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Causation, Control, and the Evolution of Complexity

  • Howard Hunt Pattee
Chapter
Part of the Biosemiotics book series (BSEM, volume 7)

Abstract

It is not obvious that the concept of causation, in any of its many forms, has ever played a necessary role in the discovery of the laws of nature. Causation has a tortuous philosophical literature with no consensus in sight (e.g., Hart and Honoré 1958; Bunge 1959; Taylor 1972), and modern physics has little interest in the concept. Nevertheless, causation is so ingrained in both the syntax and semantics of our natural language that we usually feel that events are somehow causally explained by almost any grammatically correct declarative statement that relates a noun and a verb phrase to the event: Why did the ball roll? Because John kicked the ball. Why did the ball bounce? Because the ball hit the post. In Aristotelian terms, the verb is a form of efficient cause, and either the subject or object can act as a material cause. If the subject happens to have a large brain we may also attribute a formal, teleological, or intentional cause to the event: Why did John kick the ball? Because John wanted a goal. As a child we figure out that these linguistic forms are transitive and always lead to a vicious circle or an infinite regress, but we are usually told that it is rude to continue to ask, “Why?” when presented with one proximal cause. The major weakness of the concept of causation is this Whorfian dependence on natural language. Thus, the richness and ambiguity of causal forms arises more from the richness and ambiguities of language than from any empirical necessity or from natural laws.

Keywords

Natural Language Cellular Automaton Linguistic Form Downward Causation Complementary Model 
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.

References

  1. Anderson, P. W., & Stein, D. L. (1988). Broken symmetry, emergent properties, dissipative structures, life; Are they related? In F. E. Yates (Ed.), Self-organizing systems—The emergence of order (pp. 445–457). New York: Pergamon.Google Scholar
  2. Bridgman, P. W. (1964). The nature of physical theory (p. 58). New York: Wiley.Google Scholar
  3. Brooks, R. A. (1992). Artificial life and real robots. In F. J. Varela & P. Bourgine (Eds.), Toward a practice of autonomous systems (pp. 3–10). Cambridge, MA: MIT Press.Google Scholar
  4. Bunge, M. (1959). Causality. Cambridge: Harvard University Press.Google Scholar
  5. Conrad, M. (1990). The geometry of evolution. BioSystems, 24, 61–81.PubMedCrossRefGoogle Scholar
  6. Coveney, P., & Highfield, R. (1991). The arrow of time. New York: Ballantine.Google Scholar
  7. Dummett, M. (1964). Bringing about the past. Philosophical Review, 73, 338–359.CrossRefGoogle Scholar
  8. Eigen, M., & Schuster, P. (1982). Stages of emerging life—Five principles of early organization. Journal of Molecular Biology, 19, 47–61.Google Scholar
  9. Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Reading: Addison-Wesley.Google Scholar
  10. Hart, H. L. A., & Honoré, A. M. (1958). Causation and the law. Oxford: Oxford University Press.Google Scholar
  11. Hasslacher, B., & Tilden, M. W. (1995). Living machines. In L. Steels (ed.), Robotic and autonomous systems: The biology and technology of intelligent autonomous agents. Amsterdam, The Netherlands: Elsevier.Google Scholar
  12. Hawking, S. (1988). A brief history of time (pp. 143–153). New York: Bantam Books.Google Scholar
  13. Hertz, H. (1894). Die Principien Mechanik (English trans. D. E. Jones & J. T. Walley). The principles of mechanics. New York: Dover, 1956.Google Scholar
  14. Holland, J. H. (1992). Adaptation in natural and artificial systems (2nd ed.). Cambridge, MA: MIT Press.Google Scholar
  15. Jakobson, R. (1970). Main trends of research in the social and human sciences (pp. 437–440). Paris: Mouton/UNESCO.Google Scholar
  16. Kauffman, S. (1993). Origins of order. New York: Oxford University Press.Google Scholar
  17. Langton, C. (1989). Artificial life. In C. Langton (Ed.), Artificial life (pp. 1–47). Redwood City: Addison-Wesley.Google Scholar
  18. Maes, P. (1992). Learning behavior networks from experience. In F. J. Varela & P. Bourgine (Eds.), Toward a practice of autonomous systems (pp. 48–57). Cambridge, MA: MIT Press.Google Scholar
  19. Mitchell, M., Crutchfield, J. P., & Hraber, P. T. (1994). Evolving cellular automata to perform computations: Mechanisms and impediments. Physica D, 75, 361–391.CrossRefGoogle Scholar
  20. Nicolis, G., & Prigogine, I. (1989). Exploring complexity. New York: Freeman.Google Scholar
  21. Pattee, H. H. (1980). Clues from molecular symbol systems. In U. Bellugi and M. Studdert-Kennedy (eds.), Signed and spoken language: Biological constraints on linguistic form (pp. 261–274), Dahlem Konferenzen Report 19. Weinheim: Verlag Chemie.Google Scholar
  22. Pattee, H. H. (1985). Universal principles of measurement and language function in evolving systems. In J. Casti & A. Karlqvist (Eds.), Language and life: Mathematical approaches (pp. 268–281). Berlin: Springer.Google Scholar
  23. Pattee, H. H. (1995). Evolving self-reference: Matter, symbols, and semantic closure. Communication and Cognition—Artificial Intelligence, 12(1–2), 9–28.Google Scholar
  24. Planck, M. (1960). A survey of physical theory (p. 64). New York: Dover.Google Scholar
  25. Schuster, P. (1994). Extended molecular evolutionary biology: Artificial life bridging the gap between chemistry and biology. Artificial Life, 1, 39–60.CrossRefGoogle Scholar
  26. Taylor, R. (1972). Causation. In P. Edwards (Ed.), The encyclopedia of philosophy (Vol. 1&2, pp. 56–66). New York: Macmillan.Google Scholar
  27. Tolman, R. C. (1950). The principles of statistical mechanics (p. 157). Oxford: Oxford University Press.Google Scholar
  28. von Neumann, J. (1955). The mathematical foundations of quantum mechanics (p. 351). Princeton: Princeton University Press, p. 420.Google Scholar
  29. von Neumann, J. (1966). In A. Burks (Ed.), Theory of self-reproducing automata. Urbana: University of Illinois Press.Google Scholar
  30. Weyl, H. (1949). Philosophy of mathematics and natural science (p. 203). Princeton: Princeton University Press.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  • Howard Hunt Pattee
    • 1
  1. 1.Binghamton UniversityVestalUSA

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