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Eventology versus contemporary theories of uncertainty

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  • Vorobyev, Oleg

Abstract

The development of probability theory together with the Bayesian approach in the three last centuries is caused by two factors: the variability of the physical phenomena and partial ignorance about them. As now it is standard to believe [Dubois, 2007], the nature of these key factors is so various, that their descriptions are required special uncertainty theories, which differ from the probability theory and the Bayesian credo, and provide a better account of the various facets of uncertainty by putting together probabilistic and set-valued representations of information to catch a distinction between variability and ignorance. Eventology [Vorobyev, 2007], a new direction of probability theory and philosophy, offers the original event approach to the description of variability and ignorance, entering an agent, together with his/her beliefs, directly in the frameworks of scientific research in the form of eventological distribution of his/her own events. This allows eventology, by putting together probabilistic and set-event representation of information and philosophical concept of event as co-being [Bakhtin, 1920], to provide a unified strong account of various aspects of uncertainty catching distinction between variability and ignorance and opening an opportunity to define imprecise probability as a probability of imprecise event in the mathematical frameworks of Kolmogorov's probability theory [Kolmogorov, 1933].

Suggested Citation

  • Vorobyev, Oleg, 2009. "Eventology versus contemporary theories of uncertainty," MPRA Paper 13961, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:13961
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    File URL: https://mpra.ub.uni-muenchen.de/13961/1/MPRA_paper_13961.pdf
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    References listed on IDEAS

    as
    1. Vorobyev, Oleg Yu., 2005. "Eventology of random-fuzzy events," MPRA Paper 15870, University Library of Munich, Germany.
    2. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    3. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    4. Vorobyev, Oleg Yu. & Vorobyev, Alexey O., 2003. "On the New Notion of the Set-Expectation for a Random Set of Events," MPRA Paper 17901, University Library of Munich, Germany, revised 27 Apr 2003.
    5. Karni, Edi, 1996. "Probabilities and Beliefs," Journal of Risk and Uncertainty, Springer, vol. 13(3), pages 249-262, November.
    6. F J Anscombe & R J Aumann, 2000. "A Definition of Subjective Probability," Levine's Working Paper Archive 7591, David K. Levine.
    7. Zadeh, Lotfi A., 2006. "Generalized theory of uncertainty (GTU)--principal concepts and ideas," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 15-46, November.
    8. Couso, Ines & Moral, Serafin & Walley, Peter, 2000. "A survey of concepts of independence for imprecise probabilities," Risk, Decision and Policy, Cambridge University Press, vol. 5(2), pages 165-181, June.
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    Cited by:

    1. Vorobyev, Oleg Yu., 2016. "Postulating the theory of experience and chance as a theory of co~events (co~beings)," MPRA Paper 81892, University Library of Munich, Germany.

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    More about this item

    Keywords

    uncertainty; probability; event; co-being; eventology; imprecise event;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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