Minds and Machines

, Volume 14, Issue 2, pp 197–221 | Cite as

Outline of a Theory of Strongly Semantic Information

  • Luciano Floridi


This paper outlines a quantitative theory of strongly semantic information (TSSI) based on truth-values rather than probability distributions. The main hypothesis supported in the paper is that the classic quantitative theory of weakly semantic information (TWSI), based on probability distributions, assumes that truth-values supervene on factual semantic information, yet this principle is too weak and generates a well-known semantic paradox, whereas TSSI, according to which factual semantic information encapsulates truth, can avoid the paradox and is more in line with the standard conception of what generally counts as semantic information. After a brief introduction, section two outlines the semantic paradox implied by TWSI, analysing it in terms of an initial conflict between two requisites of a quantitative theory of semantic information. In section three, three criteria of semantic information equivalence are used to provide a taxonomy of quantitative approaches to semantic information and introduce TSSI. In section four, some further desiderata that should be fulfilled by a quantitative TSSI are explained. From section five to section seven, TSSI is developed on the basis of a calculus of truth-values and semantic discrepancy with respect to a given situation. In section eight, it is shown how TSSI succeeds in solving the paradox. Section nine summarises the main results of the paper and indicates some future developments.

Bar-Hillel Carnap decision theory Dretske error analysis Grice information theory semantic inaccuracy semantic information semantic paradox semantic vacuity situation logic 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Aisbett, J. and Gibbon, G. (1999), 'A Practical Measure of the Information in a Logical Theory', Journal of Experimental and Theoretical Artificial Intelligence 11, pp. 201–218.Google Scholar
  2. Bar-Hillel, Y. (1964), Language and Information, Reading, MA, London: Addison-Wesley.Google Scholar
  3. Bar-Hillel, Y. and Carnap, R. (1953), 'An Outline of a Theory of Semantic Information', rep. in Bar-Hillel (1964), pp. 221–274, page references are to this edition.Google Scholar
  4. Barwise, J. and Perry J. (1983), Situations and Attitudes, Cambridge, MA: MIT Press.Google Scholar
  5. Barwise, J. and Seligman J. (1997), Information Flow: The Logic of Distributed Systems, Cambridge: Cambridge University Press.Google Scholar
  6. Devlin, K. (1991), Logic and Information, Cambridge: Cambridge University Press.Google Scholar
  7. Dretske, F. (1981), Knowledge and the Flow of Information, Cambridge, MA: MIT Press, rep. Stanford: CSLI, 1999.Google Scholar
  8. Floridi, L. (1999), Philosophy and Computing — An Introduction, London, New York: Routledge.Google Scholar
  9. Floridi, L. (ed.) (2003), The Blackwell Guide to the Philosophy of Computing and Information, Oxford, New York: Blackwell.Google Scholar
  10. Floridi, L. (forthcoming, a), 'Is Information Meaningful Data?', forthcoming in Philosophy and Phenomenological Research, preprint available at floridi/papers.htm.Google Scholar
  11. Floridi, L. (forthcoming b), 'Information, Semantic Conceptions of', forthcoming in Stanford Encyclopedia of Philosophy.Google Scholar
  12. Graham, G. (1999), The Internet:// A Philosophical Inquiry, London, New York: Routledge.Google Scholar
  13. Grice, P. (1989), Studies in the Way of Words, Cambridge, MA: Harvard University Press.Google Scholar
  14. Hanson, W. H. (1980), 'First-Degree Entailments and Information', Notre Dame Journal of Formal Logic 21(4), pp. 659–671.Google Scholar
  15. Heck, A. and Murtagh, F. (eds.) (1993), Intelligent Information Retrieval: The Case of Astronomy and Related Space Sciences, Dordrecht, London: Kluwer Academic Publishers.Google Scholar
