@article{3989,
  author = {Cezar Ionescu and Patrik Jansson},
  title = {Vulnerability Assessment Data Processing in Different Weather Conditions},
  journal = {Journal of Data Processing},
  year = {2024},
  volume = {14},
  number = {1},
  doi = {https://doi.org/10.6025/jdp/2024/14/1/21-33},
  url = {https://www.dline.info/jdp/fulltext/v14n1/jdpv14n1_3.pdf},
  abstract = {Higher-order property comes into play in some aspects of climatological impact research. For instance, vulnerability measures, which are essential in determining the vulnerability to climate change of different regions and places, must satisfy certain conditions best expressed by quantifying the overall increasing functions of the appropriate type. This type of property is often considered to be “cognitive”, but for the measures used in practice, it is relatively easy to code the property as a dependent type and prove it correct. In scientific programming, it is common to care about the “correctness” of the program up to the “implicit”: for example, the program would perform as expected, for example, if real numbers were used instead of floating point values. These “counterfactuals” are impossible to test, but they are easy to code and prove as types. We show examples (encoded in AGDA) encountered in actual vulnerability assessment.},
}