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Predictive Seismology

Author(s)
Marzocchi, Warner  
Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione Roma1, Roma, Italia  
Language
English
Obiettivo Specifico
6T. Studi di pericolosità sismica e da maremoto
Status
Published
JCR Journal
JCR Journal
Peer review journal
Yes
Journal
Seismological Research Letters  
Issue/vol(year)
/89 (2018)
Pages (printed)
1998-2000
Date Issued
2018
DOI
10.1785/0220180238
URI
https://www.earth-prints.org/handle/2122/12519
Abstract
In 1989 the American Association for the Advancement of Science (AAAS, 1989) wrote: “The growing ability of scientists to
make accurate predictions about natural phenomena
provides convincing evidence that we really are gaining in our
understanding of how the world works.” This statement addresses longstanding (and sometimes harsh) criticism of science
raised at different historical times by renowned philosophers and
prominent government officials (e.g.,Theocharis and Psimopoulos, 1987), criticism that sparked widespread suspicion about scientists and their discoveries. According to the statement from
AAAS, predictive capability is the distinctive feature that makes
science different from other human enterprises such as arts and
literature. ...
References
American Association for the Advancement of Science (1989). Science for
all Americans: A Project 2061 Report on Literacy Goals in Science,
Mathematics and Technology, Washington, D.C.
Bauer, P., A. Thorpe, and G. Brunet (2015). The quiet revolution of
numerical weather prediction, Nature 525, 47–55.
Cattania, C., M. J. Werner, W. Marzocchi, S. Hainzl, D. Rhoades, M.
Gerstenberger, M. Liukis, W. Savran, A. Christophersen, A. Helmstetter, A. Jimenez, S. Steacy, and T. H. Jordan (2018). The
forecasting skill of physics-based seismicity models during the
2010–2012 Canterbury, New Zealand, earthquake sequence, Seismol. Res. Lett. 89, no. 4, 1238–1250.
Ellis, G., and J. Silk (2014). Scientific method: defending the integrity of
physics, Nature 516, 321–323.
Field, E. H., G. P. Biasi, P. Bird, T. E. Dawson, K. R. Felzer, D. D. Jackson,
K. M. Johnson, T. H. Jordan, C. Madden, A. J. Michael, et al.
(2015). Long-term time-dependent probabilities for the third
Uniform California Earthquake Rupture Forecast (UCERF3),
Bull. Seismol. Soc. Am. 105, no. 2, 511–543.
Jordan, T. H. (2006). Earthquake predictability, brick by brick, Seismol.
Res. Lett. 77, no. 1, 3–6.
Jordan, T. H., W. Marzocchi, A. Michael, and M. Gerstenberger (2014).
Operational earthquake forecasting can enhance earthquake preparedness, Seismol. Res. Lett. 85, no. 5, 955–959.
Kahneman, D. (2011). Thinking Fast and Slow, Farrar, Straus, and
Giroux, New York, New York.
Marzocchi,W., and T. H. Jordan (2014). Testing for ontological errors in
probabilistic forecasting models of natural systems, Proc. Natl. Acad.
Sci. Unit. States Am. 111, no. 33, 11,973–11,978.
Ogata, Y. (1988). Statistical models for earthquake occurrences and
residual analysis for point processes, J. Am. Stat. Assoc. 83, 9–27.
Salt, J. (2008). The seven habits of highly defective simulation projects,
J. Simulation 2, no. 3, 155–161.
Shmueli, G. (2010). To explain or to predict?, Stat. Sci. 25, no. 3,
289–310.
Simon, H. A. (2001). Science seeks parsimony, not simplicity: searching
for pattern in phenomena, in Simplicity, Inference and Modelling:
Keeping it Sophisticatedly Simple, Cambridge Univ. Press,
MR1932928, 32–72.
Theocharis, T., and M. Psimopoulos (1987). Where science has gone
wrong, Nature 329, 595–598.
Zechar, J. D., D. Schorlemmer, M. Liukis, J. Yu, F. Euchner, P. J. Maechling, and T. H. Jordan (2010). The Collaboratory for the Study of
Earthquake Predictability perspective on computational earthquake
science, Concurr. Comput. Pract. Ex. 22, 1836–1847.
Type
article
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