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Soft Computing In Analytics: Handling Imprecision And Uncertainty In Strategic Decisions

Author

Listed:
  • Carlsson, Christer

    (Institute for Advanced Management Systems Research, Abo Akademi University, 20520 Turku, Finland)

Abstract

Analytics has a similar agenda as management science and is working with the same industrial and business context to support managerial planning, problem solving and decision making. Analytics has a broader scope in terms of methods – besides models and algorithms it also works with statistical methods and advanced technology for handling data, information and knowledge. Soft Computing builds on fuzzy sets theory, fuzzy logic, optimisation, neural nets, evolutionary algorithms, macro heuristics and approximate reasoning. Soft Computing is is focused on the design of intelligent systems to process uncertain, imprecise and incomplete information. Soft Computing methods applied to real-world problems offer more robust, tractable and less costly solutions than those obtained by more conventional mathematical techniques.

Suggested Citation

  • Carlsson, Christer, 2012. "Soft Computing In Analytics: Handling Imprecision And Uncertainty In Strategic Decisions," Fuzzy Economic Review, International Association for Fuzzy-set Management and Economy (SIGEF), vol. 0(2), pages 3-21, November.
    • Handle: RePEc:fzy:fuzeco:v:xvii:y:2012:i:2:p:3-21
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    Citations

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    Cited by:

    1. Christer Carlsson, 2018. "Decision analytics mobilized with digital coaching," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 25(1), pages 3-17, January.

    More about this item

    Keywords

    analytics; management science; soft computing; fuzzy real options;
    All these keywords.

    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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