Ethics and Uncertainty
Wendy Parker, Department of Philosophy, Durham University,
wendy.parker@durham.ac.uk
Nancy Tuana, Department of Philosophy and Rock Ethics Institute, Penn State
ntuana@psu.edu, http://scrimhub.org
With inputs from Klaus Keller, Lauren Mayer, Rob Lempert, Bryan Cwik, and
many others.
All errors and
opinions are
(unless cited)
ours.
ASP Summer Colloquium August 1, 2014
Contains some privileged materials, please do not cite or distribute
1
Ethical Analysis
How should we act?
Responsible
selection of research
topics
Science to Society
impacts
Epistemic Analysis
What can we know?
Coupled Ethical-Epistemic
Analysis
How do we support responsible
action with what we know?
Uncertainty
quantification
Model selection
Values that inform epistemic
decisions
Epistemic decisions that have ethical
import
Ethical values




Justice
Sustainability
Utility
Human Security
Epistemic values




Robustness of evidence
Predictive power
Convergence of evidence
Completeness
2
IPCC Treatment of Uncertainty:
The Role of Values
• IPCC Guidance Note
– The characterizations of
uncertainties is a
deliberative process
• “However, the extent that this
procedure can be considered
reliable…is contingent on
agreements based on value
judgements, for instance on
spatiotemporal resolution,
parameterization of models,
or significance level for
accuracy in empirical studies.”
Adler and Hadorn 2014
3
Example of coupled ethical-epistemic questions:
(1) What do we know about climate sensitivity?
(2) Which decision-criterion is relevant?
(3) How do we estimate climate sensitivity?
1 − Cumulative Frequency [dimensionless]
10
0
Olson et al. (2012)
Libardoni and Forest (2013)
Aldrin et al. (2012)
Urban et. al. (2014)
CMIP High Resolution Models
Missing Tails
10-1
1 in 50
●
10-2
10-3
10
1 in 10,000
-4
0
2
4
6
Climate Sensitivity [K]
8
10
Keller et al (in prep)
4
Epistemic/Evidentiary
Epistemic strengths and weaknesses
Positive and
negative impacts
of ethical features
on future
gathering of
evidence
Model /
Variable
Choice
Ethically desirable
or undesirable
consequences of
epistemic
features
Ethically desirable and undesirable
features
Ethical
5
Valles et al. forthcoming
Wendy: Coupled ethicalepistemic choices in modeling
Nancy: Coupled ethicalepistemic issues and decision
support
Coupled epistemic-ethical choices in modeling
• choices in model construction & evaluation
– which quantities to prioritize for accurate simulation
– choice of uncertain parameter value(s) / parameterizations
• how to estimate and communicate uncertainty
• which methods/studies to pursue
Coupled epistemic-ethical choices in modeling
• choices in model construction & evaluation
– which quantities to prioritize for accurate simulation
– choice of uncertain parameter value(s) / parameterizations
• how to estimate and communicate uncertainty
• which methods/studies to pursue
Ethical values should never influence how we build and evaluate
mathematical models in the study of climate change.
(e.g. which variables we focus on, which values we assign to parameters)
1. True
2. False
3. I’m really not sure
58%
32%
11%
1.
2.
3.
It is sometimes appropriate for ethical values to influence
choices in model construction and evaluation.
What we care about
(e.g. avoiding harm to
humans)
What we can hope to
provide information about,
given today’s science
choice of priority
variables/outcomes
sea level rise
loss of life in floods
economic losses
model improvement
activities, choice of
performance metrics
It is sometimes appropriate for ethical values to influence
choices in model construction and evaluation.
Underestimating X
would be worse than
overestimating it.
The best value for
parameter μ could
plausibly be anywhere lower μ  lower X
in this range…
(The lower the value of μ,
the greater the risk that the
model will underestimate
variable/outcome X.)
sea level rise
choice of numerical value
for uncertain parameter μ
The flip side is that even when ethical
values are not directly influencing choices
in model development, those choices can
have consequences with ethical import!
loss of life in floods
economic losses
larger or smaller risk of
overestimating/underestimating X
Coupled epistemic-ethical choices in modeling
• choices in model construction & evaluation
– which quantities to prioritize for accurate simulation
– choice of uncertain parameter value(s) / parameterizations
• how to estimate and communicate uncertainty
• which methods/studies to pursue
A probability density function (pdf) is always an appropriate
way to represent uncertainty.
1. True
2. False
3. I have no idea.
95%
5%
1.
0%
2.
3.
Sometimes precise probabilities aren’t appropriate
epistemically or ethically
• Sometimes uncertainty is deeper than a pdf would imply. The
science is insufficient to assign precise probabilities to outcomes.
