Testing versus proving in climate impact research
A paper by Cezar Ionescu & Patrik Jansson published in the post-proceedings of TYPES2011.
Keywords: dependently-typed programming, domain-specific languages, climate impact research, formalization
Abstract:
Higher-order properties arise naturally in some areas of climate impact research. For example, "vulnerability measures", crucial in assessing the vulnerability to climate change of various regions and entities, must fulfill certain conditions which are best expressed by quantification over all increasing functions of an appropriate type. This kind of property is notoriously difficult to test. However, for the measures used in practice, it is quite easy to encode the property as a dependent type and prove it correct. Moreover, in scientific programming, one is often interested in correctness "up to implication": the program would work as expected, say, if one would use real numbers instead of floating-point values. Such counterfactuals are impossible to test, but again, they can be easily encoded as types and proven. We show examples of such situations (encoded in Agda), encountered in actual vulnerability assessments.
BibTeX
@InProceedings{ionescujansson:LIPIcs:2013:3899,
author = {Cezar Ionescu and Patrik Jansson},
title = {Testing versus proving in climate impact research},
booktitle = {18th International Workshop on Types for Proofs and
Programs (TYPES 2011)},
pages = {41--54},
series = {Leibniz International Proceedings in Informatics
(LIPIcs)},
ISBN = {978-3-939897-49-1},
ISSN = {1868-8969},
year = 2013,
volume = 19,
editor = {Nils Anders Danielsson and Bengt Nordstr{\"o}m},
publisher = {Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
address = {Dagstuhl, Germany},
URL = {http://drops.dagstuhl.de/opus/volltexte/2013/3899},
annote = {Keywords: dependently-typed programming,
domain-specific languages, climate impact research,
formalization}
}