Risk Analysis: An International Journal

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Table of Contents for Risk Analysis. List of articles from both the latest and EarlyView issues.
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From Ideal to Real Risk: Philosophy of Causation Meets Risk Analysis

7 March 2019 - 1:13pm
Abstract

A question has been raised in recent years as to whether the risk field, including analysis, assessment, and management, ought to be considered a discipline on its own. As suggested by Terje Aven, unification of the risk field would require a common understanding of basic concepts, such as risk and probability; hence, more discussion is needed of what he calls “foundational issues.” In this article, we show that causation is a foundational issue of risk, and that a proper understanding of it is crucial. We propose that some old ideas about the nature of causation must be abandoned in order to overcome certain persisting challenges facing risk experts over the last decade. In particular, we discuss the challenge of including causally relevant knowledge from the local context when studying risk. Although it is uncontroversial that the receptor plays an important role for risk evaluations, we show how the implementation of receptor‐based frameworks is hindered by methodological shortcomings that can be traced back to Humean orthodoxies about causation. We argue that the first step toward the development of frameworks better suited to make realistic risk predictions is to reconceptualize causation, by examining a philosophical alternative to the Humean understanding. In this article, we show how our preferred account, causal dispositionalism, offers a different perspective in how risk is evaluated and understood.

Terrorism Risk Assessment, Recollection Bias, and Public Support for Counterterrorism Policy and Spending

7 March 2019 - 1:13pm
Abstract

Recollection bias (RB) refers to the phenomenon whereby after an adverse event people report that their risk assessment about a similar future event is presently no higher than their recollection of their pre‐event risk assessment. While previous research has outlined this theoretical construct and generated important empirical findings, there were some limitations. We design and employ a new national representative survey to address these limitations in this study. We examine the existence and persistence of RB among the general public in the context of a number of domestic and international terrorist attacks. We further examine the socioeconomic and political base of RB and the influences of RB on a wide range of citizens’ counterterrorism policy preferences. Our data analyses reveal strong evidence showing the occurrence of RB and its persistence across various forms of terrorism risk. With regard to the socioeconomic and political base, we find that females, older people, political conservatives, and Republicans are less likely to be subject to RB. For the effects of RB on public counterterrorism policy preferences, our analyses demonstrate that this bias significantly dampens public support for a wide range of preventive policy measures and government anti‐terrorism spending. Overall, our study, based on a national representative sample and an extended survey design, provides robust evidence of RB in terrorism risk assessment, and adds further evidence to support the idea that RB is likely a generalizable phenomenon. Implications and suggestions for future research are discussed in the conclusion.

Media Disaster Reporting Effects on Public Risk Perception and Response to Escalating Tornado Warnings: A Natural Experiment

7 March 2019 - 1:13pm
Abstract

Previous research has evaluated public risk perception and response to a natural hazards in various settings; however, most of these studies were conducted either with a single scenario or after a natural disaster struck. To better understand the dynamic relationships among affect, risk perception, and behavioral intentions related to natural disasters, the current study implements a simulation scenario with escalating weather intensity, and includes a natural experiment allowing comparison of public response before and after a severe tornado event with extensive coverage by the national media. The current study also manipulated the display of warning information, and investigated whether the warning system display format influences public response. Results indicate that (1) affect, risk perception, and behavioral intention escalated as weather conditions deteriorated, (2) responses at previous stages predicted responses at subsequent stages of storm progression, and (3) negative affect predicted risk perception. Moreover, risk perception and behavioral intention were heightened after exposure to the media coverage of an actual tornado disaster. However, the display format manipulation did not influence behavioral responses. The current study provides insight regarding public perception of predisaster warnings and the influence of exposure to media coverage of an actual disaster event.

