A Taxonomy of Errors for Information Systems
- 301 Downloads
We provide a full characterization of computational error states for information systems. The class of errors considered is general enough to include human rational processes, logical reasoning, scientific progress and data processing in some functional programming languages. The aim is to reach a full taxonomy of error states by analysing the recovery and processing of data. We conclude by presenting machine-readable checking and resolve algorithms.
KeywordsErrors Informational semantics Type-checking
Drafts of this paper were discussed at the Fourth Workshop in the Philosophy of Information, University of Hertfordshire and at the Conference on Judgement and Justification, University of Tampere. I wish to thank the participants for helpful discussions. Two anonymous referees have offered criticisms and remarks that have helped clarifying various passages of this work. My personal thanks to Patrick Allo for his comments and observations.
- Agarwal, B. B., Gupta, M., & Tayal, S. P. (2009). Software engineering and testing: an introduction. Jones & Bartlett Learning, Burlington, MA.Google Scholar
- Allchin, D. (2000). The epistemology of errors. In Philosophy of science association, Vancouver.Google Scholar
- Allo, P., & Mares, E. (2012). Informational semantics as a third alternative? Erkenntnis, 77(2), 167–185.Google Scholar
- Baker, L. R. (2009). The metaphysics of malfunction. Techné: Research in Philosophy and Technology, 13(2), 82–92.Google Scholar
- Beaver, D. (2001). Presupposition and assertion in dynamic semantics. Stanford: CSLI Publications.Google Scholar
- Bonnay, D., & Egre’, P. (2011). Knowing one’s limits—An analysis in centered dynamic epistemic logic. In: P. Girard, O. Roy, M. Marion (Eds.), Dynamic Formal Epistemology, Synthese Library (Vol. 351, pp 103–126).Google Scholar
- Curry, H. B., & Feys, R. (1958). Combinatory logic, volume I. North-Holland. Second printing 1968.Google Scholar
- Floridi, L. (2009). Philosophical conceptions of information. In G. Sommaruga (Ed.), Formal theories of information, volume 5363 of lectures notes in computer science (pp. 13–53). SpringerGoogle Scholar
- Howard, W. (1980). The formulae-as-types notion of construction. In: J. Seldin & J. Hindley (Eds.), To H. B. Curry: Essays on combinatory logic, lambda calculus and formalism (pp. 479–490). London :Academic Press.Google Scholar
- Jespersen, B. (2012). A new logic of technical malfunction. Studia Logica. doi: 10.1007/s11225-012-9397-8.
- Mayo, D. G. (2010). Learning from error severe testing, and the growth of theoretical knowledge. In: D. Mayo & A. Spanos (Eds.), Error and inference. Cambridge: Cambridge University Press.Google Scholar
- Michaelson, G. (1989). Functional programming through λ-calculus. New York: Dover.Google Scholar
- Peirce, C. S. (1878). Illustrations of the logic of science vi: Deduction, induction, and hypothesis. Popular Science Monthly, 13 Google Scholar
- Popper, K. R. (1963). Conjectures and refutations. London: Routledge & Keagan.Google Scholar
- Primiero, G. (2012). Offline and online data: On upgrading functional information to knowledge. Philosophical Studies. doi: 10.1007/s11098-012-9860-4
- Sørensen, M. H., & Urzyczyn, P. (2006). Lectures on the Curry–Howard isomorphism volume 149 of studies in logic and the foundations of mathematics. Amsterdam: Elsevier.Google Scholar
- Sundholm, B. G. (2012). Error. Topoi, 31(1), 87–92.Google Scholar
- Williamson, T. (1994). Vagueness. London: Routledge.Google Scholar