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Information Dynamics

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Every block of stone has a statue inside it and it is the task of the sculptor to discover it.

I saw the angel in the marble and carved until I set him free. Michelangelo (1475–1564)

Abstract

Though we have access to a wealth of information, the main issue is always how to process the available information. How to make sense of all we observe and know. Just like the English alphabet: we know there are 26 letters but unless we put these letters together in a meaningful way, they convey no information. There are infinitely many ways of putting these letters together. Only a small number of those make sense. Only some of those convey exactly what we wish to convey though the message may be interpreted differently by different individuals. That same issue comes up with information: how can we process the information we have? How can we infer and reason under conditions of incomplete observed information? In his seminal book on the philosophy of information, Floridi (2011a) raises a number of open questions. I discuss here one of these questions. That question is how to process information. To do so, I take the more realistic view that information is always limited, incomplete and possibly noisy. I define types of information, relate it to Floridi’s definitions and discuss a basic formulation for processing information under a unified framework. I relate it to some of the basic concepts discussed in the book.

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Notes

  1. For a related discussion on open questions in PI see also Floridi (2004), Crnkovic and Hofkirchner (2011) and a complimentary summary of open questions in the interconnection of philosophy of information and computation (Adriaans 2011) as well as the different chapters in van Benthem and Adriaans (2008).

  2. See also discussion and definitions of “information” in Floridi’s (2011a, b), Adriaans (forthcoming) as well as the different definitions in van Benthem and Adriaans (2008), the nice discussion in Caticha (2012), and the discussion in Golan (2008). For more cross disciplinary discussion of information and information dynamics, within an interdisciplinary perspective, see also the papers in the Info-Metrics proceedings on the philosophy of information (2011) and van Benthem (2011).

  3. For some early discussion of the statistical properties of that approach see for example Golan et al. (1996, 1997) and for some early applications in the natural sciences see Golan and Dose (2001) and Toussaint et al. (2004).

  4. Is it a purely “logical” contradiction or a “statistical” one is another interesting question, but the issue is that we need an inferential approach that will allow us to handle such problems.

  5. Stated simply, entropic methods are designed to process information in the form of expected value constraints. The observed sample averages are not expected values. The question then becomes how one can use information in the form of sample averages in an entropic formalism. One answer is the information-theoretic, GCE framework (developed in previous publications).

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Acknowledgments

I thank Luciano Floridi and Ariel Caticha for many enchanting conversations on the topics discussed here, and for providing me with comments on earlier versions of this paper. I also benefited from comments during recent seminars on the topic. Finally, I thank Patrick Allo for his thoughtful comments, and to the Editor, Tony Beavers.

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  1. Info-Metrics Institute and Department of Economics, American University, Washington, DC, USA

    Amos Golan

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  1. Amos Golan
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Correspondence to Amos Golan.

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Golan, A. Information Dynamics . Minds & Machines 24, 19–36 (2014). https://doi.org/10.1007/s11023-013-9326-2

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