Abstract
In this paper, we proposed a methodology based on group decision making for aggregating and weighting expert knowledge or opinions and identifying the final agreement for a group of experts. We use a case study on causes of Alzheimer’s Disease (AD) for an illustration of the procedural steps in the proposed methodology. We begin by mapping nine most commonly discussed possible causes or risk factors of Alzheimer’s disease that we obtain from the reports into the alternatives. Then, we asked medical professionals or experts to sort the alternatives by means of a fixed set of linguistic categories; each one has associated with a numerical score. We average the scores obtained by each alternative and we consider the associated preference. A method of weighting individual expert opinions is applied in order to arrange the sequence of alternatives in each step of the decision making procedure. We calculate the collective scores after we weight the opinions of the experts with the overall contributions to agreement. The sequential decision procedure is repeated until it determines a final subset of experts where all of them positively contribute to the agreement for group decision making.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Chitta, B., Sarit, K., Jack, M.: Combining Multiple Knowledge Bases. IEEE Transactions on Knowledge and Data Engineering 3(2), 208–220 (1991)
Jesus, P., David, R.I., Fabrizio, R.: On Combining Expertise in Dynamic Linear Models. Statistical and Applied Mathematical Sciences Institute. Technical report#2005-6 (2005)
Druzdzel, M.J., Diez, F.: Criteria for Combining Knowledge from Different Sources in Probabilistic Models. In: 16th Annual Conference on Uncertainty in Artificial Intellgence, Standford, CA, pp. 23–29 (2000)
Druzdzel, M.J., Diez, F.: Combining Knowledge from Different Sources in Caual Probabilistic models. Journal of Machine Learning Research 4, 295–316 (2003)
Koolen, M., Rooij, S.D.: Combining Expert Advice Efficiently. Centrum voor Wiskunde en Informatica (CWI), Amsterdam, Netherlands
Bosch, R.: Characterizations of Voting Rules and Consensus Measures, Ph.D. Dissertation, Tilburg University (2005)
Cook, W.D., Kress, M., Seiford, L.M.: A General Framework for Distance-Based Consensus in Ordinal Ranking Models. European Journal of Operational Research, 392–397 (1996)
Cook, W.D., Seiford, L.M.: Priority Ranking and Concensus Formation. Management Science 24, 1721–1732 (1978)
Cook, W.D., Seiford, L.M.: On the Borda-Kendall Consensus Method for Priority Ranking Problems. Management Science 28, 621–637 (1982)
Jose, L.: Weighting Individual Opinions in Group Decision Making. In: Torra, V., Narukawa, Y., Yoshida, Y. (eds.) MDAI 2007. LNCS (LNAI), vol. 4617, pp. 92–103. Springer, Heidelberg (2007)
Herrera, F., Herrera-Viedma, E.: Linguistic Decision Analysis: Steps for Solving Decision Problems under Linguistic Information. Fuzzy Sets and System 115, 67–82 (2000)
Yager, R.R.: Non-Numeric Multi-Criteria Multi-Person Decision Making. Journal of Group Decision and Negotiation 2, 81–93 (1993)
http://alzheimers.about.com/od/whatisalzheimer1/a/causes.htm
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jongsawat, N., Premchaiswadi, W. (2010). Aggregating and Weighting Expert Knowledge in Group Decision Making. In: Huang, DS., McGinnity, M., Heutte, L., Zhang, XP. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2010. Communications in Computer and Information Science, vol 93. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14831-6_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-14831-6_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14830-9
Online ISBN: 978-3-642-14831-6
eBook Packages: Computer ScienceComputer Science (R0)