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What Can We Know of Computational Information? Measuring, Quantity, and Quality at Work in Programmable Artifacts.

This paper explores the problem of knowledge in computational informational organisms, i.e. organisms that include a computing machinery at the artifact side. Although information can be understood in many ways, from the second half of the past century information is getting more and more digitised,...

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Published in:Topoi: An International Review of Philosophy Vol. 35; no. 1; pp. 203 - 213
Main Authors: Gobbo, Federico, Benini, Marco
Format: Article
Published: Springer Nature Apr2016
Subjects:
Information theory
Von Neumann algebras
Abstract thought
Logic
Computer systems
Online Access:View this record in EBSCOhost
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          Gobbo, Federico
          Benini, Marco
        affil:
          University of Amsterdam, Spuistraat 210 1012VT Amsterdam The Netherlands
          University of Insubria, via Mazzini 5 21100 Varese Italy
      su:
        Information theory
        Von Neumann algebras
        Abstract thought
        Logic
        Computer systems
      sug:
        subj:
          Information theory
          Von Neumann algebras
          Abstract thought
          Logic
          Computer systems
      keyword:
        Computational complexity
        Computational information
        Information measuring
        Information quality
        Informational organism
        Quantitative measuring
      ab: This paper explores the problem of knowledge in computational informational organisms, i.e. organisms that include a computing machinery at the artifact side. Although information can be understood in many ways, from the second half of the past century information is getting more and more digitised, von Neumann machines becoming dominant. Computational information is a challenge for the act of measuring, as neither purely quantitative nor totally qualitative approaches satisfy the need to explain the interplay among the agents producing and managing computational information. In this paper, Floridi's method of levels of abstraction is applied to the analysis of computational information, with a chief interest in the concepts of information measure, quantification and quality.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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