  16. Hintikka, J. and Suppes, P. (eds.) (1970), Information and Inference, Dordrecht: Reidel.Google Scholar
  17. Hockett, C. F. (1952), 'An Approach to the Quantification of Semantic Noise', Philosophy of Science 19(4), pp. 257–260.Google Scholar
  18. Jamison, D. (1970), 'Bayesian Information Usage', in J. Hintikka and P. Suppes, eds., Information and Inference, Dordrecht: Reidel, pp. 28–57.Google Scholar
  19. Jeffrey, R. C. (1990), The Logic of Decision, 2nd ed., Chicago: University of Chicago Press.Google Scholar
  20. Jones, C. B. (1986), Systematic Software Development Using VDM, London: Prentice-Hall International.Google Scholar
  21. Kemeny, J. (1953), 'A Logical Measure Function', Journal of Symbolic Logic 18, pp. 289–308.Google Scholar
  22. Levi, I. (1967), 'Information and Inference', Synthese 17, pp. 369–391.Google Scholar
  23. Lozinskii, E. (1994), 'Information and Evidence in Logic Systems', Journal of Experimental and Theoretical Artificial Intelligence 6, pp. 163–193.Google Scholar
  24. Mingers, J. (1997), 'The Nature of Information and its Relationship to Meaning', in R. L. Winder, S. K. Probert and I. A. Beeson, Philosophical Aspects of Information Systems, London: Taylor and Francis, pp. 73–84.Google Scholar
  25. Popper, K. R. (1935), Logik der Forschung, Vienna: Springer, trans. The Logic of Scientific Discovery, London: Hutchinson (1959).Google Scholar
  26. Popper, K. R. (1962), Conjectures and Refutations, London: Routledge.Google Scholar
  27. Reza, F. M. (1994), An Introduction to Information Theory, New York: Dover (orig. 1961).Google Scholar
  28. Shannon, C. E. (1948), 'A Mathematical Theory of Communication', Bell System Tech. J. 27, pp. 379–423, 623–656.Google Scholar
  29. Shannon, C. E. (1993), Collected Papers, Los Alamos, CA: IEEE Computer Society Press.Google Scholar
  30. Shannon, C. E. and Weaver, W. (1949), The Mathematical Theory of Communication, Urbana, IL.: University of Illinois Press.Google Scholar
  31. Smokler, H. (1966), 'Informational Content: A Problem of Definition', The Journal of Philosophy 63(8), pp. 201–211.Google Scholar
  32. Sneed, D. J. (1967), 'Entropy, Information and Decision', Synthese 17, pp. 392–407.Google Scholar
  33. Sorensen, R. (1988), Blindspots, Oxford: Clarendon Press.Google Scholar
  34. Szaniawski, K. (1967), 'The Value of Perfect Information', Synthese 17, pp. 408–424, now in Szaniawski (1998).Google Scholar
  35. Szaniawski, K. (1974), 'Two Concepts of Information', Theory and Decision 5, pp. 9–21, now in Szaniawski (1998).Google Scholar
  36. Szaniawski, K. (1984), 'On Defining Information', now in Szaniawski (1998).Google Scholar
  37. Szaniawski, K. (1998), On Science, Inference, Information and Decision Making, Selected Essays in the Philosophy of Science, by A. Chmielewski and J. Wolenski, eds., Dordrecht: Kluwer Academic Publishers.Google Scholar
  38. Taylor, J. R. (1997), An Introduction to Error Analysis: The Study of Uncertainty in Physical Measurements, 2nd ed., Mill Valley, CA: University Science Books.Google Scholar
  39. Taylor, K. A. (1987), 'Belief, Information and Semantic Content: A Naturalist's Lament', Synthese 71, pp. 97–124.Google Scholar
  40. Van der Lubbe, J. C. A. (1997), Information Theory, Cambridge: Cambridge U.P. (orig. 1988).Google Scholar
  41. Williamson, T. (1994), Vagueness, London: Routledge.Google Scholar
  42. Winder, R. L., Probert, S. K. and Beeson, I. A. (1997), Philosophical Aspects of Information Systems, London: Taylor and Francis.Google Scholar
  43. Woodcock, J. C. P. and Davies, J. (1996), Using Z: Specification, Refinement and Proof, London: Prentice-Hall International.Google Scholar

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  1. 1.Dipartimento di Scienze FilosoficheUniversità degli Studi di BariBariItaly
  2. 2.Information Ethics Group, Computing Laboratory and Faculty of PhilosophyOxford UniversityOxfordUK

Personalised recommendations