Minds and Machines

, Volume 28, Issue 3, pp 427–444 | Cite as

The Role of Observers in Computations

How Much Computation Does it Take to Recognize a Computation?
  • Peter LeupoldEmail author


John Searle raised the question whether all computation is observer-relative. Indeed, all of the common views of computation, be they semantical, functional or causal rely on mapping something onto the states of a physical or abstract process. In order to effectively execute such a mapping, this process would have to be observed in some way. Thus a probably syntactical analysis by an observer seems to be essential for judging whether a given process implements some computation or not. In order to be able to explore the nature of these observers in a more formal way, we look at the Computing by Observing paradigm, a theoretical model of computation that includes an observer. We argue that the observers used there, monadic transducers, are good candidates for formalizing the way in which the syntax of a process must be analysed in order to judge whether it is computational.


Computation Observer Observer-relativity 



The two anonymous referees have invested a great amount of time in the long refereeing process and in sharing their knowledge in a very constructive way with the author. Thus they have effectuated several important changes in the manuscript and have had an essential part in letting it evolve to its current state. The author feels greatly indebted to both of the referees for their memorable patience and their openness of mind.


  1. Aaronson, S. (2013). Why philosophers should care about computational complexity. In B. J. Copeland & O. S. C. Posy (Eds.), Computability: Turing, gdel, church, and beyond (pp. 261–328). London: MIT Press.Google Scholar
  2. Adleman, L. (1994). Molecular computation of solutions to combinatorial problems. Science, 226, 1021–1024.CrossRefGoogle Scholar
  3. Alhazov, A., & Cavaliere, M. (2004). Computing by observing bio-systems: The case of sticker systems. In C. Ferretti, G. Mauri, & C. Zandron (Eds.), DNA (Vol. 3384, pp. 1–13)., Lecture Notes in Computer Science Berlin: Springer.Google Scholar
  4. Bickle, J. (2013). Multiple realizability. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy (2013th ed.). Berlin: Springer.Google Scholar
  5. Book, R., & Otto, F. (1993). String-rewriting systems. Berlin: Springer.CrossRefzbMATHGoogle Scholar
  6. Cavaliere, M., Frisco, P., & Hoogeboom, H. J. (2006). Computing by only observing. In O. H. Ibarra & Z. Dang (Eds.), Developments in language theory (Vol. 4036, pp. 304–314)., Lecture Notes in Computer Science Berlin: Springer.CrossRefGoogle Scholar
  7. Cavaliere, M., & Leupold, P. (2003). Evolution and observation: A new way to look at membrane systems. In C. Martín-Vide, G. Mauri, G. Paun, G. Rozenberg, & A. Salomaa (Eds.), Workshop on membrane computing (Vol. 2933, pp. 70–87). Lecture Notes in Computer Science, Berlin: Springer.CrossRefGoogle Scholar
  8. Cavaliere, M., & Leupold, P. (2004). Evolution and observation—A non-standard way to generate formal languages. Theoretical Computer Science, 321, 233–248.MathSciNetCrossRefzbMATHGoogle Scholar
  9. Cavaliere, M., & Leupold, P. (2006). Observation of string-rewriting systems. Fundamenta Informaticae, 74(4), 447–462.MathSciNetzbMATHGoogle Scholar
  10. Chalmers, D. J. (1996). Does a rock implement every finite-state automaton? Synthese, 108, 309–333.MathSciNetCrossRefzbMATHGoogle Scholar
  11. Dassow, J., Mitrana, V., & Salomaa, A. (2002). Operations and language generating devices suggested by the genome evolution. Theoretical Computer Science, 270(1–2), 701–738.MathSciNetCrossRefzbMATHGoogle Scholar
  12. Fodor, J. A. (1981). The mind–body problem. Scientific American, 241, 114–123.CrossRefGoogle Scholar
  13. Fresco, N. (2010). Explaining computation without semantics: Keeping it simple. Minds and Machines, 20, 165–181. Scholar
  14. Krassovitskiy, A., & Leupold, P. (2012). Computing by observing insertion. In A. H. Dediu & C. Martín-Vide (Eds.), LATA (Vol. 7183, pp. 377–388)., Lecture Notes in Computer Science Berlin: Springer.Google Scholar
  15. Landweber, L. F., & Kari, L. (2002). Universal molecular computation in ciliates. In L. F. Landweber & E. Winfree (Eds.), Evolution as computation, natural computing series (pp. 257–274). Berlin: Springer.CrossRefGoogle Scholar
  16. Morse, M. (1938). A solution of the problem of infinite play in chess. Bulletin of the American Mathemetical Society, 44, 632.Google Scholar
  17. Păun, G., Rozenberg, G., & Salomaa, A. (1998). DNA computing—New computing paradigms. Berlin: Springer.CrossRefzbMATHGoogle Scholar
  18. Piccinini, G. (2008). Computation without representation. Philosophical Studies, 137(2), 205–241. Scholar
  19. Putnam, H. (1988). Representation and reality. London: MIT Press.Google Scholar
  20. Searle, J. R. (1992). The rediscovery of the mind. London: MIT Press.Google Scholar
  21. Turing, A. (1937). On computable numbers, with an application to the entscheidungsproblem. Proceedings of the London Mathematical Society, 2(42), 230–265.MathSciNetCrossRefzbMATHGoogle Scholar
  22. von Braunmühl, B., & Verbeek, R. (1979). Finite-change automata. In K. Weihrauch (Ed.), Theoretical computer science 4th GI conference (Vol. 67, pp. 91–100)., Lecture Notes in Computer Science Berlin: Springer.CrossRefGoogle Scholar

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© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.CreixellSpain

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