2.6.2. "Objective" and "Subjective" Probabilities are
not Always Explicitly Distinguished
Some scientists have expressed concern that scientific investigation requires
a long sequence of observational records, replicable trials, or model runs (e.g.,
Monte Carlo simulations) so the results can be specified by a formal statistical
characterization of the frequency and frequency distribution of outcomes being
assessed. In statistical terms, "objective" science means attempting
to verify any hypothesis through a series of experiments and recording the frequency
with which that particular outcome occurs. The idea of a limitless set of identical
and independent trials that is "objectively out there" is a heuristic
device that we use to help us rigorously quantify uncertainty by using frequentist
statistics. Although there may be a large number of trials in some cases, however,
this is not the same as a "limitless" number, and these trials rarely
are truly identical or independent.
Most interesting complex systems cannot possibly be put to every conceivable
test to find the frequency of occurrence of some socially or environmentally
salient event. The popular philosophical view of "objective science"
as a series of "falsifications" breaks down when it confronts systems
that cannot be fully tested. For example, because climate change forecasts are
not empirically determinable (except by "performing the experiment"
on the real Earth—Schneider, 1997), scientists must rely on "surrogate"
experiments, such as computer simulations of the Earth undergoing volcanic eruptions
or paleoclimatic changes. As a result of these surrogate experiments and many
additional tests of the reliability of subcomponents of such models, scientists
attain confidence to varying degrees about the likelihood of various outcomes
(e.g., they might assign with high confidence a low probability to the occurrence
of extreme climate outcomes such as a "runaway greenhouse effect").
These confidence levels are not frequentist statistics but "subjective
probabilities" that represent degrees of belief that are based on a combination
of objective and subjective subcomponents of the total system. Because subjective
characterization of the likelihood of many potentially important climatic events—especially
those that might be characterized by some people as "dangerous"—is
unavoidable, "Bayesian" or "subjective" characterization
of probability will be more appropriate.
Bayesian assessments of probability distributions would lead to the following
interpretation of probability statements: The probability of an event is the
degree of belief that exists among lead authors and reviewers that the event
will occur, given the observations, modeling results, and theory currently available,
all of which contribute to estimation of a "prior" probability for
the occurrence of an outcome. As new data or theories become available, revised
estimates of the subjective probability of the occurrence of that event—socalled
"posterior probability"—can be made, perhaps via the formalism
of Bayes theorem (see, e.g., Edwards, 1992, for a philosophical basis for Bayesian
methods; for applications of Bayesian methods, see, e.g., Howard et al.,
1972; Anderson, 1998; Tol and de Vos, 1998; Malakoff, 1999)
