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Edited By: L. Anthony Cox, Jr.
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Online ISSN: 1539-6924
Special Issue: Stanley Kaplan: Reflections and Papers Published
Reflections and Papers Published in Risk Analysis, An International Journal
B. John Garrick, Michael Greenberg, and Yacov Haimes
Stanley Kaplan passed away in June 2011 in his 79th year. We celebrate his pioneering efforts to introduce quantitative methods to risk analysis in this virtual issue of the journal. We are pleased to offer the 18 articles that Stan authored or co-authored in the journal between 1981 (the first volume) and 2002, as well as present a short biography and personal reflections from some of Stan’s associates and friends.
Stanley Kaplan Papers in Risk Analysis, An International Journal, 1981-2002
Below is the list of the 18 papers that Stan Kaplan authored or co-authored in Risk Analysis. You can go directly to the paper by clicking on the reference. To whet your appetite, I have summarized each paper and added a sentence about the current status of the issue it addresses. His pioneering efforts to place these policy challenges in context and offer plausible solutions are as relevant today as they were when he wrote them. Nearly all of these issues remain, and the emerging risk issues have similar attributes.
1. Kaplan S, Garrick BJ. On the quantitative definition of risk.1981; 1(1): 11-27.
The first article in the first of volume of the journal described the famous trilogy of risk assessment: what are the events, likelihoods and consequences. The paper has been cited close to a thousand times and was the first clear articulation of the role of Bayes’ theorem in risk analysis.
2. Kaplan S. On the method of discrete probability distributions in risk and reliability calculations: Application to seismic risk assessment. 1981; 1(3): 189-196.
Dr. Kaplan asserts that risk-related events are uncertain and hence simple numerical risk estimates should be replaced by estimates derived from probability-based distributions. He illustrates a numerical approach with seismic risk. We need not go back far into recent history to illustrate the wisdom of his suggestions applied to earthquakes, tsunamis, and hurricanes, as well as, nuclear, oil and other engineered system failures.
3. Kaplan S, Garrick BJ. Some misconceptions about misconceptions: A response to Abramson.1981; 1(4): 231 233.
This letter to the editor by Stan Kaplan and John Garrick reiterates their call for blending expert judgment and technical data into risk calculations by weighting information. This approach has become standard practice.
4. Kaplan S, Matrix theory formalism for event tree analysis, application to nuclear risk analysis. 1982; 2(1):9-18.
Stanley Kaplan explains how event trees can be used as transition probability matrices and then he applies the tool to risk assessment for a nuclear power plant. This approach and variations are used in every nuclear power plant risk assessment.
5. Kaplan S. Response to Nelson and Rasmuson’s letter. 1982; 2(4): 207.
Stanley Kaplan developed the discrete probability distribution (DPD) for risk analysis applications. Here he replies to a criticism of how the DPD was calculated. The DPD has been used in innumerable applications.
6. Kaplan S, Perla HF, Bley D. A methodology for seismic risk analysis of nuclear power plants. 1983; 3(3):169-180.
One of my favorite Kaplan papers, it walks us through how each component and system in a nuclear power plant is fragile, how these can be captured by event trees and fault trees that are combined to estimate subsystem and system risks and the uncertainties. A commonly used tool in complex engineered system, this paper is a wonderful illustration of how to capture uncertainty in risk assessment.
7. Kaplan S. The two stage poisson type problem in probabilistic risk analysis. 1985; 5(3): 227-230.
This comment compares the strengths and weaknesses of Kaplan’s two-stage Bayesian method and one proposed by F.H. Frohner using a two-stage poisson distribution for estimating failure rates. The authors concur about the importance of the issue but not on the best solution. Both approaches are used in risk analysis.
8. Kaplan S, Lin JC. An improved condensation procedure in discrete probability distribution calculations. 1987;7(1):15-19.
This fascinating paper explores different approach to estimating discrete probability distributions.
9. Murray M, Chambers D, Knapp R, Kaplan S. Estimation of long term risk from Canadian uranium mill tailings. 1987; 7(3):287 298.
Uranium mine tailings are a long term hazard. Using the Kaplan and Garrick trilogy and event trees, the authors examine the risk for a 1000 year containment facility. These containment cells are found in various locations and risk analyses appear in environmental impact statements that require excavation or other activities near them.
10. Kaplan S, Fleming J. On the use of the cause table in handling common cause events in system analysis.1987;7(4):531 537.
Fault and event trees can rapidly multiply in complex engineered systems. Kaplan and Fleming suggest and illustrate a method of reducing the number of trees and calculations.
11. Kaplan S. The words of risk analysis. 1997; 17(4):407-417.
The plenary session of the 1996 annual meeting of SRA explored definitions used in risk analysis. This paper is a transcript of Stan Kaplan’s effort to contribute to a simple and single language that we would all use. This effort is ongoing. I have found at least 17 widely used definitions of “risk” and of key terms such as vulnerable, resilience, and so on remain the subject of debate in the field and the pages of our journal.
12. Gray G, Allen J, Burmaster D, Gage S, HamittJ, Kaplan S, Keeney R, Morse J, North W, Nyrop J, Stahevitch A, Willaimslau R. Principles for conduct of pest risk analyses: report of an expert workshop. 1998;18(6):773-780.
International treaties (GATT and NAFTA) require risk analysis to support quarantine decisions. This papers summarizes a workshop that evaluated the application of PRA to pest management. Some of the invited attendees, like Stan Kaplan, were members of SRA and others were pest control experts. The article presents principles they jointly developed. Recent events across the globe highlight the need for these efforts.
13. Kaplan S, Burmaster D. How, when, why to use all of the evidence. 1999; 19(1):55-62.
The authors summarize a discussion of key questions about how to use evidence in risk analysis. Every risk analysis has to answer these questions, and this very simple essay offers some interesting and practical suggestions.
14. Hoffman F, Kaplan S. Beyond the domain of direct observation: How to specify a probability distribution that represents the “state of knowledge” about uncertain inputs. 1999; 19(1):131-134.
Hoffman and Kaplan describe and illustrate methods for obtaining distributions for uncertain inputs, and how new information can be inserted into models to update probability distributions.
15. Garrick BJ, Kaplan S. A decision theory perspective on the disposal of high-level radioactive waste: The role of probabilistic performance assessment. 1999; 19(5):903-914.
Garrick and Kaplan examine high level nuclear waste storage. They characterize the issue as eminently suitable to risk analysis and address the proposed Yucca Mountain project. The policy debate about how to dispose of high level waste, including what to do with the Yucca Mountain project, is the subject of ongoing debate.
16. Kaplan S. Comment on the paper "Combining Probability Distributions from Experts in Risk Analysis" by Clemen and Winkler. 2000; 20(2):155-156.
This note in response to a paper suggests how analysts should use expert data in generating probability distributions.
17. Kaplan S, Haimes Y, Garrick BJ. Fitting hierarchical, holographic modeling into the theory of 'scenario structuring and aresulting refinement to the quantitative definition of risk. 2001; 21(5):807.
This fascinating note suggests that events, the first part of the risk assessment trilogy, should be modified to replace singular scenarios with a set of tools that allow approximation of the actual set of events.
18. Haimes Y, Kaplan S, Lambert J. Risk filtering, ranking, and management framework using hierarchical holographic modeling. 2002;22(2):383-397.
The authors offer a framework to identify, prioritize, assess and manage the numerous scenarios in a large built system. Their approach has been presented in many articles in our journal and is used in risk analysis.