A Data‐Driven Approach to Assessing Supply Inadequacy Risks Due to Climate‐Induced Shifts in Electricity Demand

7 March 2019 - 1:13pm
Abstract

The U.S. electric power system is increasingly vulnerable to the adverse impacts of extreme climate events. Supply inadequacy risk can result from climate‐induced shifts in electricity demand and/or damaged physical assets due to hydro‐meteorological hazards and climate change. In this article, we focus on the risks associated with the unanticipated climate‐induced demand shifts and propose a data‐driven approach to identify risk factors that render the electricity sector vulnerable in the face of future climate variability and change. More specifically, we have leveraged advanced supervised learning theory to identify the key predictors of climate‐sensitive demand in the residential, commercial, and industrial sectors. Our analysis indicates that variations in mean dew point temperature is the common major risk factor across all the three sectors. We have also conducted a statistical sensitivity analysis to assess the variability in the projected demand as a function of the key climate risk factor. We then propose the use of scenario‐based heat maps as a tool to communicate the inadequacy risks to stakeholders and decisionmakers. While we use the state of Ohio as a case study, our proposed approach is equally applicable to all other states.

Quantal Risk Assessment Database: A Database for Exploring Patterns in Quantal Dose‐Response Data in Risk Assessment and its Application to Develop Priors for Bayesian Dose‐Response Analysis

7 March 2019 - 1:13pm
Abstract

Quantitative risk assessments for physical, chemical, biological, occupational, or environmental agents rely on scientific studies to support their conclusions. These studies often include relatively few observations, and, as a result, models used to characterize the risk may include large amounts of uncertainty. The motivation, development, and assessment of new methods for risk assessment is facilitated by the availability of a set of experimental studies that span a range of dose‐response patterns that are observed in practice. We describe construction of such a historical database focusing on quantal data in chemical risk assessment, and we employ this database to develop priors in Bayesian analyses. The database is assembled from a variety of existing toxicological data sources and contains 733 separate quantal dose‐response data sets. As an illustration of the database's use, prior distributions for individual model parameters in Bayesian dose‐response analysis are constructed. Results indicate that including prior information based on curated historical data in quantitative risk assessments may help stabilize eventual point estimates, producing dose‐response functions that are more stable and precisely estimated. These in turn produce potency estimates that share the same benefit. We are confident that quantitative risk analysts will find many other applications and issues to explore using this database.

Conflict of Interest Mitigation Procedures May Have Little Influence on the Perceived Procedural Fairness of Risk‐Related Research

7 March 2019 - 1:13pm
Abstract

Two between‐subject experiments explored perceived conflict of interest (COI)—operationalized as perceived procedural unfairness—in a hypothetical public–private research partnership to study the health risks of trans fats. Perceived fairness was measured as subjects’ perceptions that health researchers would be willing to listen to a range of voices and minimize bias (i.e., COI) in the context of a research project. Experiment 1 (n = 1,263) randomly assigned research subjects to a partnership that included (1) a combination of an industry partner, a university partner, and a nongovernmental organization (NGO) partner; and (2) one of three processes aimed at mitigating the potential for COI to harm the quality of the research. The procedures included an arm's‐length process meant to keep the university‐based research team from being influenced by the other partners, an independent advisory board to oversee the project, and a commitment to making all data and analyses openly available. The results suggest that having an industry partner has substantial negative effects on perceived fairness and that the benefit of employing a single COI‐mitigation process may be relatively small. Experiment 2 (n = 1,076) assessed a partnership of (1) a university and either an NGO or industry partner and (b) zero, one, two, or three of the three COI‐mitigation procedures. Results suggest there is little value in combining COI‐mitigation procedures. The study has implications for those who aim to foster confidence in scientific findings for which the underlying research may benefit from industry funding.

Validation of Quantitative Microbial Risk Assessment Using Epidemiological Data from Outbreaks of Waterborne Gastrointestinal Disease

