Suchergebnisse

The aim of this expository survey on Bayesian simulation is to stimulate more work in the area by decision analysts. We discuss the main areas of research performed thus far, including input analysis, propagation and estimation of output uncertainty, output analysis, making decisions with simulations, selecting the best simulated system, and applications of Bayesian simulation methods. Throughout, we offer avenues of future research in Bayesian simulation that may be of interest to decision analysts.

The Bayesian approach to combining expert opinions is well developed, providing a decision maker's posterior beliefs after receiving advice from people with deep knowledge in a given subject. A necessary part of these models is the inclusion of dependencies between the experts' judgments, often justified by an overlap in the information on which the experts base their judgments. In this paper, we propose a hierarchical structure different than those previously proposed, where the mixing distribution is treated nonparametrically with a Dirichlet process. This makes our overall model a Dirichlet process mixture and allows for experts' model parameters to be equal in the mixture. We apply this approach to published expert judgment data, demonstrating that the decision maker's posterior distributions on the quantities of interest are not restricted to specific parametric forms, even allowing for multiple modes, and are thus more intuitively appealing.

AbstractAttacker/defender models have primarily assumed that each decisionmaker optimizes the cost of the damage inflicted and its economic repercussions from their own perspective. Two streams of recent research have sought to extend such models. One stream suggests that it is more realistic to consider attackers with multiple objectives, but this research has not included the adaption of the terrorist with multiple objectives to defender actions. The other stream builds off experimental studies that show that decisionmakers deviate from optimal rational behavior. In this article, we extend attacker/defender models to incorporate multiple objectives that a terrorist might consider in planning an attack. This includes the tradeoffs that a terrorist might consider and their adaption to defender actions. However, we must also consider experimental evidence of deviations from the rationality assumed in the commonly used expected utility model in determining such adaption. Thus, we model the attacker's behavior using multiattribute prospect theory to account for the attacker's multiple objectives and deviations from rationality. We evaluate our approach by considering an attacker with multiple objectives who wishes to smuggle radioactive material into the United States and a defender who has the option to implement a screening process to hinder the attacker. We discuss the problems with implementing such an approach, but argue that research in this area must continue to avoid misrepresenting terrorist behavior in determining optimal defensive actions.

Counterterrorism decisions have been an intense area of research in recent years. Both decision analysis and game theory have been used to model such decisions, and more recently approaches have been developed that combine the techniques of the two disciplines. However, each of these approaches assumes that the attacker is maximizing its utility. Experimental research shows that human beings do not make decisions by maximizing expected utility without aid, but instead deviate in specific ways such as loss aversion or likelihood insensitivity. In this article, we modify existing methods for counterterrorism decisions. We keep expected utility as the defender's paradigm to seek for the rational decision, but we use prospect theory to solve for the attacker's decision to descriptively model the attacker's loss aversion and likelihood insensitivity. We study the effects of this approach in a critical decision, whether to screen containers entering the United States for radioactive materials. We find that the defender's optimal decision is sensitive to the attacker's levels of loss aversion and likelihood insensitivity, meaning that understanding such descriptive decision effects is important in making such decisions.

Considerable research has been performed to develop leading indicators of safety performance. We use value-focused thinking to understand the objectives and evaluation measures that frame a particular safety-related decision within an organization. These decisions are part of the safety culture. Our research partners were two oil shipping companies; we surveyed crewmembers on their tankers to evaluate performance in each decision objective on their vessel. We demonstrate that measurements of the achievement of these objectives are related to future safety performance and thus provide leading indicators of safety.

In recent years, Customs and Border Protection has installed radiation sensors to screen cargo containers entering the United States. They are concerned that terrorists could use containers to smuggle radiological material into the country and carry out attacks with dirty bombs or a nuclear device. Recent studies have questioned the value of improving this screening system with new sensor technology. The cost of delays caused by frequent false alarms outweighs any reduction in the probability of an attack in an expected-cost analysis. We extend existing methodology in three ways to demonstrate how additional factors affect the value of screening investments. We examine the effect that screening has in discouraging terrorists. We model multiple levels of screening. Finally, we consider additional objectives beyond cost. We find that the conclusion about screening depends on key inputs to the probability model (reflecting uncertainties) and to the value function (reflecting the stakeholders' fundamental objectives).

Several major risk studies have been performed in recent years in the maritime transportation domain. These studies have had significant impact on management practices in the industry. The first, the Prince William Sound risk assessment, was reviewed by the National Research Council and found to be promising but incomplete, as the uncertainty in its results was not assessed. The difficulty in assessing this uncertainty is the different techniques that need to be used to model risk in this dynamic and data‐scarce application area. In previous articles, we have developed the two pieces of methodology necessary to assess uncertainty in maritime risk assessment, a Bayesian simulation of the occurrence of situations with accident potential and a Bayesian multivariate regression analysis of the relationship between factors describing these situations and expert judgments of accident risk. In this article, we combine the methods to perform a full‐scale assessment of risk and uncertainty for two case studies. The first is an assessment of the effects of proposed ferry service expansions in San Francisco Bay. The second is an assessment of risk for the Washington State Ferries, the largest ferry system in the United States.

