1. Risk Analysis and Communication: What can we learn from research?
M. Granger Morgan
Head, Department of
Engineering and Public Policy
Carnegie Mellon University
Pittsburgh PA USA
tel:

2. First, Two Slides on Carnegie Mellon's Department of Engineering and Public Policy
A department in the Engineering college at Carnegie Mellon University.
Faculty: Total of 41. Include true 50:50 joint appointments with all five engineering departments as well as joint appointments with four different social science units in three other colleges.
Undergraduate double major degrees with traditional departments (574 BS graduates to date).
Graduate program is a research-oriented Ph.D. focused on problem in which the technical details really matter (current enrollment 45, 119 Ph.D. graduates to date).

3. Research Four major areas: 1. energy and environment.
2. risk analysis and communication.
3. telecommunication and information policy.
4. technology policy.
In the context of these four areas, we also work on issues in technology and development (China and India) and on issues in dual-use technology, arms control, and defense policy.
EPP currently has several large collaborative group efforts:
• Center for Integrated Study of the Human Dimensions of Global Change.
• The Electricity Industry Center.
• Green Design Initiative.
• Center for Energy and Environmental Systems.
• Center for the Study and Improvement of Regulation.
• Brownfields Center.
• IT and telecommunications policy.
• Risk analysis, ranking, communication.

4. Today I will talk about:
• What is risk?
• Basic ideas in risk analysis.
• A few details on the characterization and treatment of uncertainty.
• Basic ideas in risk management.
• Basic ideas in risk communication.
I've also included some slides which I will not show which summarize a few results from four different recent research projects which I'd be happy to discuss individually.
• Some recent topics of research.

5. Risk My dictionary defines risk as follows:
risk - n. 1. The exposure to the chance of injury or loss; a hazard or dangerous chance: he decided to take the risk.
Note:
• negative outcome;
• uncertain;
• not just the probability of loss.
While this looks relatively simple, in the real world,
things get more complicated…

6. Let's perform a little thought experiment
Suppose I have a new product.
I've done careful market research and know:
I could sell Q devices at a price P, for total revenues QP.
I'd make a profit R.
BUT, the product will have a net impact on national mortality of D excess deaths/year.
When would I be justified in introducing this product?

7. Some of the things that may matter...
- A few people bear the risks and many get the benefits.
- The product is frivolous.
- The deaths do not occur immediately.
- I can identify the individuals before/after the deaths.
- The deaths all occur at once/are spread out.
- The people are socially related.
- D = N-M where N is deaths caused and M is deaths prevented.
- The effects are uncertain, <P(n)> = N.
- In addition to mortality, there is morbidity; environmental impact; etc.
In short, risk is a "multi-attribute concept…

8. Risks can be reliably sorted in terms of such factors...
Source: Slovic et al., in Readings in Risk, 1980.

9. A useful framework for thinking about risk

10. A specific example

11. The role of values

12. Today I will talk about:
• What is risk?
• Basic ideas in risk analysis
• A few details on the characterization and treatment of uncertainty.
• Basic ideas in risk management.
• Basic ideas in risk communication.
• Some recent topics of research.

13. A highly simplified taxonomy of tools for risk assessment

14. Transport and Dispersion Models
Source: Morgan and McMichael, Policy Sciences, 1981.

15. Dose Response Functions
Linear, without (A)
and with (B) threshold.
Non-linear, without (A)
and with (B) threshold.

16. Complications Variable sensitivity among individuals.
Response depends in a time-dynamic way on exposure (i.e., no one-to-one mapping between cumulative exposure and response).

17. Examples of real dose-response functions
Source: Morgan, IEEE Spectrum, 1981.

18. We could go through a similar discussion for discrete events
Event trees:
Fault trees:
Source: Rasmussen, Ann. Rev. of Energy, 1981.

19. Today I will talk about:
• What is risk?
• Basic ideas in risk analysis
• A few details on the characterization and treatment of uncertainty.
M. Granger Morgan, Max Henrion, with a chapter by Mitchell Small, Uncertainty: A guide to dealing with uncertainty in quantitative risk and policy analysis, 332pp., Cambridge University Press, New York, (Paperback edition Latest printing (with revised Chapter 10) 1998.)
• Basic ideas in risk management.
• Basic ideas in risk communication.
• Some recent topics of research.

20. Probability Probability is the basic language of uncertainty.
I will adopt a personalistic view of probability (sometimes also called a subjectivist or Bayesian view).
In this view, probability is a statement of the degree of belief that a person has that a specified event will occur given all the relevant information currently known by that person.
P(X|i) where:
X is the uncertain event
i is the person's state of information.

