Download Decision-making under uncertainty

Decision-making under uncertainty:
Is there any other kind?
Naomi Oreskes
History Department and
Science Studies Program
University of California, San Diego
“Decision-making under uncertainty”
implies the existence of an alternative
Presumably: decision-making under
conditions of certainty
No such thing
Statistician George Chacko (1991)
defines decision-making as “the
commitment of resources today for
results tomorrow.”
Because decisions involve
expectations about the future,
they always involve uncertainty
If people talk about “certainty”
they can only be referring to
certainty about what they want the
outcome to be (desires)
Why would anyone even imagine
certainty is possible?
• Decision-making involves premises
(assumptions, beliefs, conditions)
• Logic tells us that if premises of a
conditional statement are correct, then
outcome is known (predictable).
• Common assumption: the premises are
correct. (Least examined aspect)
In environmental decision-making,
the premises typically include
“underlying scientific information”
Examples?
CO2 is a greenhouse gas
Lead is a neurotoxin
Marine biodiversity is declining
We take these things to be true,
and I’ve chosen examples that I
think are true.
But experience proves that
widely accepted premises may
turn out to be incorrect
Premises can always be disputed.
• Marine biodiversity is declining
Ransom Myers and Boris Worm, 2003. "Rapid
worldwide depletion of predatory fish communities."
Nature 423: 280-283.
• Is it really? Or is it just a handful of heavily hunted
fish and mammals species?
• Response: Sloan Foundation Census of Marine
Biodiversity. More information to test premise.
Certainty is a false idol
Why would anyone imagine that scientific
knowledge could be certain?
Erroneous and refuted conception
of science: Positivism
Sea of “positivist expectation”
Certain knowledge based on
1. Observable foundations
2. Verifiable implications
No need to be cruel…
Positivist aspirations were laudable
enough: Science as alternative to
superstition, clericalism, confusion.
Positivists asked important questions
• What aspects of scientific investigation
account for the reliability of the knowledge
produced?
• Can those elements be adopted by others
wishing to increase the robustness of their
own investigations?
• Can these elements be used as a criterion
for judging information?
But the vision failed
• Historically: it fails to account for major
conceptual revisions in science.
• Philosophically: it fails to account for the diversity
of scientific methods and the flexible interplay
between theory and observation.
• Sociologically: it fails to account for the social
dimensions of scientific proof and persuasion.
Verification of knowledge is a social process.
Alternative?
Science as an intellectual
and social consensus of
affiliated experts
Scientific consensus achieved by
1. Consilience of empirical evidence,
achieved by tested methodologies.
2. Coherence between evidential frameworks
and theoretical understandings
3. “Theoretical integrity” (relation to existing
beliefs and commitments)
4. Social organization for establishing and
declaring agreement on all of the above.
Does this process eliminate
uncertainty?
Of course not.
So how do we judge consensus?
How should we response to the
presence of vocal dissenters, and
imperfect data?
Need to reject a second false idol:
“The Kuhnian expectation”
• Kuhn’s famous paradigm concept accounted
for the social dimensions of scientific
consensus and the historical reality of
conceptual change.
• Left us with an incorrect impression of
“normal science”: dissent- & anomaly-free
Science as dissent-free
In Structure (1962) Kuhn wrote:
“What is surprising, and perhaps also
unique in…the fields we call science, is
that…initial divergences…disappear to
a very considerable extent, and then
apparently once and for all.”
What characterizes--even
defines--science is unanimity
Science as anomaly-free
• Kuhn characterized normal science as
(essentially) anomaly-free, with emergence
of an anomaly as the beginning of crisis.
• Most “problems” are viewed as “puzzles.”
When puzzle changes to an “anomaly”-->
crisis --> revolution.
“When…an anomaly comes to
seem more than just another
puzzle of normal science, the
transition to crisis…has begun.”
Left impression that normal
science involves few if any
meaningful uncertainties.
Just filling in details.
A very mistaken view,
as erroneous and damaging as the
positivist view
Generates impossible expectations.
Gives fodder for exploitation of dissent.
Alternative?
Living with uncertainty
If uncertainty, anomalies, and
dissent are normal science, how
can we learn to live with them?
I. A “reasonable expectations” model
II. Taxonomy of uncertainties to help
to identify useful courses of action.
A “reasonable expectations” model
• Consensus  unanimity
• There are always dissenters.
– Better: “outliers”
• There are always anomalies
• Anomalies and outliers can (and
probably will) be exploited.
A taxonomy of uncertainty
(preliminary)
I.
Science not generally accepted--active
scientific debate by scientists
II. Science mostly accepted by scientists,
perhaps some outliers.
III. Science contested by parties outside the
scientific community
Appropriate responses
depend on the situation
I. Area of active scientific debate
Response: More research.
While no guarantee, increased
knowledge base has potential to
increase technical consensus.
II. Science mostly accepted by
scientists, with some outliers
More scientific research is unlikely to
decrease uncertainty.
In fact, it may increase it.
More information  less uncertainty
Existing consensus can be destabilized.
III. Science contested by parties
outside the scientific community
• The issues at stake are almost certainly not
technical (moral, political, religious,
aesthetic).
• More technical research will not resolve
disputes.
• Inclusionary processes essential.
More science is unlikely to help
us make our most important
decisions
References
• George Chacko, 1991, Decision-making
under uncertainty: An Applied Statistics
Approach, New York: Praeger1991, quote
on p. 5
• Kuhn, T. S., 1962. The Structure of
Scientific Revolutions, University of
Chicago Press, quote on p. 17, 82.