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Digital Library of the
European Council for Modelling and Simulation |
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Title: |
Causality Versus Predictability
In Neural Network Modeling |
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Authors: |
Hans-Georg
Zimmermann |
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Published in:
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(2015).ECMS 2015 Proceedings edited
by: Valeri M. Mladenov, Grisha Spasov, Petia Georgieva, Galidiya Petrova, European
Council for Modeling and Simulation. doi:10.7148/2015 ISBN:
978-0-9932440-0-1 29th
European Conference on Modelling and Simulation, Albena (Varna), Bulgaria,
May 26th – 29th,
2015 |
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Citation
format: |
Hans-Georg Zimmermann
(2015). Causality Versus Predictability In Neural Network Modeling, ECMS 2015 Proceedings
edited by: Valeri M. Mladenov,
Petia Georgieva, Grisha Spasov, Galidiya Petrova European Council for Modeling and Simulation. doi:10.7148/2015-0005 |
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DOI: |
http://dx.doi.org/10.7148/2015-0005 |
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Abstract: |
In this talk I
want to understand Causality as the intellectual effort to interpret a model behavior
(e.g. a forecast) - it is the answer to the question `Why something happens´.
On the other hand we have the goal to compute best possible predictions - it
is the answer to the question `How something happens´. Unfortunately both
goals do not match. To see this, we have to do a fast review on different
forecasting methods. Neural networks are an appropriate framework for the modeling of high dimensional, nonlinear
models. This may be function approximations or state space models, realized
in form of various recurrent neural networks. Along this line we will improve
our predictability, but loose at least a part of the interpretability. This
is not a drawback of the modeling but a result of reconstructed unobserved
hidden variablesin the more advanced models. It is
our decision to focus on the WHY or on the HOW. |
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