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Performance of Kullback-Leibler Based Expert Opinion Pooling for Unlikely Events
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SYSNO ASEP 0479432 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Performance of Kullback-Leibler Based Expert Opinion Pooling for Unlikely Events Author(s) Sečkárová, Vladimíra (UTIA-B) RID Number of authors 1 Source Title Proceedings of the NIPS 2016 Workshop on Imperfect Decision Makers. - Cambridge : JMLR, 2017 / Guy Tatiana Valentine ; Kárný Miroslav ; Rios-Insua D. ; Wolpert D. H. Pages s. 41-50 Number of pages 10 s. Publication form Online - E Action NIPS 2016 Workshop on Imperfect Decision Makers Event date 09.12.2016 - 09.12.2016 VEvent location Barcelona Country ES - Spain Event type WRD Language eng - English Country ES - Spain Keywords Opinion Pooling ; Combining Probability Distributions ; Minimum KullbackLeibler Divergence Subject RIV BC - Control Systems Theory OECD category Statistics and probability R&D Projects GA16-09848S GA ČR - Czech Science Foundation (CSF) Institutional support UTIA-B - RVO:67985556 Annotation The aggregation of available information is of great importance in many branches of economics,
social sciences. Often, we can only rely on experts’ opinions, i.e. probabilities assigned to possible events. To deal with opinions in probabilistic form, we focus on the Kullback-Leibler (KL) divergence based pools: linear, logarithmic and KL-pool (Seckarova, 2015). Since occurrence of events is subject to random influences of the real world, it is important to address events assigned lower probabilities (unlikely events). This is done by choosing pooling with a higher entropy than standard linear or logarithmic options, i.e. the KL-pool. We show how well the mentioned pools perform on real data using absolute error, KL-divergence and quadratic reward. In cases favoring events assigned higher probabilities, the KL-pool performs similarly to the linear pool and outperforms the logarithmic pool. When unlikely events occur, the KL-pool outperforms both pools, which makes it a reasonable way of pooling.
Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2018
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