21. The clairvoyant test
Even if we take a personalist view of probability, the event or quantity of interest must be well specified for a probability, or a probability distribution, to be meaningful.
"The retail price of gasoline in 2008" does not pass this test. A clairvoyant would need to know things such as:
Where will the gas be purchased?
At what time of year?
What octane?

22. Does a subjectivist view mean your probability can be arbitrary?
NO, because if they are legitimate probabilities, they must
• conform with the axioms of probability
• be consistent with available empirical data.
Lots of people ask, why deal with probability? Why not just use subjective words such as "likely" and "unlikely" to describe uncertainties? There are very good reasons not to do this.

23. The risks of using qualitative uncertainty language
Qualitative uncertainty language is inadequate because:
the same words can mean very different things to different people.
the same words can mean very different things to the same person in different contexts.
important differences in experts' judgments about mechanisms (functional relationships), and about how well key coefficients are known, can be easily masked in qualitative discussions.

24. Mapping words to probabilities
This figure shows the range of probabilities that people are asked to assign probabilities to words, absent any specific context.
Figure adapted from Wallsten et al., 1986.

25. Ex Com of EPA SAB
The minimum probability associated with the word "likely" spanned four orders of magnitude.
The maximum probability associated with the word "not likely" spanned more than five orders of magnitude.
There was an overlap of the probability associated with the word "likely" and that associated with the word "unlikely"!
Figure from Morgan, HERA, 1998.

26. The bottom line
Without at least some quantification, qualitative descriptions of uncertainty convey little, if any, useful information.
Here are two examples from the climate assessment community:
Schneider and Moss have worked to get a better treatment of uncertainty incorporated into the past and current round of IPCC. Progress is uneven, but awareness is growing. Individual investigators are pushing the process along.
The U.S. National Assessment Synthesis Team gave quantitative definitions to five probability words and tried to use them consistently throughout their overview report.

27. In doing risk analysis, we must consider two quite different kinds of uncertainty
Situations in which we know the relevant variables and the functional relationships among them, but we no not know the values of key coefficients (e.g., the "climate sensitivity").
Situations in which we are not sure what all the relevant variables are, or the functional relationships among them (e.g., will rising energy prices induce more technical innovation?).
Both are challenging, but the first is much more easily addressed than the second.

28. Uncertainty about quantities
From Morgan and Henrion, Uncertainty, Cambridge, 1990/99.

29. One should…
…use available data, and well-established physical and statistical theory, to describe uncertainty whenever either or both are available.
However, often the available data and theory are not exactly relevant to the problem at hand, or they are not sufficiently complete to support the full objective construction of a probability distribution.
In such cases, one may have to rely on expert judgment. This brings us to the problem of how to "elicit" expert judgment.

30. Expert elicitation takes time and care
Eliciting subjective probabilistic judgments requires careful preparation and execution.
Developing and testing an appropriate interview protocol typically takes several months. Each interview is likely to require several hours.
When addressing complex, scientifically subtle questions of the sorts involved with most problems in climate change, there are no satisfactory short cuts. Attempts to simplify and speed up the process almost always lead to shoddy results.
The next eight slides (which I will skip because time is short) talk about the important issues of overconfidence and the impacts of "cognitive heuristics." These are critically important topics for anyone who actually plans to do expert elicitation. <Skip to 37.>

31. Over Confidence Extra slide - will not show
Source: Morgan and Henrion, 1990/99.

32. Over Confidence Extra slide - will not show
Source: Morgan and Henrion, 1990/99.

33. Extra slide - will not show
Cognitive heuristics
When ordinary people or experts make judgments about uncertain events, such as numbers of deaths from chance events, they use simple mental rules of thumb called "cognitive heuristics."
In many day-to-day circumstances, these serve us very well, but in some instances they can lead to bias - such as over confidence - in the judgments we make.
This can be a problem for experts too.
The three slides that follow illustrate three key heuristics: "availability," "anchoring and adjustment," and "representativeness."

34. Extra slide - will not show
Cognitive bias
from Lichtenstein et al., 1978.
Availability: probability judgment is driven by ease with which people can think of previous occurrences of the event or can imagine such occurrences.

35. Cognitive bias…(Cont.)
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Cognitive bias…(Cont.)
from Lichtenstein et al., 1978.
Anchoring and adjustment: probability judgment is frequently driven by the starting point which becomes an "anchor."

