Skip to main content
SpringerLink
Log in
Book cover

LAWS, LANGUAGE and LIFE pp 227–243Cite as

Artificial Life Needs a Real Epistemology

  • Chapter
  • First Online:

  • 1343 Accesses

Part of the book series: Biosemiotics ((BSEM,volume 7))

Abstract

Foundational controversies in artificial life and artificial intelligence arise from lack of decidable criteria for defining the epistemic cuts that separate knowledge of reality from reality itself, e.g., description from construction, simulation from realization, mind from brain. Selective evolution began with a description-construction cut, i.e., the genetically coded synthesis of proteins. The highly evolved cognitive epistemology of physics requires an epistemic cut between reversible dynamic laws and the irreversible process of measuring initial conditions. This is also known as the measurement problem. Good physics can be done without addressing this epistemic problem, but not good biology and artificial life, because open-ended evolution requires the physical implementation of genetic descriptions. The course of evolution depends on the speed and reliability of this implementation, or how efficiently the real or artificial physical dynamics can be harnessed by non-dynamic genetic symbols.

Reprinted from Advances in Artificial Life, F. Moran, A. Moreno, J. J. Merelo, O. Chacon, Eds. Berlin: Springer, 1995, pp. 23–38.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   309.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   299.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Adelman, L. M. (1994). Molecular computation of solutions to combinatorial problems. Science, 266, 1021–1024.

    Article  Google Scholar 

  • Barrow, J. D. (1991). Theories of everything (p. 203). New York: Oxford University Press.

    Google Scholar 

  • Bennett, C. H. (1982). The thermodynamics of computation—A review. International Journal of Theoretical Physics, 21, 905–940.

    Article  CAS  Google Scholar 

  • Bennett, C. H. (1987). Demons, engines, and the second law. Scientific American, 257, 108–116.

    Article  Google Scholar 

  • Born, M. (1964). Symbol and reality. Universitas 7, 337–353. Reprinted in Born, M. Physics in my generation (pp. 132–146). New York: Springer.

    Google Scholar 

  • Brooks, R. (1992). Artificial life and real robots. In F. Varela & P. Bourgine (Eds.), Toward a practice of autonomous systems (pp. 3–10). Cambridge, MA: MIT Press.

    Google Scholar 

  • Cassirer, E. (1957). The philosophy of symbolic forms (The phenomena of knowledge, Vol. 3). New Haven: Yale University Press.

    Google Scholar 

  • Chaitin, G. (1982). Gödel’s theorem and information. International Journal of Theoretical Physics, 21, 941–953.

    Article  Google Scholar 

  • Chaitin, G. (1987). Algorithmic information theory. Cambridge/ New York: Cambridge University Press.

    Book  Google Scholar 

  • Churchland, P. S., & Sejnowski, T. J. (1992). The computational brain. Cambridge, MA: MIT Press.

    Google Scholar 

  • Conrad, M. (1983). Adaptability. New York: Plenum.

    Book  Google Scholar 

  • Conrad, M. (1989). The brain-machine disanalogy. BioSystems, 22, 197–213.

    Article  PubMed  CAS  Google Scholar 

  • Conrad, M. (1990). The geometry of evolution. BioSystems, 24, 61–81.

    Article  PubMed  CAS  Google Scholar 

  • Curie, P. (1908). Oeuvres (p. 127). Paris: Gauthier-Villars.

    Google Scholar 

  • Curtis, C., & Greenslet, F. (1945). The practical cogitator (p. 277). Boston: Houghton Mifflin.

    Google Scholar 

  • Dennett, D. (1994). Artificial life as philosophy. Artificial Life, 1, 291–292.

    Article  Google Scholar 

  • Dietrich, E. (1994). Thinking computers and virtual persons (p. 13). New York: Academic Press.

    Google Scholar 

  • Eddington, A. (1928). The nature of the physical world (p. 260). New York: Macmillan.

    Google Scholar 

  • Eigen, M. (1992). Steps toward life. Oxford: Oxford University Press.

    Google Scholar 

  • Fontana, W., Wagner, G., & Buss, L. W. (1994). Beyond digital naturalism. Artificial Life, 1, 211–227.

    Article  Google Scholar 

  • Gödel, K. (1964). Russell’s mathematical logic, and what is Cantor’s continuum problem? In P. Benacerraf & H. Putnam (Eds.), Philosophy of mathematics (pp. 211–232). Englewood Cliffs: Prentice-Hall, 258–273.

    Google Scholar 

  • Harnad, S. (1990). The symbol grounding problem. Physica D, 42, 335–346.

    Article  Google Scholar 

  • Hopfield, J. J. (1994). Physics, computation, and why biology looks so different. Journal of Theoretical Biology, 171, 53–60.

    Article  Google Scholar 

  • Houtappel, R. M. F., Van Dam, H., & Wigner, E. P. (1965). The conceptual basis and use of the geometric invariance principles. Reviews of Modern Physics, 37, 595–632.

    Article  Google Scholar 

  • Hunter, L. (Ed.). (1993). Artificial intelligence and molecular biology. Menlo Park: AAAI/MIT Press.

    Google Scholar 

  • Jaynes, E. (1990). Probability in quantum theory. In W. H. Zurek (Ed.), Complexity, entropy, and the physics of information (p. 382). Redwood City: Addison-Wesley.

