IDEAS home Printed from https://ideas.repec.org/a/wly/isacfm/v15y2007i1-2p73-84.html
   My bibliography  Save this article

Knowledge representation of rules: a note

Author

Listed:
  • Daniel E. O'Leary

Abstract

This paper examines knowledge acquisition and representation of ‘if–then’ rules and the linkage between the two. It provides an empirical analysis of the relationship between using different knowledge representations that are logically equivalent. A priori, logically equivalent forms would be expected to result in gathering the same knowledge. However, this paper does not substantiate that conclusion. Instead, different logically equivalent representations used for knowledge elicitation can result in different orderings of the probability of events. As a result, elicitation and representation must be tightly linked. In addition, this paper finds that groups of non‐professionals can generate better orderings than individual professionals and non‐professionals. Copyright © 2007 John Wiley & Sons, Ltd.

Suggested Citation

  • Daniel E. O'Leary, 2007. "Knowledge representation of rules: a note," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 15(1‐2), pages 73-84, January.
    • Handle: RePEc:wly:isacfm:v:15:y:2007:i:1-2:p:73-84
    DOI: 10.1002/isaf.286
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/isaf.286
    Download Restriction: no
    File URL: https://libkey.io/10.1002/isaf.286?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Wright, George, 2002. "Game theory, game theorists, university students, role-playing and forecasting ability," International Journal of Forecasting, Elsevier, vol. 18(3), pages 383-387.
    2. Ringuest, Jeffrey L. & Tang, Kwei, 1987. "Simple rules for combining forecasts: Some empirical results," Socio-Economic Planning Sciences, Elsevier, vol. 21(4), pages 239-243.
    3. Rowe, Gene & Wright, George, 1999. "The Delphi technique as a forecasting tool: issues and analysis," International Journal of Forecasting, Elsevier, vol. 15(4), pages 353-375, October.
    4. Rowe, Gene & Wright, George, 1996. "The impact of task characteristics on the performance of structured group forecasting techniques," International Journal of Forecasting, Elsevier, vol. 12(1), pages 73-89, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sutton, Steve G. & Holt, Matthew & Arnold, Vicky, 2016. "“The reports of my death are greatly exaggerated”—Artificial intelligence research in accounting," International Journal of Accounting Information Systems, Elsevier, vol. 22(C), pages 60-73.

    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. Makkonen, Marika & Hujala, Teppo & Uusivuori, Jussi, 2016. "Policy experts' propensity to change their opinion along Delphi rounds," Technological Forecasting and Social Change, Elsevier, vol. 109(C), pages 61-68.
    2. Kawamoto, Carlos Tadao & Wright, James Terence Coulter & Spers, Renata Giovinazzo & de Carvalho, Daniel Estima, 2019. "Can we make use of perception of questions' easiness in Delphi-like studies? Some results from an experiment with an alternative feedback," Technological Forecasting and Social Change, Elsevier, vol. 140(C), pages 296-305.
    3. Meijering, Jurian Vincent & Tobi, Hilde, 2018. "The effects of feeding back experts’ own initial ratings in Delphi studies: A randomized trial," International Journal of Forecasting, Elsevier, vol. 34(2), pages 216-224.
    4. Barrios, Maite & Guilera, Georgina & Nuño, Laura & Gómez-Benito, Juana, 2021. "Consensus in the delphi method: What makes a decision change?," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    5. Gebhardt, Maximilian & Spieske, Alexander & Birkel, Hendrik, 2022. "The future of the circular economy and its effect on supply chain dependencies: Empirical evidence from a Delphi study," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    6. Wright, George & Rowe, Gene, 2011. "Group-based judgmental forecasting: An integration of extant knowledge and the development of priorities for a new research agenda," International Journal of Forecasting, Elsevier, vol. 27(1), pages 1-13, January.
    7. Meijering, Jurian V. & Tobi, Hilde, 2016. "The effect of controlled opinion feedback on Delphi features: Mixed messages from a real-world Delphi experiment," Technological Forecasting and Social Change, Elsevier, vol. 103(C), pages 166-173.
    8. Wentholt, M.T.A. & Rowe, G. & König, A. & Marvin, H.J.P. & Frewer, L.J., 2009. "The views of key stakeholders on an evolving food risk governance framework: Results from a Delphi study," Food Policy, Elsevier, vol. 34(6), pages 539-548, December.
    9. Kauko, Karlo & Palmroos, Peter, 2014. "The Delphi method in forecasting financial markets— An experimental study," International Journal of Forecasting, Elsevier, vol. 30(2), pages 313-327.
    10. repec:eee:intfor:v:27:y:2011:i:1:p:1-13 is not listed on IDEAS
    11. Song, Haiyan & Gao, Bastian Z. & Lin, Vera S., 2013. "Combining statistical and judgmental forecasts via a web-based tourism demand forecasting system," International Journal of Forecasting, Elsevier, vol. 29(2), pages 295-310.
    12. Delke, Vincent & Schiele, Holger & Buchholz, Wolfgang & Kelly, Stephen, 2023. "Implementing Industry 4.0 technologies: Future roles in purchasing and supply management," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    13. Winkler, Jens & Moser, Roger, 2016. "Biases in future-oriented Delphi studies: A cognitive perspective," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 63-76.
    14. Ian Belton & Kerstin Cuhls & George Wright, 2022. "A critical evaluation of 42, large‐scale, science and technology foresight Delphi surveys," Futures & Foresight Science, John Wiley & Sons, vol. 4(2), June.
    15. Kopyto, Matthias & Lechler, Sabrina & von der Gracht, Heiko A. & Hartmann, Evi, 2020. "Potentials of blockchain technology in supply chain management: Long-term judgments of an international expert panel," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    16. Bolger, Fergus & Rowe, Gene & Belton, Ian & Crawford, Megan M & Hamlin, Iain & Sissons, Aileen & Taylor Browne Lūka, Courtney & Vasilichi, Alexandrina & Wright, George, 2020. "The Simulated Group Response Paradigm: A new approach to the study of opinion change in Delphi and other structured-group techniques," OSF Preprints 4ufzg, Center for Open Science.
    17. Nur, Suardi & Burton, Bruce & Bergmann, Ariel, 2023. "Evidence on optimal risk allocation models for Indonesian geothermal projects under PPP contracts," Utilities Policy, Elsevier, vol. 81(C).
    18. Perera, H. Niles & Hurley, Jason & Fahimnia, Behnam & Reisi, Mohsen, 2019. "The human factor in supply chain forecasting: A systematic review," European Journal of Operational Research, Elsevier, vol. 274(2), pages 574-600.
    19. Prommer, Lisa & Tiberius, Victor & Kraus, Sascha, 2020. "Exploring the future of startup leadership development," Journal of Business Venturing Insights, Elsevier, vol. 14(C).
    20. Bas Kolen & Matthijs Kok & Ira Helsloot & Bob Maaskant, 2013. "EvacuAid: A Probabilistic Model to Determine the Expected Loss of Life for Different Mass Evacuation Strategies During Flood Threats," Risk Analysis, John Wiley & Sons, vol. 33(7), pages 1312-1333, July.
    21. Meissner, Philip & Brands, Christian & Wulf, Torsten, 2017. "Quantifiying blind spots and weak signals in executive judgment: A structured integration of expert judgment into the scenario development process," International Journal of Forecasting, Elsevier, vol. 33(1), pages 244-253.

    More about this item

    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:wly:isacfm:v:15:y:2007:i:1-2:p:73-84. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/1099-1174/ .

    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.