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
%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
}