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Corporate Evidential Decision Making in Performance Prediction Domains

Buchner, AG, Dubitzky, Werner, Schuster, A, Lopes, P, O'Donoghue, P, Hughes, John, Bell, DA, Adamson, Kenneth, White, JA, Anderson, JMCC and Mulvenna, Maurice (1997) Corporate Evidential Decision Making in Performance Prediction Domains. In: Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI1997), Providence, RI, USA. UAI. 8 pp. [Conference contribution]

[img] Text - Accepted Version


Performance prediction or forecasting sporting outcomes involves a great deal of insight into the particular area one is dealing with, and a considerable amount of intuition about the factors that bear on such outcomes and performances. The mathematical Theory of Evidence offers representation formalisms which grant experts a high degree of freedom when expressing their subjective beliefs in the context of decision-making situations like performance prediction. Furthermore, this reasoning framework incorporates a powerful mechanism to systematically pool the decisions made by individual subject matter experts. The idea behind such a combination of knowledge is to improve the competence (quality) of the overall decision-making process. This paper reports on a performance prediction experiment carried out during the European Football Championship in 1996. Relying on the knowledge of four predictors, Evidence Theory was used to forecast the final scores of all 31 matches. The results of this empirical study are very encouraging.

Item Type:Conference contribution (Paper)
Keywords:Evidential decision making, Football
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Faculty of Life and Health Sciences > School of Biomedical Sciences
Faculty of Computing & Engineering > School of Engineering
Faculty of Life and Health Sciences
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
ID Code:36509
Deposited By: Professor Maurice Mulvenna
Deposited On:14 Feb 2017 15:22
Last Modified:17 Oct 2017 16:26

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