The instructional information processing account of digital computation
- 305 Downloads
What is nontrivial digital computation? It is the processing of discrete data through discrete state transitions in accordance with finite instructional information. The motivation for our account is that many previous attempts to answer this question are inadequate, and also that this account accords with the common intuition that digital computation is a type of information processing. We use the notion of reachability in a graph to defend this characterization in memory-based systems and underscore the importance of instructional information for digital computation. We argue that our account evaluates positively against adequacy criteria for accounts of computation.
KeywordsDigital computation Information processing Instructional information Turing machines Finite state automata Physical computation Computational taxonomy
We thank Oron Shagrir and Luciano Floridi for their helpful comments on earlier drafts of this paper. We are grateful to the anonymous referees for their constructive critiques and suggestions that have significantly improved the paper. A preliminary version of this paper was presented at the 2012 CiE Turing Centenary Conference at Cambridge University, Cambridge, U.K. A significant part of this research was conducted while Nir Fresco was a visiting fellow at the School of Humanities & Languages, University of New South Wales, Australia. He gratefully acknowledges their support. The usual disclaimer applies: any remaining mistakes are the sole responsibility of the authors.
- Copeland, B. J. (2004). Computation. In L. Floridi (Ed.), The Blackwell guide to the philosophy of computing and information (pp. 3–17). Malden, MA: Blackwell.Google Scholar
- Floridi, L. (2014). Perception and Testimony as Data Providers. In F. Ibekwe-SanJuan & T. M. Dousa (Eds.) Theories of Information, Communication and Knowledge. doi: 10.1007/978-94-007-6973-1_4
- Fresco, N. (2013). Information processing as an account of concrete digital computation. Philosophy & Technology, 26(1), 31–60. doi: 10.1007/s13347-011-0061-4.
- Fresco, N. (In Press). Physical computation and cognitive science (1st ed., Vol. 12). New York, NY: Springer Berlin Heidelberg.Google Scholar
- Fresco, N. (unpublished). Objective computation vs. subjective computation.Google Scholar
- Fresco, N., & Primiero, G. (2013). Miscomputation. Philosophy & Technology, 26(3), 253–272. doi: 10.1007/s13347-013-0112-0.
- Fresco, N., & Wolf, M. J. (unpublished). Connectionist computation as information processing.Google Scholar
- Godfrey-Smith, P. (2008). Information in biology. In D. Hull & M. Ruse (Eds.), Cambridge companions to philosophy (pp. 103–119). New York: Cambridge University Press.Google Scholar
- Godfrey-Smith, P., & Sterelny, K. (2008). Biological information. In E. N. Zalta The Stanford encyclopedia of philosophy (Fall). Stanford, CA: Stanford University. http://plato.stanford.edu/archives/fall2008/entries/information-biological/.
- Gurevich, Y. (2012). What is an algorithm? In M. Bieliková, G. Friedrich, G. Gottlob, S. Katzenbeisser, & G. Turán (Eds.), SOFSEM 2012: Theory and practice of computer science (Vol. 7147, pp. 31–42). Berlin: Springer.Google Scholar
- Hamblin, C. L. (1987). Imperatives. New York: Basil Blackwell.Google Scholar
- Harris, D. M., & Harris, S. L. (2013). Digital design and Computer architecture. Waltham, MA: Elsevier: Morgan Kaufmann.Google Scholar
- Haugeland, J. (1985). Artificial intelligence: The very idea. Cambridge, MA: MIT Press.Google Scholar
- Lloyd, S. (2006). Programming the Universe: A quantum computer scientist takes on the cosmos. New York: Knopf.Google Scholar
- Matthews, R. (2011). Natural computation. Jerusalem: The Institute for Advanced Studies, Hebrew University in Jerusalem. https://sites.google.com/site/iascomputationbrainhuji2011/home/previous-lectures/Matthews%282011%29NaturalComputation.pdf?attredirects=0&d=1.
- Miestamo, M., & van der Auwera, J. (2007). Negative declaratives and negative imperatives: Similarities and differences. In A. Ammann (Ed.), Linguistics festival: May 2006, Bremen (pp. 59–77). Diversitas Linguarum v. 14. Bochum: Universitätsverlag Brockmeyer.Google Scholar
- Neary, T., & Woods, D. (2012). The complexity of small universal Turing machines: A survey. In M. Bieliková, G. Friedrich, G. Gottlob, S. Katzenbeisser, & G. Turán (Eds.), SOFSEM 2012: Theory and practice of computer science (Vol. 7147, pp. 385–405). Berlin: Springer.Google Scholar
- Pérez-Ramírez, M., & Fox, C. (2003). Imperatives as obligatory and permitted actions. In A. Gelbukh (Ed.), Computational linguistics & intelligent text processing (Vol. 2588, pp. 52–64). Berlin: Springer.Google Scholar
- Putnam, H. (1988). Representation and reality. Cambridge, MA: MIT Press.Google Scholar
- Pylyshyn, Z. W. (1984). Computation and cognition: Toward a foundation for cognitive science. Cambridge, MA: The MIT Press.Google Scholar
- Van Leeuwen, J. (forthcoming). On Floridi’s method of levels of abstraction. Minds and Machines (Special Issue on Floridi and Philosophy of Information).Google Scholar