Minds and Machines

, Volume 11, Issue 4, pp 543–566 | Cite as

Computational versus Causal Complexity

  • Matthias Scheutz


The main claim of this paper is that notions of implementation based on an isomorphic correspondence between physical and computational states are not tenable. Rather, ``implementation'' has to be based on the notion of ``bisimulation'' in order to be able to block unwanted implementation results and incorporate intuitions from computational practice. A formal definition of implementation is suggested, which satisfies theoretical and practical requirements and may also be used to make the functionalist notion of ``physical realization'' precise. The upshot of this new definition of implementation is that implementation cannot distinguish isomorphic bisimilar from non-isomporphic bisimilar systems anymore, thus driving a wedge between the notions of causal and computational complexity. While computationalism does not seem to be affected by this result, the consequences for functionalism are not clear and need further investigations.

causal complexity cognitive science computation computational complexity computationalism functional architecture functionalism implementation realization 


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  1. Agre, P. (1997), Computation and Human Experience, Cambridge: Cambridge University Press.Google Scholar
  2. Barwise, J. and Moss, L. (1996), Vicious Circles, CSLI Lecture Notes, Cambridge University Press.Google Scholar
  3. Block, N. (1996), 'What is Functionalism?' in The Encyclopedia of Philosophy Supplement, Macmillan.Google Scholar
  4. Chalmers, D.J. (1994), 'On Implementing a Computation', Minds and Machines 4, 391–402.Google Scholar
  5. Chalmers, D.J. (1996), 'Does a Rock Implement Every Finite-State Automaton?', Synthese 108, 310–333.Google Scholar
  6. Chrisley, R.L. (1994), 'Why Everything Doesn't Realize Every Computations', Minds and Machines 4, 391–402.Google Scholar
  7. Copeland, B.J. (1996), 'What is Computation?', Synthese 108, 403–420.Google Scholar
  8. Cummins, R. (1989), Meaning and Mental Representation, Cambridge, MA, MIT Press.Google Scholar
  9. David, M. (1997), 'Kim's Functionalism', in Philosophical Perspectives, 11, Mind, Causation, and World.Google Scholar
  10. Hopcroft, J.E. and Ullman, J.D. (1979), Introduction to Automata Theory, Languages, and Computation, Massachusetts, Addison-Wesley Publishing Company.Google Scholar
  11. Kim, J. (1998), Mind in a Physical World, Cambridge, MA: MIT Press.Google Scholar
  12. MacLennan, B.J. (1994), 'Words Lie in Our Way', Minds and Machines 4, 421–437.Google Scholar
  13. Melnyk, A. (1996), 'Searle's Abstract Argument Against Strong AI', Synthese 108, 391–419.Google Scholar
  14. Papineau, D. (1995), 'Arguments for Supervenience and Physical Realization'. in Savellos, E. and Yalçin, Ñ, eds., Supervenience, Cambridge University Press.Google Scholar
  15. Putnam, H. (1988), Representation and Reality, Cambridge: MIT Press.Google Scholar

Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  • Matthias Scheutz
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of Notre DameNotre DameUSA

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