IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v253y2017i1d10.1007_s10479-016-2318-x.html
   My bibliography  Save this article

The value of additional information in multicriteria decision making choice problems with information imperfections

Author

Listed:
  • Sarah Ben Amor

    (University of Ottawa)

  • Kazimierz Zaras

    (UQAT)

  • Ernesto A. Aguayo

    (Technical University of Madrid (UPM))

Abstract

Processing information is a key ingredient for decision making. In most decision-making cases, information is distributed across various sources that may differ in reliability and accuracy. Various sources and kinds of uncertainty are encountered in the same decision situation. “Information imperfections” is a general term that encompasses all kinds of “deficiencies” (such as uncertainty, imprecision, ambiguity, incompleteness) that may affect the quality of information at hand. In discrete multicriteria decision making, where several alternatives are assessed according to heterogeneous and conflicting criteria, information used to assess such alternatives can also be imperfect. It is rather natural, in such a context, to seek additional information to reduce these imperfections. This paper aims at extending the Bayesian model for assessing the value of additional information to multicriteria decision analysis in a context of imperfect information. A unified procedure for processing additional information has been proposed in a previous work. It leads to prior and posterior global preference relational systems. It will be extended here to include pre-posterior analysis where concepts such as the expected value of perfect information and the expected value of imperfect information are adapted to multicriteria decision making choice problems.

Suggested Citation

  • Sarah Ben Amor & Kazimierz Zaras & Ernesto A. Aguayo, 2017. "The value of additional information in multicriteria decision making choice problems with information imperfections," Annals of Operations Research, Springer, vol. 253(1), pages 61-76, June.
    • Handle: RePEc:spr:annopr:v:253:y:2017:i:1:d:10.1007_s10479-016-2318-x
    DOI: 10.1007/s10479-016-2318-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-016-2318-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-016-2318-x?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Durbach, Ian N. & Stewart, Theodor J., 2012. "Modeling uncertainty in multi-criteria decision analysis," European Journal of Operational Research, Elsevier, vol. 223(1), pages 1-14.
    2. Munda, G. & Nijkamp, P. & Rietveld, P., 1995. "Qualitative multicriteria methods for fuzzy evaluation problems: An illustration of economic-ecological evaluation," European Journal of Operational Research, Elsevier, vol. 82(1), pages 79-97, April.
    3. Clemon, Robert T & Winkler, Robert L, 1986. "Combining Economic Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 39-46, January.
    4. Samson, D. & Wirth, A. & Rickard, J., 1989. "The value of information from multiple sources of uncertainty in decision analysis," European Journal of Operational Research, Elsevier, vol. 39(3), pages 254-260, April.
    5. Zaras, Kazimierz, 2004. "Rough approximation of a preference relation by a multi-attribute dominance for deterministic, stochastic and fuzzy decision problems," European Journal of Operational Research, Elsevier, vol. 159(1), pages 196-206, November.
    6. James S. Dyer & Rakesh K. Sarin, 1979. "Measurable Multiattribute Value Functions," Operations Research, INFORMS, vol. 27(4), pages 810-822, August.
    7. Amor, Sarah Ben & Jabeur, Khaled & Martel, Jean-Marc, 2007. "Multiple criteria aggregation procedure for mixed evaluations," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1506-1515, September.
    8. David M. Pennock & Michael P. Wellman, 2005. "Graphical Models for Groups: Belief Aggregation and Risk Sharing," Decision Analysis, INFORMS, vol. 2(3), pages 148-164, September.
    9. Dennis V. Lindley, 1986. "The Reconciliation of Decision Analyses," Operations Research, INFORMS, vol. 34(2), pages 289-295, April.
    10. Azondekon, Sebastien H. & Martel, Jean-Marc, 1999. ""Value" of additional information in multicriterion analysis under uncertainty," European Journal of Operational Research, Elsevier, vol. 117(1), pages 45-62, August.
    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. Gais Alhadi & Imed Kacem & Pierre Laroche & Izzeldin M. Osman, 2020. "Approximation algorithms for minimizing the maximum lateness and makespan on parallel machines," Annals of Operations Research, Springer, vol. 285(1), pages 369-395, February.

