IDEAS home Printed from https://ideas.repec.org/a/cys/ecocyb/v50y2017i1p263-280.html
   My bibliography  Save this article

Qualitative Evaluation of Knowledge Based Model of Project Time-Cost as Decision Making Support

Author

Listed:
  • Radek DOSKOČIL
  • Karel DOUBRAVSKÝ

    (Department of Informatics Faculty of Business and Management Brno University of Technology)

Abstract

An integral part of effective project management is the knowledge management. Knowledge analyses are based on deep information(equations)or shallow information (verbal description). The paper presents a quantitative evaluation of qualitative knowledge model. Qualitative quantifications are based on three words (increasing, constant, decreasing).Any qualitative model M has a discrete set of qualitative scenarios S. An algorithm is used to generate all possible transitions O among the set of S. A transitional graph T has as nodes scenarios S and as arcs transitions O. Any behaviour of the model M can be described by a sequence of scenarios.A tree R, which is sub-graph of the T graph and can be taken for any qualitative forecasting, is used to evaluate probabilities of different branches of the tree. The presented evaluation provides new knowledge analysis and its main advantage is that no numerical values are needed. The qualitative model shows a seven-dimensional project management problem.

Suggested Citation

  • Radek DOSKOČIL & Karel DOUBRAVSKÝ, 2017. "Qualitative Evaluation of Knowledge Based Model of Project Time-Cost as Decision Making Support," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 51(1), pages 263-280.
  • Handle: RePEc:cys:ecocyb:v:50:y:2017:i:1:p:263-280
    as

    Download full text from publisher

    File URL: ftp://www.eadr.ro/RePEc/cys/ecocyb_pdf/ecocyb1_2017p263-280.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yossi Bukchin & Shai Rozenes, 2011. "A multi-objective approach for decision making during the project life cycle," International Journal of Project Organisation and Management, Inderscience Enterprises Ltd, vol. 3(2), pages 184-203.
    2. Karel Doubravsky & Mirko Dohnal, 2015. "Reconciliation of Decision-Making Heuristics Based on Decision Trees Topologies and Incomplete Fuzzy Probabilities Sets," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-18, July.
    3. Omar Bentahar, 2015. "A sequential and concurrent mixed method research in project management," Post-Print hal-01697550, HAL.
    4. Arora Siddharth & Little Max A. & McSharry Patrick E., 2013. "Nonlinear and nonparametric modeling approaches for probabilistic forecasting of the US gross national product," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(4), pages 395-420, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Balcilar, Mehmet & Gupta, Rangan & van Eyden, Reneé & Thompson, Kirsten & Majumdar, Anandamayee, 2018. "Comparing the forecasting ability of financial conditions indices: The case of South Africa," The Quarterly Review of Economics and Finance, Elsevier, vol. 69(C), pages 245-259.
    2. Xiuying Ma & Yongjing Wang & Haiyan Song & Han Liu, 2020. "Time-varying mechanisms between foreign direct investment and tourism development under the new normal in China," Tourism Economics, , vol. 26(2), pages 324-343, March.
    3. Lan Bai & Xiafei Li & Yu Wei & Guiwu Wei, 2022. "Does crude oil futures price really help to predict spot oil price? New evidence from density forecasting," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3694-3712, July.
    4. Karel Doubravský & Alena Kocmanová & Mirko Dohnal, 2018. "Analysis of Sustainability Decision Trees Generated by Qualitative Models Based on Equationless Heuristics," Sustainability, MDPI, vol. 10(7), pages 1-18, July.
    5. Grabowski Daniel & Winker Peter & Staszewska-Bystrova Anna, 2017. "Generating prediction bands for path forecasts from SETAR models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(5), pages 1-18, December.
    6. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2012. "Was the Recent Downturn in US GDP Predictable?," Working Papers 1210, University of Nevada, Las Vegas , Department of Economics.
    7. Berg Tim Oliver, 2017. "Forecast accuracy of a BVAR under alternative specifications of the zero lower bound," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(2), pages 1-29, April.
    8. Giovanni Ballarin & Petros Dellaportas & Lyudmila Grigoryeva & Marcel Hirt & Sophie van Huellen & Juan-Pablo Ortega, 2022. "Reservoir Computing for Macroeconomic Forecasting with Mixed Frequency Data," Papers 2211.00363, arXiv.org, revised Jan 2024.
    9. Dohnal, Mirko, 2016. "Complex biofuels related scenarios generated by qualitative reasoning under severe information shortages: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 676-684.
    10. Dohnal, Mirko & Doubravsky, Karel, 2016. "Equationless and equation-based trend models of prohibitively complex technological and related forecasts," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 297-304.
    11. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2015. "Was the recent downturn in US real GDP predictable?," Applied Economics, Taylor & Francis Journals, vol. 47(28), pages 2985-3007, June.
    12. Shaun P Vahey & Elizabeth C Wakerly, 2013. "Moving towards probability forecasting," BIS Papers chapters, in: Bank for International Settlements (ed.), Globalisation and inflation dynamics in Asia and the Pacific, volume 70, pages 3-8, Bank for International Settlements.

    More about this item

    Keywords

    equation less model; quantitative evaluation; qualitative tree; decision support; knowledge management; project management.;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • E60 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cys:ecocyb:v:50:y:2017:i:1:p:263-280. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Corina Saman (email available below). General contact details of provider: https://edirc.repec.org/data/feasero.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.