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
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