36. Cognitive bias…(Cont.)
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Cognitive bias…(Cont.)
I flip a fair coin 8 times. Which of the following two outcomes is more likely?
Outcome 1: T, T, T, T, H, H, H, H
Outcome 2: T, H, T, H, H, T, H, T
Of course, the two specific sequences are equally likely...but the second seems more likely because it looks more representative of the underlying random process.
Representativeness: people judge the likelihood that an object belongs to a particular class in terms of how much it resembles that class.

37. Expert elicitation…(Cont.)
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Expert elicitation…(Cont.)
In all our elicitation studies, we've focused on creating a process that allows the experts to provide their carefully considered judgment, supported by all the resources they may care to use. Thus, we have:
Prepared a background review of the relevant literatures.
Carefully iterated the questions with selected experts and run pilot studies with younger (Post-doc) experts to distil and refine the questions.
Conducted interviews in experts' offices with full resources at hand.
Provide ample opportunity for subsequent review and revision of the judgments provided.
All of these efforts have involved the development of new question formats that fit the issues at hand.

38. Expert elicitation …(Cont.)
Over the past two decades, my colleagues and I have developed and performed a number of substantively detailed expert elicitations. These have been designed to obtain experts’ considered judgments. Examples include work on:
Health effects of air pollution from coal-fired power plants.
• M. Granger Morgan, Samuel C. Morris, Alan K. Meier and Debra L. Shenk, "A Probabilistic Methodology for Estimating Air Pollution Health Effects from Coal-Fired Power Plants," Energy Systems and Policy, 2, , 1978.
• M. Granger Morgan, Samuel C. Morris, William R. Rish and Alan K. Meier, "Sulfur Control in Coal-Fired Power Plants: A Probabilistic Approach to Policy Analysis," Journal of the Air Pollution Control Association, 28, , 1978.
• M. Granger Morgan, Samuel C. Morris, Max Henrion, Deborah A.L. Amaral and William R. Rish, "Technical Uncertainty in Quantitative Policy Analysis: A Sulfur Air Pollution Example," Risk Analysis, 4, , 1984 September.
• M. Granger Morgan, Samuel C. Morris, Max Henrion and Deborah A. L. Amaral, "Uncertainty in Environmental Risk Assessment: A case study involving sulfur transport and health effects," Environmental Science & Technology, 19, , 1985 August.

39. Expert elicitation…(Cont.)
Climate science, climate impacts
and mitigation technology:
• M. Granger Morgan and David Keith, "Subjective Judgments by Climate Experts," Environmental Science & Technology, 29(10), , October 1995.
• Elizabeth A. Casman, M. Granger Morgan and Hadi Dowlatabadi, “Mixed Levels of Uncertainty in Complex Policy Models,” Risk Analysis, 19(1), 33-42, 1999.
• M. Granger Morgan, Louis F. Pitelka and Elena Shevliakova, "Elicitation of Expert Judgments of Climate Change Impacts on Forest Ecosystems," Climatic Change, 49, , 2001.
• Anand B. Rao, Edward S. Rubin and M. Granger Morgan, "Evaluation of Potential Cost Reductions from Improved CO2 Capture Systems,"Proceedings of the 2nd National Conference on Carbon Sequestration, Alexandria, VA, May 5-8, 2003.
Bounding uncertain health risks:
• M. Granger Morgan, "The Neglected Art of Bounding Analysis," Environmental Science & Technology, 35, 162A-164A, April 1, 2001.
• Minh Ha-Duong, Elizabeth A. Casman, and M. Granger Morgan, "Bounding Poorly Characterized Risks: A lung cancer example," Risk Analysis, in press.

40. Warming for 2x[CO2] …and, lest you conclude that most of these
Source:
Morgan and Keith, ES&T, 1995.
…and, lest you conclude that most of these
experts are in basic agreement…

41. Pole to equator temperature gradient for 2x[CO2]
Source: Morgan and Keith, ES&T, 1995.

42. Biomass in Northern Forests w/ 2xCO2 climate change
Change in standing biomass
Change in soil carbon
Source: Morgan et al., Climatic Change, 2001.

43. Biomass in Tropical Forests w/ 2xCO2 climate change
Change in standing biomass
Change in soil carbon
Source: Morgan et al., Climatic Change, 2001.

44. Uncertainty about model form
Often uncertainty about model form is as or more important than uncertainty about values of coefficients. Until recently there had been little practical progress in dealing with such uncertainty, but now there are several good examples:
• John Evans and his colleagues at the Harvard School of Public Health (Evans et al., 1994).
• Alan Cornell and others in the seismic risk (Budnitz et al., 1995).
• Hadi Dowlatabadi and colleagues at Carnegie Mellon in Integrated Assessment of Climate Change (ICAM) (Morgan and Dowlatabadi, 1996).