7 March 2019 - 1:13pm
Abstract

The assumptions underlying quantitative microbial risk assessment (QMRA) are simple and biologically plausible, but QMRA predictions have never been validated for many pathogens. The objective of this study was to validate QMRA predictions against epidemiological measurements from outbreaks of waterborne gastrointestinal disease. I screened 2,000 papers and identified 12 outbreaks with the necessary data: disease rates measured using epidemiological methods and pathogen concentrations measured in the source water. Eight of the 12 outbreaks were caused by Cryptosporidium, three by Giardia, and one by norovirus. Disease rates varied from 5.5 × 10−6 to 1.1 × 10−2 cases/person‐day, and reported pathogen concentrations varied from 1.2 × 10−4 to 8.6 × 102 per liter. I used these concentrations with single‐hit dose–response models for all three pathogens to conduct QMRA, producing both point and interval predictions of disease rates for each outbreak. Comparison of QMRA predictions to epidemiological measurements showed good agreement; interval predictions contained measured disease rates for 9 of 12 outbreaks, with point predictions off by factors of 1.0–120 (median = 4.8). Furthermore, 11 outbreaks occurred at mean doses of less than 1 pathogen per exposure. Measured disease rates for these outbreaks were clearly consistent with a single‐hit model, and not with a “two‐hit” threshold model. These results demonstrate the validity of QMRA for predicting disease rates due to Cryptosporidium and Giardia.

From the Editors

7 March 2019 - 1:13pm
Risk Analysis, Volume 39, Issue 3, Page 509-510, March 2019.

Issue Information ‐ TOC

7 March 2019 - 1:13pm
Risk Analysis, Volume 39, Issue 3, March 2019.

Time‐Varying Risk Measurement for Ship Collision Prevention

7 March 2019 - 12:44pm
Abstract

We propose an innovative time‐varying collision risk (TCR) measurement for ship collision prevention in this article. The proposed measurement considers the level of danger of the approaching ships and the capability of a ship to prevent collisions. We define the TCR as the probability of the overlap of ships’ positions in the future, given the uncertainty of maneuvers. Two sets are identified: (1) the velocity obstacle set as the maneuvers of the own ship that lead to collisions with target ships, and (2) the reachable velocity set as the maneuvers that the own ship can reach regarding its maneuverability. We then measure the TCR as the time‐dependent percentage of overlap between these two sets. Several scenarios are presented to illustrate how the proposed measurement identifies the time‐varying risk levels, and how the approach can be used as an intuitively understandable tool for collision avoidance.

Time‐Varying Risk Measurement for Ship Collision Prevention

7 March 2019 - 12:44pm
Abstract

We propose an innovative time‐varying collision risk (TCR) measurement for ship collision prevention in this article. The proposed measurement considers the level of danger of the approaching ships and the capability of a ship to prevent collisions. We define the TCR as the probability of the overlap of ships’ positions in the future, given the uncertainty of maneuvers. Two sets are identified: (1) the velocity obstacle set as the maneuvers of the own ship that lead to collisions with target ships, and (2) the reachable velocity set as the maneuvers that the own ship can reach regarding its maneuverability. We then measure the TCR as the time‐dependent percentage of overlap between these two sets. Several scenarios are presented to illustrate how the proposed measurement identifies the time‐varying risk levels, and how the approach can be used as an intuitively understandable tool for collision avoidance.

Farmers’ Risk‐Based Decision Making Under Pervasive Uncertainty: Cognitive Thresholds and Hazy Hedging

4 March 2019 - 6:19pm
Abstract

Researchers in judgment and decision making have long debunked the idea that we are economically rational optimizers. However, problematic assumptions of rationality remain common in studies of agricultural economics and climate change adaptation, especially those that involve quantitative models. Recent movement toward more complex agent‐based modeling provides an opportunity to reconsider the empirical basis for farmer decision making. Here, we reconceptualize farmer decision making from the ground up, using an in situ mental models approach to analyze weather and climate risk management. We assess how large‐scale commercial grain farmers in South Africa (n = 90) coordinate decisions about weather, climate variability, and climate change with those around other environmental, agronomic, economic, political, and personal risks that they manage every day. Contrary to common simplifying assumptions, we show that these farmers tend to satisfice rather than optimize as they face intractable and multifaceted uncertainty; they make imperfect use of limited information; they are differently averse to different risks; they make decisions on multiple time horizons; they are cautious in responding to changing conditions; and their diverse risk perceptions contribute to important differences in individual behaviors. We find that they use two important nonoptimizing strategies, which we call cognitive thresholds and hazy hedging, to make practical decisions under pervasive uncertainty. These strategies, evident in farmers' simultaneous use of conservation agriculture and livestock to manage weather risks, are the messy in situ performance of naturalistic decision‐making techniques. These results may inform continued research on such behavioral tendencies in narrower lab‐ and modeling‐based studies.

