Abstract
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.
Similar content being viewed by others
References
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.
Bar-Hillel, Y. (1964), Language and Information, Reading, MA, London: Addison-Wesley.
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.
Barwise, J. and Perry J. (1983), Situations and Attitudes, Cambridge, MA: MIT Press.
Barwise, J. and Seligman J. (1997), Information Flow: The Logic of Distributed Systems, Cambridge: Cambridge University Press.
Devlin, K. (1991), Logic and Information, Cambridge: Cambridge University Press.
Dretske, F. (1981), Knowledge and the Flow of Information, Cambridge, MA: MIT Press, rep. Stanford: CSLI, 1999.
Floridi, L. (1999), Philosophy and Computing — An Introduction, London, New York: Routledge.
Floridi, L. (ed.) (2003), The Blackwell Guide to the Philosophy of Computing and Information, Oxford, New York: Blackwell.
Floridi, L. (forthcoming, a), 'Is Information Meaningful Data?', forthcoming in Philosophy and Phenomenological Research, preprint available at http://www.wolfson.ox.ac.uk/ floridi/papers.htm.
Floridi, L. (forthcoming b), 'Information, Semantic Conceptions of', forthcoming in Stanford Encyclopedia of Philosophy.
Graham, G. (1999), The Internet:// A Philosophical Inquiry, London, New York: Routledge.
Grice, P. (1989), Studies in the Way of Words, Cambridge, MA: Harvard University Press.
Hanson, W. H. (1980), 'First-Degree Entailments and Information', Notre Dame Journal of Formal Logic 21(4), pp. 659–671.
Heck, A. and Murtagh, F. (eds.) (1993), Intelligent Information Retrieval: The Case of Astronomy and Related Space Sciences, Dordrecht, London: Kluwer Academic Publishers.
Hintikka, J. and Suppes, P. (eds.) (1970), Information and Inference, Dordrecht: Reidel.
Hockett, C. F. (1952), 'An Approach to the Quantification of Semantic Noise', Philosophy of Science 19(4), pp. 257–260.
Jamison, D. (1970), 'Bayesian Information Usage', in J. Hintikka and P. Suppes, eds., Information and Inference, Dordrecht: Reidel, pp. 28–57.
Jeffrey, R. C. (1990), The Logic of Decision, 2nd ed., Chicago: University of Chicago Press.
Jones, C. B. (1986), Systematic Software Development Using VDM, London: Prentice-Hall International.
Kemeny, J. (1953), 'A Logical Measure Function', Journal of Symbolic Logic 18, pp. 289–308.
Levi, I. (1967), 'Information and Inference', Synthese 17, pp. 369–391.
Lozinskii, E. (1994), 'Information and Evidence in Logic Systems', Journal of Experimental and Theoretical Artificial Intelligence 6, pp. 163–193.
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.
Popper, K. R. (1935), Logik der Forschung, Vienna: Springer, trans. The Logic of Scientific Discovery, London: Hutchinson (1959).
Popper, K. R. (1962), Conjectures and Refutations, London: Routledge.
Reza, F. M. (1994), An Introduction to Information Theory, New York: Dover (orig. 1961).
Shannon, C. E. (1948), 'A Mathematical Theory of Communication', Bell System Tech. J. 27, pp. 379–423, 623–656.
Shannon, C. E. (1993), Collected Papers, Los Alamos, CA: IEEE Computer Society Press.
Shannon, C. E. and Weaver, W. (1949), The Mathematical Theory of Communication, Urbana, IL.: University of Illinois Press.
Smokler, H. (1966), 'Informational Content: A Problem of Definition', The Journal of Philosophy 63(8), pp. 201–211.
Sneed, D. J. (1967), 'Entropy, Information and Decision', Synthese 17, pp. 392–407.
Sorensen, R. (1988), Blindspots, Oxford: Clarendon Press.
Szaniawski, K. (1967), 'The Value of Perfect Information', Synthese 17, pp. 408–424, now in Szaniawski (1998).
Szaniawski, K. (1974), 'Two Concepts of Information', Theory and Decision 5, pp. 9–21, now in Szaniawski (1998).
Szaniawski, K. (1984), 'On Defining Information', now in Szaniawski (1998).
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.
Taylor, J. R. (1997), An Introduction to Error Analysis: The Study of Uncertainty in Physical Measurements, 2nd ed., Mill Valley, CA: University Science Books.
Taylor, K. A. (1987), 'Belief, Information and Semantic Content: A Naturalist's Lament', Synthese 71, pp. 97–124.
Van der Lubbe, J. C. A. (1997), Information Theory, Cambridge: Cambridge U.P. (orig. 1988).
Williamson, T. (1994), Vagueness, London: Routledge.
Winder, R. L., Probert, S. K. and Beeson, I. A. (1997), Philosophical Aspects of Information Systems, London: Taylor and Francis.
Woodcock, J. C. P. and Davies, J. (1996), Using Z: Specification, Refinement and Proof, London: Prentice-Hall International.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Floridi, L. Outline of a Theory of Strongly Semantic Information. Minds and Machines 14, 197–221 (2004). https://doi.org/10.1023/B:MIND.0000021684.50925.c9
Issue Date:
DOI: https://doi.org/10.1023/B:MIND.0000021684.50925.c9