45. A very large hierarchically organized stochastic
ICAM
Integrated Climate
Assessment Model
A very large hierarchically
organized stochastic
simulation model built
in Analytica®.
See for example:
Hadi Dowlatabadi and M. Granger Morgan, "A Model Framework for Integrated Studies of the Climate Problem," Energy Policy, 21(3), , March 1993.
and
M. Granger Morgan and Hadi Dowlatabadi, "Learning from Integrated Assessment of Climate Change," Climatic Change, 34, , 1996.

46. ICAM deals with...
…both of the types of uncertainty I've talked about:
1. It deals with uncertain coefficients by assigning PDFs to them and then performing stochastic simulation to propagate the uncertainty through the model.
It deals with uncertainty about model functional form (e.g., will rising energy prices induce more technical innovation?) by introducing multiple alternative models which can be chosen by throwing "switches."

47. ICAM
There is not enough time to present any details from our work with the ICAM integrated assessment model. Here are a few conclusions from that work:
Different sets of plausible model assumptions give dramatically different results.
No policy we have looked at is dominant over the wide range of plausible futures we’ve examined.
The regional differences in outcomes are so vast that few if any policies would pass muster globally for similar decision rules.
Different metrics of aggregate outcomes (e.g., $s versus hours of labor) skew the results to reflect the OECD or developing regional issues respectively.

48. These findings lead us...
...to switch from trying to project and examine the future, to using the modeling framework as a test-bed to evaluate the relative robustness, across a wide range of plausible model futures, of alternative strategies that regional actors in the model might adopt.
We populated the model's regions with simple decision agents and asked, which behavioral strategies are robust in the face of uncertain futures, which get us in trouble.
Thus, for example, it turns out that tracking and responding to atmospheric concentration is more likely to lead regional policy makers in the model to stable strategies than tracking and responding to emissions.

49. Our conclusion
Prediction and policy optimization are pretty silly analytical objectives for much assessment and analysis related to the climate problem.
It makes much more sense to:
• Acknowledge that describing and bounding a range of futures may often be the best we can do.
• Recognize that climate is not the only thing that is changing, and address the problem in that context.
• Focus on developing adaptive strategies and evaluating their likely robustness in the face of a range of possible climate, social, economic and ecological futures.

50. Today I will talk about:
• What is risk?
• Basic ideas in risk analysis
• A few details on the characterization and treatment of uncertainty.
• Basic ideas in risk management.
• Basic ideas in risk communication.
• Some recent topics of research.

51. Strategies for managing risk

52. Examples
No strategy is best everywhere. Depending on context, different strategies do best in different settings.

53. Strategies for managing risk
• Tort and other common law (e.g., negligence, liability, nuisance, trespass).
• Insurance (private, public or hybrid).
• Voluntary standard setting organizations (UL, ASTM, etc.).
• Individual and collective corporate initiatives (CMA - Responsible Care).
• Information-based strategies (e.g, TRI, green labels).
• Mandatory government standards and other regulations (performance standards, design standards).
• Market-based solutions (emissions taxes, tradable permits).

54. Today I will talk about:
• What is risk?
• Basic ideas in risk analysis
• A few details on the characterization and treatment of uncertainty.
• Basic ideas in risk management.
• Basic ideas in risk communication.
M. Granger Morgan, Baruch Fischhoff, Ann Bostrom and Cynthia Atman, Risk Communication: A mental models approach, 351pp., Cambridge University Press, New York, 2002.
• Some recent topics of research.

55. The traditional approach…
…to risk communication involves two steps:
Ask an expert what people should be told.
Get a "communications expert" to package it.
And, if you are being really fancy:
Run some tests to see how people like it.
However, if you think about it for a few minutes, this approach ignores two critical issues:
• What the people receiving the message already "know" about the topic.
• What they need to know to make the decisions they face.

56. Finding out what people know…
…is not simple! If I give people a questionnaire, I have to put information in my questions.
People are smart. They will start making inferences based on the information in my questions.
Pretty soon I won't know if their answers reflect what they already knew, or the new ideas they have come up with because of the information I have given them.
I need a better, less intrusive way to learn what they know. The method we have developed to do this is called the mental model interview.

57. Five step process:
In work done over the past 15 years at Carnegie Mellon, we have developed a five-step approach to risk communication, based on people's mental models of risk processes:
Develop an "influence diagram" to structure expert knowledge.
2. Conduct an open-ended elicitation of people's beliefs about a hazard, allowing the expression of both accurate and inaccurate concepts. Map the results to the expert influence diagram.