– The probability of more than two Cat 5 hurricanes making landfall in the
U.S during the 2070s under RCP 6.0.
• In these cases, representing uncertainty with precise probabilities
(or a full pdf) would be inaccurate, implying that we know more
than we really do.
• It would also be misleading. If decisions with real consequences
will be informed by these uncertainty estimates, offering precise
probabilities may be inappropriate from an ethical point of view;
we can expect bad consequences.
Being epistemically responsible
“…striving for epistemic excellence captures well what is
fundamental to…epistemic responsibility. It is for one to do
the best she can with what she has available to her,
epistemically speaking. It is to be circumspect in seeking
truth and avoiding error.” (Corlett 2008, p.180)
Epistemic responsibility & uncertainty
Estimating uncertainty about future climate change in an
epistemically responsible way would seem to require:
– striving to take account of all available evidence
– striving to take account of all recognized sources of uncertainty
– trying to factor in the possibility of “unknown unknowns”
– avoiding a one-size-fits-all mentality about the nature of one’s
uncertainty (e.g. probability-like vs. deeper)
– offering a depiction of uncertainty that does not misrepresent
one’s epistemic state (indicating that one knows more / less /
different than one in fact does) -- ownership
“likely range” =
we are roughly
66-100%
confident that
the change
would be in this
range
How to interpret these results?
“UKCP09 offers projections of the future climate that [are] based on
the current understanding of the climate system – there may be
scientific unknowns that would affect the information provided. Hence
UKCP09 should be seen as providing possible projections rather than
absolute predictions or forecasts of future climate.”
(http://ukclimateprojections.defra.gov.uk/content/view/633/531/)
“Probabilistic projections, although they are designed to quantify
uncertainty, … are themselves uncertain. …However, as a general
guideline we suggest that users should be able to use the distribution
from the 10 to the 90% probability levels, but not outside this range...”
(Murphy et al. 2009, Section 4.1)
Coupled epistemic-ethical choices in modeling
• choices in model construction/selection & evaluation
– which quantities to prioritize for accurate simulation
– choice of uncertain parameter value(s) / parameterizations
• how to estimate and communicate uncertainty
• which methods/studies to pursue
If a modeling study is on scientifically shaky ground, and we know
that decision makers will be tempted to use the quantitative results
in decisions with real consequences, should we not do the study?
70%
1. I think we probably should
not do the study.
2. It depends…
3. As a scientist, it’s not my
job to worry about that.
4. I have no idea.
15%
10%
1.
5%
2.
3.
4.
Attribution of extreme events
Douglas on the moral responsibilities of scientists
“With full awareness of science's efficacy and
power, scientists must think carefully about the
possible impacts and potential implications of
their work. Although there is no qualitative
difference between this responsibility and the
responsibility of automobile drivers to proceed with due care
and caution, the quantitative burden is much greater. The
ability to do harm (and good) is much greater for a scientist,
and the terrain almost always unfamiliar. The level of
reflection such responsibility requires may slow down science,
but such is the price we all pay for responsible behavior. The
driver may need to take more time getting to his destination;
similarly the scientist may need to take more time in
developing her research and determining how to present the
results.” (Douglas 2003, p.66)
Coupled ethical-epistemic issues and
decision support
1. Why are coupled ethical-epistemic issues
important to decision support?
2. What are ethically and epistemically
responsible approaches to decision support?
3. A challenge…
26
Why are coupled
ethical-epistemic issues
important to decision
support?
27
Overconfidence can result in downwards biased risk
estimates.
(Slide from K Keller)
Legally acceptable
flooding probabilities
often range between
1/100 and 1/10 000.
Revised estimate
with high reliability
Overconfident
projections can lead to
downwards biased risk
estimates of tail area
events and downwards
biased (constrained)
optimal risk
management strategies.
This example is about adaptation. Does this also apply to mitigation?
28
Do the analysts
and the users
share the same
understanding?
• How are instances of
overconfidence
and/or uncertainties
communicated?
“The gaps between the
authors’ intentions and the
readers’ understanding of the
probabilistic communications
are large andBudescu
systematic
et al 2012”
29
Rosenzweig et al 2011
Are Value Choices Transparent?
Risks from climate change, by reason for concern—2001 compared with updated data.
Smith J B et al. PNAS 2009;106:4133-4137
©2009 by National Academy of Sciences
30
Aggregating impacts risks obscuring
distributive justice concerns
Smith J B et al. PNAS 2009;106:4133-4137
©2009 by National Academy of Sciences
31
How do we balance epistemic robustness with
ethical salience in modeling?
32
Where is the problem located in the joint knowledge / values space?
Keller et al (in prep.)
33
What are ethically and
epistemically
responsible approaches
to decision support?
34
Whose Values?