    Google Scholar 

  • Kleene, S. C. (1952). Introduction to metamathematics. Princeton: Van Nostrand.

    Google Scholar 

  • Landauer, R. (1986). Computation and physics: Wheeler’s meaning circuit. Foundations of Physics, 16, 551–564.

    Article  Google Scholar 

  • Langton, C. (1989). Artificial life. In C. Langton (Ed.), Artificial life (pp. 1–47). Red-wood City: Addison-Wesley.

    Google Scholar 

  • Langton, C., Taylor, C., Farmer, J., & Rasmussen, S. (Eds.). (1992). Artificial life II. Redwood City: Addison-Wesley.

    Google Scholar 

  • Leff, H. S., & Rex, A. F. (Eds.). (1990). Maxwell’s demon, entropy, information, computing. Princeton: Princeton University Press.

    Google Scholar 

  • Pattee, H. H. (1969). How does a molecule become a message? Developmental Biology Supplement, 3, 1–16.

    Google Scholar 

  • Pattee, H. H. (1972). Laws, constraints, symbols, and languages. In C. H. Waddington (Ed.), Towards a theoretical biology (Vol. 4, pp. 248–258). Edinburgh: Edinburgh University Press.

    Google Scholar 

  • Pattee, H. H. (1982). Cell psychology: An evolutionary approach to the symbol-matter problem. Cognition and Brain Theory, 5(4), 325–341.

    Google Scholar 

  • Pattee, H. H. (1988). Simulations, realizations, and theories of life. In C. Langton (Ed.), Artificial life (pp. 63–77). Redwood City: Addison-Wesley.

    Google Scholar 

  • Pattee, H. H. (1989). The measurement problem in artificial world models. BioSystems, 23, 281–290.

    Article  PubMed  CAS  Google Scholar 

  • Pattee, H. H. (1990). Response to E. Dietrich’s “Computationalism”. Social Epistemology, 4(2), 176–181.

    Google Scholar 

  • Pattee, H. H. (1993). The limitations of formal models of measurement, control, and cognition. Applied Mathematicals and Computation, 56, 111–130.

    Article  Google Scholar 

  • Pattee, H. H. (1995). Evolving self-reference: Matter, symbols, and semantic closure. Communication and Cognition - AI, 12(1–2), 9–28.

    Google Scholar 

  • Penrose, R. (1989). The emperor’s new mind (p. 352). Oxford: Oxford University Press.

    Google Scholar 

  • Planck, M. (1960). A survey of physical theory (p. 64). New York: Dover.

    Google Scholar 

  • Polanyi, M. (1964). Personal knowledge. New York: Harper & Row.

    Google Scholar 

  • Rosen, R. (1986). Causal structures in brains and machines. International Journal of General Systems, 12, 107–126.

    Article  Google Scholar 

  • Schuster, P. (1994). Extended molecular evolutionary biology: Artificial life bridging the gap between chemistry and biology. Artificial Life, 1, 39–60.

    Article  Google Scholar 

  • Toffoli, T. (1982). Physics and computation. International Journal of Theoretical Physics, 21, 165–175.

    Article  Google Scholar 

  • Turing, A. (1936). On computable numbers, with an application to the Entscheidungsproblem. Proceedings of the London Mathematical Society, 42, 230–265.

    Article  Google Scholar 

  • von Neumann, J. (1955). Mathematical foundations of quantum mechanics. Princeton: Princeton University Press. Chapter VI.

    Google Scholar 

  • von Neumann, J. (1966). In A. Burks (Ed.), The theory of self-reproducing automata. Urbana: University of Illinois Press.

    Google Scholar 

  • von Weizsäcker, C. F. (1973). Probability and quantum mechanics. British Journal for the Philosophy of Science, 24, 321–337.

    Article  Google Scholar 

  • Wheeler, J. A. (1990). Information, physics, quantum: The search for links. In W. H. Zurek (Ed.), Complexity, entropy, and the physics of information. Redwood City: Addison-Wesley.

    Google Scholar 

  • Wheeler, J., & Zurek, W. (1983). Quantum theory and measurement. Princeton: Princeton University Press.

    Google Scholar 

  • Whitehead, A. N. (1927). Symbolism: Its meaning and effect. New York: Macmillan.

    Google Scholar 

  • Whitley, D., & Hanson, T. (1989). Optimizing neural networks using faster, more accurate genetic search. In Proceedings of the third international conference on GAs (pp. 391–396). Burlington, MA: Morgan Kauffman.

    Google Scholar 

  • Wigner, E. P. (1964). Events, laws, and invariance principles. Science, 145, 995–999.

    Article  PubMed  CAS  Google Scholar 

  • Wilber, K. (1985). Quantum questions. Boston: Shambala.

    Google Scholar 

  • Zuker, M. (1989). On finding all suboptimal folding of large RNA sequences using thermodynamics and auxiliary information. Nucleic Acids Research, 9, 133.

    Article  Google Scholar 

  • Zurek, W. H. (1990). Complexity, entropy, and the physics of information. Redwood City: Addison Wesley.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Binghamton University, Vestal, NY, USA

    Howard Hunt Pattee

Authors
  1. Howard Hunt Pattee
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Pattee, H.H. (2012). Artificial Life Needs a Real Epistemology. In: LAWS, LANGUAGE and LIFE. Biosemiotics, vol 7. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5161-3_15

Download citation

Publish with us

Policies and ethics

Search

Navigation