Proceedings of the NIPS 2016 Workshop on Imperfect Decision Makers, PMLR 58:41-50, 2017.
Abstract
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. 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.
@InProceedings{pmlr-v58-seckarova17a,
title = {Performance of {K}ullback-{L}eibler Based Expert Opinion Pooling for Unlikely Events},
author = {Vladimı́ra Sečkárová},
booktitle = {Proceedings of the NIPS 2016 Workshop on Imperfect Decision Makers},
pages = {41--50},
year = {2017},
editor = {Tatiana V. Guy and Miroslav Kárný and David Rios-Insua and David H. Wolpert},
volume = {58},
series = {Proceedings of Machine Learning Research},
address = {Centre de Convencions Internacional de Barcelona, Barcelona, Spain},
month = {09 Dec},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v58/seckarova17a/seckarova17a.pdf},
url = {http://proceedings.mlr.press/v58/seckarova17a.html},
abstract = {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. 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.}
}
%0 Conference Paper
%T Performance of Kullback-Leibler Based Expert Opinion Pooling for Unlikely Events
%A Vladimı́ra Sečkárová
%B Proceedings of the NIPS 2016 Workshop on Imperfect Decision Makers
%C Proceedings of Machine Learning Research
%D 2017
%E Tatiana V. Guy
%E Miroslav Kárný
%E David Rios-Insua
%E David H. Wolpert
%F pmlr-v58-seckarova17a
%I PMLR
%J Proceedings of Machine Learning Research
%P 41--50
%U http://proceedings.mlr.press
%V 58
%W PMLR
%X 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. 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.
Sečkárová, V.. (2017). Performance of Kullback-Leibler Based Expert Opinion Pooling for Unlikely Events. Proceedings of the NIPS 2016 Workshop on Imperfect Decision Makers, in PMLR 58:41-50
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