58. Five steps…(Cont.)
3. On the basis of results from the open-ended interviews, develop and administer a closed-form questionnaire to a much larger group in order to determine the prevalence of these beliefs.
Develop a draft communication based on both a decision analytic assessment of what people need to know in order to make informed decisions and a psychological assessment of their current beliefs.
Iteratively test successive versions of those communications using open-ended, closed-form, and problem-solving instruments, administered before, during, and after the receipt of the message.
In 1994, we applied these methods to study public understanding of climate change. While the results are now getting old, I will show you a few since they are unlikely to have changed much.

59. Example of opening response in interview
Interviewer: "I'd like you to tell me all about the issue of climate change."
Subject: "Climate change. Do you mean global warming?"
Interviewer: "Climate change."
Subject: "OK. Let's see. What do I know. The earth is getting warmer because there are holes in the atmosphere and this is global warming and the greenhouse effect. Um... I really don't know very much about it, but it does seem to be true. The temperatures do seem to be kind of warm in the winters. They do seem to be warmer than in the past.. and.. hmm.. That's all I know about global warming.

60. Another example…
Interviewer: "Tell me all about the issue of climate change."
Subject: "I'm pretty interested in it... The ice caps are melting -- the hole in the ozone layer. They think pollution from cars and aerosol cans are the cause of all that. I think the space shuttle might have something to do with it too, because they always send that up through the earth, to get out in outer space. So I think that would have something to do with it, too."

61. Another example…
Interviewer: "Tell me all about the issue of climate change."
Subject: "Climate change? Like, what about it? Like, as far as the ozone layer and ice caps melting, water level raising, rainforest going down, oxygen going down because of that? All of that kind of stuff?"
Interviewer: "Anything else?"
Subject: "Well, erosion all over the place. Um, topsoils going down into everywhere. Fertilizer poisoning. "Interviewer: "Anything else that comes to mind related to climate change?Subject: "Climate change. Winter's ain't like they used to be. Nothing's as severe. Not as much snow. Nothing like that."

62. Schematic of the approach2 3 4 1 5

63. Based on what we have learned…
…in our studies of public understanding of various risks we have produced various public communication materials:
I have brought a few examples for people who are interested.

64. A few conclusions
There is no such thing as an "expert" in public communication who can simply tell you what to do.
An empirical approach is essential.
Before developing a communication one must learn what the public knows and thinks.
The mental models method offers a promising strategy for doing this.
Uncertainty must be quantified.

65. Today I will talk about:
• What is risk?
• Basic ideas in risk analysis
• A few details on the characterization and treatment of uncertainty.
• Basic ideas in risk management.
• Basic ideas in risk communication.
• Some recent topics of research.

66. In the Department of Engineering and Public Policy at Carnegie Mellon…
…we've had an active program in risk-related research for 30 years. Since time is very short, I've included a few illustrative slides on four recent projects:
• Dealing with mixed and extreme uncertainty.
Elizabeth A. Casman, M. Granger Morgan and Hadi Dowlatabadi, “Mixed Levels of Uncertainty in Complex Policy Models,” Risk Analysis, 19(1), 33-42, 1999.
• The use of bounding analysis.
Minh Ha-Duong, Elizabeth A. Casman, and M. Granger Morgan, “Bounding Poorly Characterized Risks: A Lung Cancer Example,” Risk Analysis, in press.
• Risk ranking.
H. Keith Florig et al. "A Deliberative Method for Ranking Risks (I): Overview and test bed development," Risk Analysis, 21(5), , 2001 and Kara M. Morgan et al. "A Deliberative Method for Ranking Risks (II): Evaluation of validity and agreement among risk managers," Risk Analysis, 21(5), , 2001.
• Public perceptions of deep geological disposal of CO2.
Claire R. Palmgren, M. Granger Morgan, Wändi Bruine de Bruin, and David W. Keith "Initial Public Perceptions of Deep Geological and Oceanic Disposal of Carbon Dioxide," in review at ES&T.

67. Acknowledgments
Over the years my work on risk has been greatly assisted by collaborations with many colleagues including:
Elizabeth Casman
Michael DeKay
Hadi Dowlatabadi
Paul Fischbeck
Baruch Fischhoff
Keith Florig
Max Henrion
Karen Jenni
David Keith
Lester Lave
Kara Morgan
Louis Pitelka
Elena Shevliakova
Patti Steranchak
Henry Willis

68. Limited domain of model validity
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Limited domain of model validity
Examples of warming estimated via the ICAM model (dark curves) and probability that the associated climate forcing will induce a state change in the climate system (light curves) using the probabilistic judgments of three different climate experts.