The other uncertainty: values
35
Figure from K. Keller
What is the underlying value?
• Equity?
– Equity based on what measure?
• Attention to the least well off?
• Sustainability?
– Sustainability based on what measure?
• ………
36
Values Uncertainty
• Experts know what’s best…
• Stakeholders know what’s best…
Whose values?
What knowledge do we need given
the values?
37
“Deliberation with Analysis” Offers an
Effective Decision Support Process for Decisionmaking Under
Uncertainty
Deliberate:
•
Participants to decision
define objections, options,
and other parameters
Analysis:
•
Participants work with
experts to generate and
interpret decisionrelevant information
How do we include values in this process?
• Just ask them?
• How self-transparent are values?
• What if our stakeholder group isn’t representative?
• What if it is, but it still misses important values?
What are the best methods for collecting inputs
for Ethically Informed Robust Decision Making?
• Can we ensure values
comprehensiveness and
objectivity?
– Rawl’s Veil of Ignorance
insure impartiality?
• How do we include
“absent” stakeholders?
• How do we adjust for value
uncertainties?
• What if the analysts values
are different than the
stakeholder values?
39
A simplified flow diagram of ethically informed RDM
Values Informed
Mental Models
Veil of Ignorance
deliberations
Ethically
informed robust
strategies
40
But what if the analysts values inform the models/scenarios?
Values Informed
Mental Models
Veil of Ignorance
deliberations
Ethically
informed robust
strategies
41
GR
SCRIM
Decision analysts talk
statistically significantly
more often about
consequentialist values than
decisionmakers
• SCRiM Project Leaders
– prevalence of
nonconsequentialist
values
+
20
30
40
50
60
70
80
Frequency [%]
– prevalence of
consequentialist values
0.06
0.05
0.04
Density
• West Michigan Climate
Resiliency Consortium
+
95% CI
0.03
0.02
0.01
0
10
20
30
Difference in mean w elfare
response frequency (SCRiM − GR) [%]
40
42
H1: Decision analysts are mostly consequentialists;
decisionmakers are mostly not.
H2: This difference is decision-relevant
43
Challenge
• When you do your RDM exercise,
consider how many coupled
ethical-epistemic issues are
involved.
Potential discussion questions
1.
2.
3.
4.
5.
6.
7.
8.
What do we need to know to contribute to responsible decision-making?
What are some epistemic or ethical pitfalls to avoid if we are working in
‘climate services’?
How can we identify the most decision-relevant uncertainties?
What is a useful characterization of decision-relevant uncertainties?
How do we deal with the incompleteness of our models and their
potential overconfidence?
How do we find strategies that perform reasonably well in the face of
deep and dynamic uncertainties and across a wide range of objectives /
ethical frameworks?
How can we measure and improve the usefulness of decision-support
tools / models?
What (if any) advice do we give to (i) people who need to pour concrete
for coastal infrastructures now and (ii) a grad student who wants to study
the most important part of the problem?
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RESERVE MATERIALS
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References
•
•
•
•
•
Adler, C. E. and G. Hirsch Hadorn. (2014) The IPCC and treatment of uncertainties: topics and
sources of dissensus WIREs Clim Change 2014. doi: 10.1002/wcc.297.
Budescu, David V., Han-Hui Por & Stephen B. Broomell (2012) Effective communication of
uncertainty in the IPCC reports Climatic Change 113:181–200.
Mastrandrea MC, Field CB, Stocker TF, Edenhofer O, Ebi KL, Frane DJ, Held H, Kriegler E, Mach
KJ, Matschoss PR, et al. Guidance Note for Lead Authors of the IPCC Fifth Assessment Report
on Consistent Treatment of Uncertainties. Geneva, Switzerland: Intergovernmental Panel on
Climate Change (IPCC); 2010. Available at: http://www.ipcc.ch.
Rosenzweig, C., W. D., Solecki, R. Blake, M. Bowman, C. Faris, V. Gornitz, R. Horton, K. Jacob,
A. LeBlanc, R. Leichenko. M. Linkin, D. Major, M. O’Grady, L. Patrick, E. Sussman, G. Yohe, R.
Zimmerman. (2011). Developing coastal adaptation to climate change in the New York City
infrastructure-shed: process, approach, tools, and strategies. Climatic Change (2011) 106:93–
127.
Smith, Joel B., Stephen H. Schneider, Michael Oppenheimer, Gary W. Yohe, William Hare,
Michael D. Mastrandrea, Anand Patwardhan, et al. 2009. Assessing dangerous climate change
through an update of the Intergovernmental Panel on Climate Change (IPCC) ‘reasons for
concern.’ Proceedings of the National Academy of Sciences of the United States of America
106(11) (March 17): 4133-37.
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Risks from climate change, by reason for concern—AR5
48