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Towards New Probabilistic Assumptions in Business Intelligence

Andrew Schumann
  • University of Information Technology and Management in Rzeszow, Poland
  • :
/ Andrzej Szelc
  • University of Information Technology and Management in Rzeszow, Poland
  • :
Published Online: 2015-01-31 | DOI: https://doi.org/10.1515/sh-2015-0003


One of the main assumptions of mathematical tools in science is represented by the idea of measurability and additivity of reality. For discovering the physical universe additive measures such as mass, force, energy, temperature, etc. are used. Economics and conventional business intelligence try to continue this empiricist tradition and in statistical and econometric tools they appeal only to the measurable aspects of reality. However, a lot of important variables of economic systems cannot be observable and additive in principle. These variables can be called symbolic values or symbolic meanings and studied within symbolic interactionism, the theory developed since George Herbert Mead and Herbert Blumer. In statistical and econometric tools of business intelligence we accept only phenomena with causal connections measured by additive measures. In the paper we show that in the social world we deal with symbolic interactions which can be studied by non-additive labels (symbolic meanings or symbolic values). For accepting the variety of such phenomena we should avoid additivity of basic labels and construct a new probabilistic method in business intelligence based on non-Archimedean probabilities.

Keywords:: business intelligence; additivity; measurability; non-additive measures; symbolic interactionism; symbolic value; non-Archimedean probabilities


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Published Online: 2015-01-31

Citation Information: Studia Humana. Volume 3, Issue 4, Pages 11–21, ISSN (Online) 2299-0518, DOI: https://doi.org/10.1515/sh-2015-0003, January 2015

© 2015 Andrew Schumann and Andrzej Szelc. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. (CC BY-NC-ND 3.0)

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