69. Extra slide - will not show
Model switching
Schematic illustration of the strategy of switching to progressively simpler models as one moves into less well understood regions of the problem phase space, in this case, over time.

70. Illustration of model switching
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Illustration of model switching
Results of applying the model switch-over strategy to the ICAM demographic model (until about 2050) and an estimate of the upper-bound estimate of global population carrying capacity based on J. S. Cohen.

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Bounding
When the science is poorly understood, probabilistic risk analysis is routinely used to obtain estimates of health impacts, with results typically reported in the form of a very broad subjective probability density function.
For impacts with multiple causes, such estimates are usually made separately, by different investigators, for each cause of interest.
However, if those separate probabilistic estimates were brought together and summed, the results could sometimes substantially exceed the numbers of cases actually observed.

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Bounding analysis
In a commentary in ES&T in April of 2001, I argued that methods of bounding analysis could be used in environmental risk analysis to avoid such problems. For health endpoints with multiple external causes, the available knowledge can be used to constrain estimates of the magnitude of the poorly characterized risks.
If most risks were known with precision, this would be a simple subtraction problem. But health risks from environmental causes often involve high uncertainty. However, in many cases, there is agreement on the general magnitude of the impacts of the best-studied causes. The idea is to use this knowledge to bound the sum of the other less well known risks.
M. Granger Morgan, "The Neglected Art of Bounding Analysis," Viewpoint, Environmental Science & Technology, 35, 162A-164A, April 1, 2001.

73. To illustrate the method…
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To illustrate the method…
…we have developed an example that harmonizes informed beliefs about the relative contribution made by different causal factors using total current U.S. lung cancer mortality.
The goal of the analysis is to generate an upper bound on the mortality attributed to the group of poorly-characterized factors, derived from information on the group of well-characterized risk factors.
To perform this bounding analysis it is first necessary to apportion lung cancer mortalities among the various known causes, or groups of causes, according to current scientific knowledge and opinion.

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Illustration

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In the example…
…I am presenting today, we have developed the needed bounds by reviewing the relevant literatures and making our own judgments.
We are currently running an expert elicitation to seek the carefully informed judgment of a number of lung cancer experts.

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We partition…
…the lung cancer deaths into non-intersecting categories of single causative factors and causal factor groupings and constrain the sum of all mortality across those partitions to equal the observed mortality.
In addition to causal categories, we define a "background" category for the lung cancer deaths that would have occurred in the absence of exposure to all carcinogens.
While there is no way to measure this quantity, clearly it exists in principle, and it can be bounded through consideration of lung cancer deaths in groups with low exposures to known carcinogens.

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In our illustration…
… the set of possible causes of lung cancer () consists of active, former, and passive smoking (C); domestic exposure to radon (R); occupational exposure to inhaled asbestos (A); and the group of all other environmental risk factors (X).
Note that our analysis deals with annual lung cancer mortality in the U. S. population as a whole and not in subpopulations that experience different exposure histories, display different susceptibilities and have different access to health care.
Since genetic factors play a role in all cancers, we consider them to be a priori, non-manipulable attributes of the overall U.S. population.

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Graphically

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Smoking

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In summary
Based on our review of the literature, we have constructed a set of judgments apportioning lung cancer deaths among these causes which imply the following constraints:
f u (C) = 0.70, f l(C) = 0.95
f u (R) = 0.21, f l(R) = 0.02
f u (A) = 0.05, f l (A) = 0.01
where f u denotes an upper bound on the fraction of lung cancer deaths due to a particular cause and f l the lower bound.

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From the bounds…
…on the proportions of annual lung cancer mortality due to the major lung cancer causes (C, R, and A), information about the magnitude of risk from all other lung cancer causes (X) is
then inferred, using a consistency constraint on the total number of deaths.
Graphically we perform:

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Example results

83. Example results…(Cont.)
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Example results…(Cont.)
This example calculation assigns between 0% and 3.2% of lung cancer deaths to X, the group of unspecified occupational and environmental pollutants.
Thus, for the group of known and suspected lung carcinogens other than C, A, and R, if one is confident in the bounds assigned to the well understood risk factors, the sum of the effects of the poorly understood factors collectively should account for no more than 3.2% of total lung cancer mortality.
This provides a constraint on estimates of those risks produced by more conventional risk analysis.

84. Checking diesel estimates
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Checking diesel estimates
Two national associations of air quality control offices published a projection of the number of cancers due to exposure to diesel exhaust.
They estimated that diesel would be responsible for 125,110 cancers for all metropolitan and non-metropolitan areas of the U.S. (over 70-year lifetimes) for an annual rate of 1,787 cancers.
This figure is below 3% (4,716 deaths) of the projected 2003 lung cancer mortality rate of 157,200, even without adjusting for the non-lung cancer mortality inherent in their risk estimate, and therefore, to first order, would pass our plausibility test.

