%0 Journal Article %1 Pielke2003prediction %A Pielke, Roger A. %A Conant, Richard T. %D 2003 %J Ecology %K best_practice bioenergy_fauna decision-making modelling prediction predictive_knowledge review %N 6 %P 1351--1358 %T Best practices in prediction for decision-making: lessons from the atmospheric and earth sciences %U http://dx.doi.org/10.1890%2F0012-9658%282003%29084%5B1351%3ABPIPFD%5D2.0.CO%3B2 %V 84 %X Predictions that result from scientific research hold --- great appeal for decision-makers who are grappling with complex and controversial environmental issues, by promising to enhance their ability to --- determine a need for and --- determine outcomes of alternative decisions. A problem exists in that decision-makers and scientists in the public and private sectors -- solicit (erbeten, erbitten, erbetteln), -- produce, and -- use such predictions with --- little understanding of their accuracy or utility, and often --- without systematic evaluation or mechanisms? of accountability. In order to contribute to a more effective role for ecological science in support of decision-making, this paper discusses three "best practices" for quantitative ecosystem modeling and prediction gleaned from research on modeling, prediction, and decision-making in the atmospheric and earth sciences. The lessons are distilled from a series of case studies and placed into the specific context of examples from ecological science.Corresponding Editor: J. S. Clark use for prediction in the energy data forecasting a flood quite accurately but being accused: no understanding of uncertainty both forecasters and decision-makers failed to understand the uncertainty associated with the predictions both forecasters and decision-makers failed to understand the implications of uncertainty for decision-making thus, a "good" prediction product can contribute to a "bad" decision three lessons: 1 -- effective use of predictions results from focusing on prediction as one component in theprecess of decision-making sarewitz (2000) identifies three processes associated to the process connecting scientific predictions and policy: a research process, a communication process and a choice process 2 -- don't conflate (zusammenfügen, verschmelzen) prediction for science and prediction for policy 3 -- prediction products are difficult to evaluate and easy to misuse

@article{Pielke2003prediction, abstract = {Predictions that result from scientific research hold --- great appeal for decision-makers who are grappling with complex and controversial environmental issues, by promising to enhance their ability to --- determine a need for and --- determine outcomes of alternative decisions. A problem exists in that decision-makers and scientists in the public and private sectors -- solicit (erbeten, erbitten, erbetteln), -- produce, and -- use such predictions with --- little understanding of their accuracy or utility, and often --- without systematic evaluation or mechanisms? of accountability. In order to contribute to a more effective role for ecological science in support of decision-making, this paper discusses three "best practices" for quantitative ecosystem modeling and prediction gleaned from research on modeling, prediction, and decision-making in the atmospheric and earth sciences. The lessons are distilled from a series of case studies and placed into the specific context of examples from ecological science.Corresponding Editor: J. S. Clark use for prediction in the energy data forecasting a flood quite accurately but being accused: no understanding of uncertainty both forecasters and decision-makers failed to understand the uncertainty associated with the predictions both forecasters and decision-makers failed to understand the implications of uncertainty for decision-making thus, a "good" prediction product can contribute to a "bad" decision three lessons: 1 -- effective use of predictions results from focusing on prediction as one component in theprecess of decision-making sarewitz (2000) identifies three processes associated to the process connecting scientific predictions and policy: a research process, a communication process and a choice process 2 -- don't conflate (zusammenfügen, verschmelzen) prediction for science and prediction for policy 3 -- prediction products are difficult to evaluate and easy to misuse}, added-at = {2008-05-29T10:28:29.000+0200}, author = {Pielke, Roger A. and Conant, Richard T.}, biburl = {https://www.bibsonomy.org/bibtex/272cd903db7f17126906b470785ed2ff5/karinnadrowski}, interhash = {0c8ef2b8111a76af3256527de7464984}, intrahash = {72cd903db7f17126906b470785ed2ff5}, journal = {Ecology}, keywords = {best_practice bioenergy_fauna decision-making modelling prediction predictive_knowledge review}, month = {June}, number = 6, numlit = {00162}, owner = {Karin}, pages = {1351--1358}, timestamp = {2008-08-06T16:50:20.000+0200}, title = {Best practices in prediction for decision-making: lessons from the atmospheric and earth sciences}, url = {http://dx.doi.org/10.1890%2F0012-9658%282003%29084%5B1351%3ABPIPFD%5D2.0.CO%3B2}, volume = 84, year = 2003 }