Corporate Evidential Decision Making in Performance Prediction Domains
Abstract
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.
Cite
Text
Büchner et al. "Corporate Evidential Decision Making in Performance Prediction Domains." Conference on Uncertainty in Artificial Intelligence, 1997.Markdown
[Büchner et al. "Corporate Evidential Decision Making in Performance Prediction Domains." Conference on Uncertainty in Artificial Intelligence, 1997.](https://mlanthology.org/uai/1997/buchner1997uai-corporate/)BibTeX
@inproceedings{buchner1997uai-corporate,
title = {{Corporate Evidential Decision Making in Performance Prediction Domains}},
author = {Büchner, Alex G. and Dubitzky, Werner and Schuster, Alfons and Lopes, Philippe and O'Donoghue, Peter G. and Hughes, John G. and Bell, David A. and Adamson, Kenneth and White, John A. and Anderson, John M. C. C. and Mulvenna, Maurice D.},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {1997},
pages = {38-45},
url = {https://mlanthology.org/uai/1997/buchner1997uai-corporate/}
}