    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. Durbach, Ian N., 2014. "Outranking under uncertainty using scenarios," European Journal of Operational Research, Elsevier, vol. 232(1), pages 98-108.
    2. Durbach, Ian N. & Stewart, Theodor J., 2012. "Modeling uncertainty in multi-criteria decision analysis," European Journal of Operational Research, Elsevier, vol. 223(1), pages 1-14.
    3. Robert L. Winkler & Robert T. Clemen, 2004. "Multiple Experts vs. Multiple Methods: Combining Correlation Assessments," Decision Analysis, INFORMS, vol. 1(3), pages 167-176, September.
    4. Scott L. Rosen & Christopher P. Saunders & Samar K Guharay, 2015. "A Structured Approach for Rapidly Mapping Multilevel System Measures via Simulation Metamodeling," Systems Engineering, John Wiley & Sons, vol. 18(1), pages 87-101, January.
    5. Gawlik, Remigiusz, 2016. "Encompassing the Work-Life Balance into Early Career Decision-Making of Future Employees Through the Analytic Hierarchy Process," MPRA Paper 80260, University Library of Munich, Germany.
    6. Bana e Costa, Carlos A. & Oliveira, Mónica D. & Rodrigues, Teresa C. & Vieira, Ana C.L., 2023. "Desirability–doability group judgment framework for the collaborative multicriteria evaluation of public policies," LSE Research Online Documents on Economics 118192, London School of Economics and Political Science, LSE Library.
    7. Scholten, Lisa & Schuwirth, Nele & Reichert, Peter & Lienert, Judit, 2015. "Tackling uncertainty in multi-criteria decision analysis – An application to water supply infrastructure planning," European Journal of Operational Research, Elsevier, vol. 242(1), pages 243-260.
    8. Jiang, Yanping & Liang, Xia & Liang, Haiming & Yang, Ningman, 2018. "Multiple criteria decision making with interval stochastic variables: A method based on interval stochastic dominance," European Journal of Operational Research, Elsevier, vol. 271(2), pages 632-643.
    9. Shuang Liu & Kirsten Maclean & Cathy Robinson, 2019. "A cost-effective framework to prioritise stakeholder participation options," EURO Journal on Decision Processes, Springer;EURO - The Association of European Operational Research Societies, vol. 7(3), pages 221-241, November.
    10. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    11. Ezbakhe, Fatine & Pérez-Foguet, Agustí, 2021. "Decision analysis for sustainable development: The case of renewable energy planning under uncertainty," European Journal of Operational Research, Elsevier, vol. 291(2), pages 601-613.
    12. Mark Steyvers & Thomas S. Wallsten & Edgar C. Merkle & Brandon M. Turner, 2014. "Evaluating Probabilistic Forecasts with Bayesian Signal Detection Models," Risk Analysis, John Wiley & Sons, vol. 34(3), pages 435-452, March.
    13. Aniruddh Nain & Deepika Jain & Shivam Gupta & Ashwani Kumar, 2023. "Improving First Responders' Effectiveness in Post-Disaster Scenarios Through a Hybrid Framework for Damage Assessment and Prioritization," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(3), pages 409-437, September.
    14. McKenna, Claire & Chalabi, Zaid & Epstein, David & Claxton, Karl, 2010. "Budgetary policies and available actions: A generalisation of decision rules for allocation and research decisions," Journal of Health Economics, Elsevier, vol. 29(1), pages 170-181, January.
    15. Marcello Basili & Alain Chateauneuf, 2013. "Aggregation of coherent experts opinion: a tractable extreme-outcomes consistent rule," Department of Economics University of Siena 676, Department of Economics, University of Siena.
    16. Samuels, Jon D. & Sekkel, Rodrigo M., 2017. "Model Confidence Sets and forecast combination," International Journal of Forecasting, Elsevier, vol. 33(1), pages 48-60.
    17. Janne Gustafsson, 2020. "Valuation of Research and Development Projects Using Buying and Selling Prices: Generalized Definitions," Decision Analysis, INFORMS, vol. 17(2), pages 154-168, June.
    18. Peter Reichert & Klemens Niederberger & Peter Rey & Urs Helg & Susanne Haertel-Borer, 2019. "The need for unconventional value aggregation techniques: experiences from eliciting stakeholder preferences in environmental management," EURO Journal on Decision Processes, Springer;EURO - The Association of European Operational Research Societies, vol. 7(3), pages 197-219, November.
    19. Wynn C. Stirling & Teppo Felin, 2016. "Satisficing, preferences, and social interaction: a new perspective," Theory and Decision, Springer, vol. 81(2), pages 279-308, August.
    20. Christophe Labreuche & Michel Grabisch, 2016. "A comparison of the GAI model and the Choquet integral with respect to a k-ary capacity," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01277825, HAL.

    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:spr:annopr:v:253:y:2017:i:1:d:10.1007_s10479-016-2318-x. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.