York: John Wiley & Sons.Google Scholar
  45. Turing, A.M. (1950). Computing machinery and intelligence.Mind, 59, 433–460.CrossRefGoogle Scholar
  46. Winner, L. (1975). Complexity and the limits of human understanding. In T.R. LaPorte (Ed.),Organized social complexity. Princeton: NJ: Princeton University Press.Google Scholar
  47. Wittgenstein, L. (1953).Philosophic investigations. New York: Macmillan.Google Scholar

Copyright information

© Springer 1990

Authors and Affiliations

  • Harold D. Carrier

There are no affiliations available

Personalised recommendations