Recent work in the assessment of risk in maritime transportation systems has used simulation‐based probabilistic risk assessment techniques. In the Prince William Sound and Washington State Ferries risk assessments, the studies' recommendations were backed up by estimates of their impact made using such techniques and all recommendations were implemented. However, the level of uncertainty about these estimates was not available, leaving the decisionmakers unsure whether the evidence was sufficient to assess specific risks and benefits. The first step toward assessing the impact of uncertainty in maritime risk assessments is to model the uncertainty in the simulation models used. In this article, a study of the impact of proposed ferry service expansions in San Francisco Bay is used as a case study to demonstrate the use of Bayesian simulation techniques to propagate uncertainty throughout the analysis. The conclusions drawn in the original study are shown, in this case, to be robust to the inherent uncertainties. The main intellectual merit of this work is the development of Bayesian simulation technique to model uncertainty in the assessment of maritime risk. However, Bayesian simulations have been implemented only as theoretical demonstrations. Their use in a large, complex system may be considered state of the art in the field of computational sciences.

We develop a Bayesian multivariate analysis of expert judgment elicited using an extended form of pairwise comparisons. The method can be used to estimate the effect of multiple factors on the probability of an event and can be applied in risk analysis and other decision problems. The model, which parallels Bayesian models for combining expert judgments, provides predictions of the quantity of interest that incorporate dependencies among the various experts. In this form we may learn about the dependencies between the experts from their responses. The analysis is applied to a real data set of expert judgments elicited during the Washington State Ferries Risk Assessment. The effect of the statistical dependence among experts is compared to an analysis assuming independence among them.

The objective of this special issue is to introduce a new theme, the use of game and decision theory in reliability modeling and risk analysis, which was the focus of the First Symposium on Games and Decisions in Reliability and Risk (GDRR) held at the George Washington University on May 27–28, 2009. The issue considered papers presented at the Second Symposium on GDRR ( http://www.mi.imati.cnr.it/conferences/gdrr11.html ), held at the Hotel Villa Carlotta, Belgirate (VB), Lake Maggiore, Italy, on May 19–21, 2011, and also was open to the public for submission of papers relevant to the theme. The contributors to the special issue include Sevillano, Rios Insua, and Rios; Delquié; Foschi and Spizzichino; French; Bhattacharjya and Deleris; Bhattacharjya and Shachter; Lejeune; Cheung and Zhuang; Smith and Dodd; Sri Bhashyam and Montibeller; and Paté-Cornell.

Decision analysis is recognized as the right way to make risk management decisions, using probabilistic techniques to assess the accident risk. It is also accepted that the decisions that individuals in the organization make affect the likelihood of an accident and thus managerial and organizational factors should be included in the risk modeling process. However, decision analytic techniques have not been used to understand the decisions that are made by these individuals. The initial domain for this research is marine transportation. We use the framework of value‐focused thinking in order to understand safety decisions made within our research partner organization, a major domestic oil tanker operator. We describe the results of interviews held with managers and employees from this organization. Through these interviews, we sought to understand the values these experts apply in their roles within the organization and the objectives they seek to achieve to contribute to its overall safety performance. The end result is a framework that not only portrays the fundamental objectives of safe operations for various roles in the organization, but also interconnects these different decision contexts. We believe that this approach is fundamentally different from those used in previous work and that this is an interesting application of value‐focused thinking.

This paper describes the use of multiple-objective decision analysis to qualitatively and quantitatively assess the quality of an endangered watershed and guide future efforts to improve the quality of the watershed. The Upham Brook Watershed is an urban watershed that lies at the interface of declining inner-city Richmond, Virginia, and growth-oriented Henrico County. A section of stream within the watershed has been identified as so dangerously polluted that it threatens the health of the residents who live within the watershed boundaries. With funding provided by the National Science Foundation, the Upham Brook Watershed project committee was formed to address the quality of the Upham Brook Watershed; it consisted of experts from multiple disciplines: stream ecology, environmental policy, water policy, ground and surface water hydrology and quality, aquatic biology, political science, sociology, citizen participation, community interaction, psychology, and decision and risk analysis. Each member's values and goals were brought together using a watershed management framework to meet the overall objective of the committee: to maximize the quality of the Upham Brook Watershed. The resulting model was used to identify the largest value gaps and to identify future programs needed to improve the quality of the watershed.

The state of Washington operates the largest passenger vessel ferry system in the United States. In part due to the introduction of high‐speed ferries, the state of Washington established an independent blue‐ribbon panel to assess the adequacy of requirements for passenger and crew safety aboard the Washington state ferries. On July 9, 1998, the Blue Ribbon Panel on Washington State Ferry Safety engaged a consultant team from The George Washington University and Rensselaer Polytechnic Institute/Le Moyne College to assess the adequacy of passenger and crew safety in the Washington state ferry (WSF) system, to evaluate the level of risk present in the WSF system, and to develop recommendations for prioritized risk reduction measures, which, once implemented, can improve the level of safety in the WSF system. The probability of ferry collisions in the WSF system was assessed using a dynamic simulation methodology that extends the scope of available data with expert judgment. The potential consequences of collisions were modeled in order to determine the requirements for onboard and external emergency response procedures and equipment. The methodology was used to evaluate potential risk reduction measures and to make detailed risk management recommendations to the blue‐ribbon panel and the Washington State Transportation Commission.

This issue's "From the Editors" column is coauthored with all the associate editors to emphasize their major role in the leadership of the journal. Our first article is on "Proper Scoring Rules, Dominated Forecasts, and Coherence," by Mark J. Schervish, Teddy Seidenfeld, and Joseph B. Kadane. In the second article, Jason R. W. Merrick provides an overview of "Bayesian Simulation and Decision Analysis: An Expository Survey." Our third article, by Barry R. Cobb and Atin Basuchoudhary, is on "A Decision Analysis Approach to Solving the Signaling Game." The next article, by Robert T. Clemen and James E. Smith, is "On the Choice of Baselines in Multiattribute Portfolio Analysis: A Cautionary Note." In our final article, Russ Garber presents "An Interview with Ronald A. Howard."