Farmers’ Risk‐Based Decision Making Under Pervasive Uncertainty: Cognitive Thresholds and Hazy Hedging

4 March 2019 - 6:19pm
Abstract

Researchers in judgment and decision making have long debunked the idea that we are economically rational optimizers. However, problematic assumptions of rationality remain common in studies of agricultural economics and climate change adaptation, especially those that involve quantitative models. Recent movement toward more complex agent‐based modeling provides an opportunity to reconsider the empirical basis for farmer decision making. Here, we reconceptualize farmer decision making from the ground up, using an in situ mental models approach to analyze weather and climate risk management. We assess how large‐scale commercial grain farmers in South Africa (n = 90) coordinate decisions about weather, climate variability, and climate change with those around other environmental, agronomic, economic, political, and personal risks that they manage every day. Contrary to common simplifying assumptions, we show that these farmers tend to satisfice rather than optimize as they face intractable and multifaceted uncertainty; they make imperfect use of limited information; they are differently averse to different risks; they make decisions on multiple time horizons; they are cautious in responding to changing conditions; and their diverse risk perceptions contribute to important differences in individual behaviors. We find that they use two important nonoptimizing strategies, which we call cognitive thresholds and hazy hedging, to make practical decisions under pervasive uncertainty. These strategies, evident in farmers' simultaneous use of conservation agriculture and livestock to manage weather risks, are the messy in situ performance of naturalistic decision‐making techniques. These results may inform continued research on such behavioral tendencies in narrower lab‐ and modeling‐based studies.

Integrating Stakeholder Mapping and Risk Scenarios to Improve Resilience of Cyber‐Physical‐Social Networks

1 March 2019 - 8:00pm
Abstract

The future of energy mobility involves networks of users, operators, organizations, vehicles, charging stations, communications, materials, transportation corridors, points of service, and so on. The integration of smart grids with plug‐in electric vehicle technologies has societal and commercial advantages that include improving grid stability, minimizing dependence on nonrenewable fuels, reducing vehicle emissions, and reducing the cost of electric vehicle ownership. However, ineffective or delayed participation of particular groups of stakeholders could disrupt industry plans and delay the desired outcomes. This article develops a framework to address enterprise resilience for two modes of disruptions—the first being the influence of scenarios on priorities and the second being the influence of multiple groups of stakeholders on priorities. The innovation of this study is to obtain the advantages of integrating two recent approaches: scenario‐based preferences modeling and stakeholder mapping. Public agencies, grid operators, plug‐in electric vehicle owners, and vehicle manufacturers are the four groups of stakeholders that are considered in this framework, along with the influence of four scenarios on priorities.

Integrating Stakeholder Mapping and Risk Scenarios to Improve Resilience of Cyber‐Physical‐Social Networks

1 March 2019 - 8:00pm
Abstract

The future of energy mobility involves networks of users, operators, organizations, vehicles, charging stations, communications, materials, transportation corridors, points of service, and so on. The integration of smart grids with plug‐in electric vehicle technologies has societal and commercial advantages that include improving grid stability, minimizing dependence on nonrenewable fuels, reducing vehicle emissions, and reducing the cost of electric vehicle ownership. However, ineffective or delayed participation of particular groups of stakeholders could disrupt industry plans and delay the desired outcomes. This article develops a framework to address enterprise resilience for two modes of disruptions—the first being the influence of scenarios on priorities and the second being the influence of multiple groups of stakeholders on priorities. The innovation of this study is to obtain the advantages of integrating two recent approaches: scenario‐based preferences modeling and stakeholder mapping. Public agencies, grid operators, plug‐in electric vehicle owners, and vehicle manufacturers are the four groups of stakeholders that are considered in this framework, along with the influence of four scenarios on priorities.

