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.
Updated: 48 min 38 sec ago

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.

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.

Effect of Providing the Uncertainty Information About a Tornado Occurrence on the Weather Recipients’ Cognition and Protective Action: Probabilistic Hazard Information Versus Deterministic Warnings

21 February 2019 - 5:23pm
Abstract

Currently, a binary alarm system is used in the United States to issue deterministic warning polygons in case of tornado events. To enhance the effectiveness of the weather information, a likelihood alarm system, which uses a tool called probabilistic hazard information (PHI), is being developed at National Severe Storms Laboratory to issue probabilistic information about the threat. This study aims to investigate the effects of providing the uncertainty information about a tornado occurrence through the PHI's graphical swath on laypeople's concern, fear, and protective action, as compared with providing the warning information with the deterministic polygon. The displays of color‐coded swaths and deterministic polygons were shown to subjects. Some displays had a blue background denoting the probability of any tornado formation in the general area. Participants were asked to report their levels of concern, fear, and protective action at randomly chosen locations within each of seven designated levels on each display. Analysis of a three‐stage nested design showed that providing the uncertainty information via the PHI would appropriately increase recipients’ levels of concern, fear, and protective action in highly dangerous scenarios, with a more than 60% chance of being affected by the threat, as compared with deterministic polygons. The blue background and the color‐coding type did not have a significant effect on the people's cognition of the threat and reaction to it. This study shows that using a likelihood alarm system leads to more conscious decision making by the weather information recipients and enhances the system safety.

Effect of Providing the Uncertainty Information About a Tornado Occurrence on the Weather Recipients’ Cognition and Protective Action: Probabilistic Hazard Information Versus Deterministic Warnings

21 February 2019 - 5:23pm
Abstract

Currently, a binary alarm system is used in the United States to issue deterministic warning polygons in case of tornado events. To enhance the effectiveness of the weather information, a likelihood alarm system, which uses a tool called probabilistic hazard information (PHI), is being developed at National Severe Storms Laboratory to issue probabilistic information about the threat. This study aims to investigate the effects of providing the uncertainty information about a tornado occurrence through the PHI's graphical swath on laypeople's concern, fear, and protective action, as compared with providing the warning information with the deterministic polygon. The displays of color‐coded swaths and deterministic polygons were shown to subjects. Some displays had a blue background denoting the probability of any tornado formation in the general area. Participants were asked to report their levels of concern, fear, and protective action at randomly chosen locations within each of seven designated levels on each display. Analysis of a three‐stage nested design showed that providing the uncertainty information via the PHI would appropriately increase recipients’ levels of concern, fear, and protective action in highly dangerous scenarios, with a more than 60% chance of being affected by the threat, as compared with deterministic polygons. The blue background and the color‐coding type did not have a significant effect on the people's cognition of the threat and reaction to it. This study shows that using a likelihood alarm system leads to more conscious decision making by the weather information recipients and enhances the system safety.

Comment: The Precautionary Principle and Judgment Aggregation

21 February 2019 - 5:22pm
Risk Analysis, EarlyView.

Comment: The Precautionary Principle and Judgment Aggregation

21 February 2019 - 5:22pm
Risk Analysis, EarlyView.

Reply

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

Reply

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

An Optimization‐Based Framework for the Identification of Vulnerabilities in Electric Power Grids Exposed to Natural Hazards

19 February 2019 - 6:47pm
Abstract

This article proposes a novel mathematical optimization framework for the identification of the vulnerabilities of electric power infrastructure systems (which is a paramount example of critical infrastructure) due to natural hazards. In this framework, the potential impacts of a specific natural hazard on an infrastructure are first evaluated in terms of failure and recovery probabilities of system components. Then, these are fed into a bi‐level attacker–defender interdiction model to determine the critical components whose failures lead to the largest system functionality loss. The proposed framework bridges the gap between the difficulties of accurately predicting the hazard information in classical probability‐based analyses and the over conservatism of the pure attacker–defender interdiction models. Mathematically, the proposed model configures a bi‐level max‐min mixed integer linear programming (MILP) that is challenging to solve. For its solution, the problem is casted into an equivalent one‐level MILP that can be solved by efficient global solvers. The approach is applied to a case study concerning the vulnerability identification of the georeferenced RTS24 test system under simulated wind storms. The numerical results demonstrate the effectiveness of the proposed framework for identifying critical locations under multiple hazard events and, thus, for providing a useful tool to help decisionmakers in making more‐informed prehazard preparation decisions.

An Optimization‐Based Framework for the Identification of Vulnerabilities in Electric Power Grids Exposed to Natural Hazards

19 February 2019 - 6:47pm
Abstract

This article proposes a novel mathematical optimization framework for the identification of the vulnerabilities of electric power infrastructure systems (which is a paramount example of critical infrastructure) due to natural hazards. In this framework, the potential impacts of a specific natural hazard on an infrastructure are first evaluated in terms of failure and recovery probabilities of system components. Then, these are fed into a bi‐level attacker–defender interdiction model to determine the critical components whose failures lead to the largest system functionality loss. The proposed framework bridges the gap between the difficulties of accurately predicting the hazard information in classical probability‐based analyses and the over conservatism of the pure attacker–defender interdiction models. Mathematically, the proposed model configures a bi‐level max‐min mixed integer linear programming (MILP) that is challenging to solve. For its solution, the problem is casted into an equivalent one‐level MILP that can be solved by efficient global solvers. The approach is applied to a case study concerning the vulnerability identification of the georeferenced RTS24 test system under simulated wind storms. The numerical results demonstrate the effectiveness of the proposed framework for identifying critical locations under multiple hazard events and, thus, for providing a useful tool to help decisionmakers in making more‐informed prehazard preparation decisions.

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