@article{SysłoKwiatkowska2015,
  author    = {Sysło, Maciej M. and Kwiatkowska, Anna Beata},
  title     = {Think logarithmically!},
  series = {KEYCIT 2014 - Key Competencies in Informatics and ICT},
  journal   = {KEYCIT 2014 - Key Competencies in Informatics and ICT},
  number    = {7},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  issn      = {1868-0844},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-82923},
  pages     = {371 -- 380},
  year      = {2015},
  abstract  = {We discuss here a number of algorithmic topics which we use in our teaching and in learning of mathematics and informatics to illustrate and document the power of logarithm in designing very efficient algorithms and computations - logarithmic thinking is one of the most important key competencies for solving real world practical problems. We demonstrate also how to introduce logarithm independently of mathematical formalism using a conceptual model for reducing a problem size by at least half. It is quite surprising that the idea, which leads to logarithm, is present in Euclid's algorithm described almost 2000 years before John Napier invented logarithm.},
  language  = {en}
}
@article{ToppingAlroeFarrelletal.2015,
  author    = {Topping, Christopher J. and Alroe, Hugo Fjelsted and Farrell, Katharine N. and Grimm, Volker},
  title     = {Per Aspera ad Astra: Through Complex Population Modeling to Predictive Theory},
  series = {The American naturalist : a bi-monthly journal devoted to the advancement and correlation of the biological sciences},
  volume    = {186},
  journal   = {The American naturalist : a bi-monthly journal devoted to the advancement and correlation of the biological sciences},
  number    = {5},
  publisher = {Univ. of Chicago Press},
  address   = {Chicago},
  issn      = {0003-0147},
  doi       = {10.1086/683181},
  pages     = {669 -- 674},
  year      = {2015},
  abstract  = {Population models in ecology are often not good at predictions, even if they are complex and seem to be realistic enough. The reason for this might be that Occam's razor, which is key for minimal models exploring ideas and concepts, has been too uncritically adopted for more realistic models of systems. This can tic models too closely to certain situations, thereby preventing them from predicting the response to new conditions. We therefore advocate a new kind of parsimony to improve the application of Occam's razor. This new parsimony balances two contrasting strategies for avoiding errors in modeling: avoiding inclusion of nonessential factors (false inclusions) and avoiding exclusion of sometimes-important factors (false exclusions). It involves a synthesis of traditional modeling and analysis, used to describe the essentials of mechanistic relationships, with elements that arc included in a model because they have been reported to be or can arguably be assumed to be important under certain conditions. The resulting models should be able to reflect how the internal organization of populations change and thereby generate representations of the novel behavior necessary for complex predictions, including regime shifts.},
  language  = {en}
}