Stochastic Counterfactual Risk Analysis for the Vulnerability Assessment of Cyber‐Physical Attacks on Electricity Distribution Infrastructure Networks

27 February 2019 - 10:42am
Abstract

In December 2015, a cyber‐physical attack took place on the Ukrainian electricity distribution network. This is regarded as one of the first cyber‐physical attacks on electricity infrastructure to have led to a substantial power outage and is illustrative of the increasing vulnerability of Critical National Infrastructure to this type of malicious activity. Few data points, coupled with the rapid emergence of cyber phenomena, has held back the development of resilience analytics of cyber‐physical attacks, relative to many other threats. We propose to overcome data limitations by applying stochastic counterfactual risk analysis as part of a new vulnerability assessment framework. The method is developed in the context of the direct and indirect socioeconomic impacts of a Ukrainian‐style cyber‐physical attack taking place on the electricity distribution network serving London and its surrounding regions. A key finding is that if decision‐makers wish to mitigate major population disruptions, then they must invest resources more‐or‐less equally across all substations, to prevent the scaling of a cyber‐physical attack. However, there are some substations associated with higher economic value due to their support of other Critical National Infrastructures assets, which justifies the allocation of additional cyber security investment to reduce the chance of cascading failure. Further cyber‐physical vulnerability research must address the tradeoffs inherent in a system made up of multiple institutions with different strategic risk mitigation objectives and metrics of value, such as governments, infrastructure operators, and commercial consumers of infrastructure services.

Stochastic Counterfactual Risk Analysis for the Vulnerability Assessment of Cyber‐Physical Attacks on Electricity Distribution Infrastructure Networks

27 February 2019 - 10:42am
Abstract

In December 2015, a cyber‐physical attack took place on the Ukrainian electricity distribution network. This is regarded as one of the first cyber‐physical attacks on electricity infrastructure to have led to a substantial power outage and is illustrative of the increasing vulnerability of Critical National Infrastructure to this type of malicious activity. Few data points, coupled with the rapid emergence of cyber phenomena, has held back the development of resilience analytics of cyber‐physical attacks, relative to many other threats. We propose to overcome data limitations by applying stochastic counterfactual risk analysis as part of a new vulnerability assessment framework. The method is developed in the context of the direct and indirect socioeconomic impacts of a Ukrainian‐style cyber‐physical attack taking place on the electricity distribution network serving London and its surrounding regions. A key finding is that if decision‐makers wish to mitigate major population disruptions, then they must invest resources more‐or‐less equally across all substations, to prevent the scaling of a cyber‐physical attack. However, there are some substations associated with higher economic value due to their support of other Critical National Infrastructures assets, which justifies the allocation of additional cyber security investment to reduce the chance of cascading failure. Further cyber‐physical vulnerability research must address the tradeoffs inherent in a system made up of multiple institutions with different strategic risk mitigation objectives and metrics of value, such as governments, infrastructure operators, and commercial consumers of infrastructure services.

Comments to Orri Stefánsson's Paper on the Precautionary Principle

21 February 2019 - 5:25pm
Risk Analysis, EarlyView.

Comments to Orri Stefánsson's Paper on the Precautionary Principle

21 February 2019 - 5:25pm
Risk Analysis, EarlyView.

On the Limits of the Precautionary Principle

21 February 2019 - 5:23pm
Abstract

The precautionary principle (PP) is an influential principle of risk management. It has been widely introduced into environmental legislation, and it plays an important role in most international environmental agreements. Yet, there is little consensus on precisely how to understand and formulate the principle. In this article I prove some impossibility results for two plausible formulations of the PP as a decision‐rule. These results illustrate the difficulty in making the PP consistent with the acceptance of any tradeoffs between catastrophic risks and more ordinary goods. How one interprets these results will, however, depend on one's views and commitments. For instance, those who are convinced that the conditions in the impossibility results are requirements of rationality may see these results as undermining the rationality of the PP. But others may simply take these results to identify a set of purported rationality conditions that defenders of the PP should not accept, or to illustrate types of situations in which the principle should not be applied.

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