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Our objective...
...in the work we've done on risk ranking has been to develop and demonstrate a method which:
• Uses experts to analyze and characterize the risks (because they have the necessary knowledge).
• Uses modern risk communication methods to describe the risks in multi-attribute terms (so that they will be understandable to educated members of the general public).
• Uses representative groups of laypeople to perform the actual ranking (because ranking requires the application of social values).
• Produces a fairly "thick" description of the deliberations, including ranks which are robust, and which are useful as an input in risk management decision making.

86. Steps in the risk-ranking method
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Steps in the risk-ranking method

87. First experimental test-bed: risks in schools
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First experimental test-bed: risks in schools
We chose risks in a hypothetical middle school as our test-bed because:
most people know and care about the topic -- major efforts are not required to get lay subjects briefed before participating in studies;
risks in schools are not the responsibility of any single existing U.S. Government risk management agency;
the topic offers opportunities to address a wide range of physical and chemical risks as well as important social issues;
there are a number of recent studies on which we can build.

88. Example of a risk summary sheet
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Example of a risk summary sheet

89. Extra slide - will not show
Town of Centerville

90. Centerville Middle School
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Centerville Middle School

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Second test-bed
The middle school test-bed involved only health and safety risks.
After developing and refining the method in that context, we then developed a second test-bed (based on a hypothetical county) in order to extend the work to include ecological and environmental risks.
A major part of the work in creating this second test-bed has involved developing an appropriate set of relevant attributes. That's a fairly long story that I will not go into, but Ph.D. student Henry Willis has done a great job with this problem.

92. Extra slide - will not show
DePaul County

93. Risks used in the two test-beds
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Risks used in the two test-beds
The middle school test-bed
Accidental injuries
Airplane Crash
Allergens
Asbestos
Bites and Stings
Building Collapse
Commuting to school on foot, bike or by car
Common infectious diseases
Drowning
Electrical Power
Electromagnetic fields (EMF)
Fire and Explosion
Food Poisoning
Hazardous material transport
Intentional Injury
Lead poisoning
Less common infectious diseases
Lightning
Radon gas
School bus accidents
Self-Inflicted Injury
Team Sports
The county-level test-bed
Agricultural runoff
Air pollution from electric power generation
Food poisoning
Genetically-modified corn
Invasive species
Land filling municipal solid waste
Motor vehicle accidents
Recreational motor boating
Road salt and road salt runoff
Transporting hazardous materials by truck

94. Typical ranking procedure
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Typical ranking procedure
Study
all
materials
Individual
holistic
ranking
Start
group
ranking
Individual MA
ranking
Revise
group
ranking
Final
individual
H and MA
rankings

95. Summary of findings with school test-bed:
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Summary of findings with school test-bed:
Consistency between the rankings that have resulted from the holistic and multiattribute procedures has been good for both individuals and for groups, suggesting that these procedures capture an underlying construct of riskiness.
Rankings of risks were similar across individuals and groups, even though individuals and groups did not always agree on the relative importance of risk attributes.
Lower consistency between the risk rankings from the holistic and multiattribute procedures and lower agreement among individuals and groups regarding these rankings were observed for a set of high-variance risks.

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The story in numbers:
218 individuals (in 43 groups) have performed risk rankings for health and safety risks.

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Summary...(Cont.)
Participants reported high levels of satisfaction with their groups’ decision-making processes and the resulting rankings, and these reports were corroborated by regression analyses.
Because of the generally high levels of consistency, satisfaction, and agreement we have observed we conclude that this deliberative method is capable of producing risk rankings that can serve as informative inputs to public risk-management decision making.

98. Findings from the environmental/ecological studies
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Findings from the environmental/ecological studies
Participants are able to complete expanded ranking task.
Holistic and multiattribute rankings are consistent with each other.
Agreement shown between individuals and groups.
Though less satisfied with attribute ranking process, participants revealed satisfaction with final group risk rankings.
Results parallel previous findings from health and safety test-bed.

