Consequences of a Functional Account of Information
- 84 Downloads
This paper aims to establish several interconnected points. First, a particular interpretation of the mathematical definition of information, known as the causal interpretation, is supported largely by misunderstandings of the engineering context from which it was taken. A better interpretation, which makes the definition and quantification of information relative to the function of its user, is outlined. The first half of the paper is given over to introducing communication theory and its competing interpretations. The second half explores three consequences of the main thesis. First, a popular claim that the quantification of information in a signal is irrelevant for the meaning of that signal is exposed as fallacious. Second, a popular distinction between causal and semantic information is shown to be misleading, and I argue it should be replaced with a related distinction between natural and intentional signs. Finally, I argue that recent empirical work from microbiology and cognitive science drawing on resources of mathematical communication theory is best interpreted by the functional account. Overall, a functional approach is shown to be both theoretically and empirically well-supported.
KeywordsMathematical communication theory Teleosemantics Sender-receiver framework Primitive content Rate-distortion theory
Thanks to Ron Planer, two anonymous referees, and the editors for comments. Thanks also to Manolo Martínez for pointing me in the direction of rate-distortion theory. This research is supported by an Australian Government Research Training Program (RTP) Scholarship and Australian Research Council Laureate Fellowship Grant FL130100141.
- Dennett, D. C. 2017. From bacteria to bach and back: The evolution of minds. Penguin UK.Google Scholar
- Dretske, F. 1981. Knowledge and the flow of information. Cambridge: MIT Press.Google Scholar
- Dretske, F. 1988. Explaining behavior: Reasons in a world of causes. Cambridge: MIT Press.Google Scholar
- Fresco, N., E. Jablonka, and S. Ginsburg. 2018. Functional information: A graded taxonomy of difference makers. Review of Philosophy and Psychology. (this issue).Google Scholar
- Godfrey-Smith, P., and K. Sterelny. 2016. Biological information. In The Stanford encyclopedia of philosophy, ed. Zalta E.N. Summer 2016 edition.Google Scholar
- Harms, W.F. 2004. Primitive content, translation, and the emergence of meaning in animal communication. In Evolution of communication systems: A comparative approach, eds. Oller D.K. and Griebel U., 31–48. Cambridge, MIT Press.Google Scholar
- Hutto, D.D., and E. Myin. 2013. Radicalizing enactivism: Basic minds without content. Cambridge: MIT Press.Google Scholar
- Krebs, J.R., and R. Dawkins. 1984. Animal signals: Mind-reading and manipulation. In Behavioural ecology: An evolutionary approach, 2nd ed., eds. Krebs J.R. and Davies N.B., 380–402. Oxford, Blackwell Scientific.Google Scholar
- Lean, O.M. 2016. Biological information. Bristol: PhD thesis, University of Bristol.Google Scholar
- Lewis, D. 1969. Convention: A philosophical study. Oxford: Blackwell.Google Scholar
- MacKay, D.M. 1969. Information, mechanism and meaning. Cambridge: M.I.T. Press.Google Scholar
- Mann, S. F. 2018. Attribution of information in animal interaction. Biological Theory. https://doi.org/10.1007/s13752-018-0299-5.
- Millikan, R.G. 2013a. Natural information, intentional signs and animal communication. In Animal communication theory, ed. Stegmann U.E., 133–146. New York, Cambridge University Press.Google Scholar
- Millikan, R.G. 2013b. Reply to Rescorla. In Millikan and her critics, eds. Ryder D., Kingsbury J., and Williford K., 103–106. New York, Wiley.Google Scholar
- Noel, A., Y. Fang, N. Yang, D. Makrakis, and A.W. Eckford. 2017. Using Game Theory for Real-Time Behavioral Dynamics in Microscopic Populations with Noisy Signaling.Google Scholar
- Price, H. 2008. Two readings of representationalism.Google Scholar
- Rescorla, M. 2013. Millikan on honeybee navigation and communication. In Millikan and her critics, eds. Ryder D., Kingsbury J., and Williford K., 87–102. Wiley.Google Scholar
- Sarkar, S. 2013. Information in animal communication: When and why does it matter? In Animal communication theory, ed. Stegmann U.E., 189–205. New York, Cambridge University Press.Google Scholar
- Shannon, C.E. 1959. Coding theorems for a discrete source with a fidelity criterion. In Collected Papers, Wiley-IEEE Press, pp 325–350.Google Scholar
- Shannon, C.E., and W. Weaver. 1949. The mathematical theory of communication. Urbana: University of Illinois Press.Google Scholar
- Shea, N., P. Godfrey-Smith, and R. Cao. 2017. Content in simple signalling systems. The British Journal for the Philosophy of Science, axw036. https://doi.org/10.1093/bjps/axw036.
- Stegmann, U.E. (Ed.) 2013. Animal communication theory: Information and influence. Cambridge University Press: New York.Google Scholar