99. In order to study public perceptions…
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In order to study public perceptions…
…of CCD, we had to modify the basic mental model interview approach since the typical member of the general public knows nothing about this technology.
Part 1: Using language that we made as neutral as we could, we explained the motivation of wishing to reduce the accumulation of atmospheric CO2 and briefly outlined the basic design options of CCD technologies. We then asked a series of questions to elicit reactions.
Part 2: Briefly discussed three questions: 1) "Can the technology to separate and dispose of carbon dioxide be made practical, and cheap enough"; 2) "Once the CO2 is put down deep in rock formations or deep in the oceans, will it stay there?" and 3) "If the technology can be made cheap and reliable, will the energy industry adopt it and use it widely?" Again, we asked a series of questions to elicit reactions.
Part 3: Discussed "some of the concerns that critics might raise about these technologies." Topics covered including CO2 pipeline issues, slow leaks, fast releases, issues related to ocean ecology and issues related to hydrogen safety.
We asked a series of specific evaluative questions.

100. Results from our pilot study suggested...
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Results from our pilot study suggested...
...that the standard concerns about siting would arise but may not be dramatically different than those associated with other large technologies.
Fears about rupture of high pressure CO2 pipelines did not appear to be any greater than those associated with natural gas pipelines.
Some concerns were expressed about large rapid releases from deep geological formations, but they looked manageable.
However, while deep geological injection appeared like it would prove to be publicly acceptable, the pilot results suggested that proposals for deep ocean injection were likely to lead to vigorous public opposition.

101. Ocean sequestration example comments
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Ocean sequestration example comments
I think the concern that really strikes me the most would have to be pumping it under very high pressure into the deep ocean I know the ocean is very big and is very deep but I’m wondering what kind of affect it would have on our oceans. [S1]
That, if this extra CO2 is absorbed into the ocean, would it disrupt whatever balance is in the ocean? That it might be harmful to things that live in the ocean. [S3]
Well, where are they going to build these? Do they have to be near the ocean? Or are they going to build big pipelines into the ocean to flush the stuff away? In the process of doing this, is there going to be pollution occurring, from this process? [S5]
So, I don’t necessarily like the fact that it’s being pumped down deep in the ocean, kind of like out of sight, out of mind. [S7]
So, if we were to put it, like, in the ocean, we could be messing with
some form of life that’s on the bottom. I don’t think we have much
knowledge of what’s down there. Because we really can’t explore that deep. So we’d be messing with something we have no knowledge of. [S8]

102. One of the things we noted…
…in our pilot studies was that subjects wanted to consider alternatives to CCD.
Accordingly, in designing the closed form survey, we included questions which allowed us to explore preferences across several alternative strategies for reducing CO2 emissions.
Survey outline:
1. Background
2a. Climate change
2b. General issues including climate change
Options for limiting CO2
Energy systems that dispose of CO2
Places to dispose of CO2
5.1 Deep rock formations
5.2 Disposal of CO2 in the deep ocean
Final evaluation
Environmental issues (NEPS = New Ecological Paradigm Scale)
A few questions about yourself

103. Views on climate change
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Views on climate change

104. Not high on respondent's list of concerns
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Not high on respondent's list of concerns
This is consistent with previous studies that we and others have done in the U.S. Results are quite different in Europe.
BUT, remember, despite this result, climate change clearly has political legs in the U.S. in many states.

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"Whatever your own beliefs are about climate change, imagine that the U.S. government has decided that we must cut in half the amount of CO2 that is released by generating electric power.
Suppose that your electricity supplier has different methods of meeting the goal to reduce CO2 emissions by 50%. Some of these methods use a mixture of generation systems that produce little or no CO2, combined with regular coal-burning power plants. These methods do not all cost the same because some ways of making electricity with less CO2 are cheaper than others.
The supplier will be offering their customers a choice of how they would like them to meet this reduction. Some of the options to reduce CO2 emissions will be more expensive than the way we produce electricity today, so we would like you to tell us which method you would be willing to pay the most for."

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Rank order of options
Options used different generation mixes (base of coal) in order to reduce CO2 emissions by 50%.

107. Evaluation of specifics
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Evaluation of specifics
The next three slides, which I will not show, give you the numbers on these and other results.

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Summary evaluations

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CCD Conclusions
The results of this study suggest that, at best, the public is likely to view this technology with mixed feelings.
High levels of public acceptance will almost certainly require:
• broader public understanding of the need to limit carbon dioxide emissions and alternative options for carbon management;
• a much stronger scientific understanding and a larger empirical base for claims about the likely efficacy and safety of disposal; and
• an approach to public communication, regulation, monitoring, and emergency response which is open and respectful of public concerns.

110. CCD Conclusions…(Cont.)
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CCD Conclusions…(Cont.)
An open and inclusive approach does not guarantee success.
However, an arrogant approach such as the one adopted in the past by the industries responsible for nuclear power and genetically modified crops, could create a level of public distrust that makes the wide-spread implementation of geological carbon disposal in the U.S. difficult, if not impossible.