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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Understanding the Ecological Validity of Relying Practice as a Basis for Risk Identification

28 March 2020 - 10:25am
Abstract

Understanding the reliability of hazardous organizations and their protective systems is central to understanding the risk they produce. Work on “high reliability organization” has done much to illuminate the conditions in which social organization becomes reliable in highly demanding conditions. But risk depends just as much on how relying entities do their relying as it does on the reliability of the entities they rely on. Patterns of relying are often opaque in sociotechnical systems, and processes of relying and being relied on are mutually influencing in complex ways, so the relationship between relying and risk may not be at all obvious. This study was an attempt to study relying as a social practice, in particular analyzing how it had ecological validity in a social organization—how practice was responsive to the conditions in which it took place. This involved observational fieldwork and inductive, qualitative analysis on an offshore oil and gas production platform that was nearing the end of its design life and undergoing refurbishment. The analysis produced four main categories of ecological validity: responsiveness to formal organization, responsiveness to situational contingency, responsiveness to information asymmetry, and responsiveness to sociomateriality. This ecological validity of relying practice should be a primary focus of risk identification, assessing how relying can become mismatched to reliability in certain ways, both when relying practice is responsive to circumstances and when it is not.

Fuzzy System Dynamics Risk Analysis (FuSDRA) of Autonomous Underwater Vehicle Operations in the Antarctic

28 March 2020 - 10:25am
Abstract

With the maturing of autonomous technology and better accessibility, there has been a growing interest in the use of autonomous underwater vehicles (AUVs). The deployment of AUVs for under‐ice marine science research in the Antarctic is one such example. However, a higher risk of AUV loss is present during such endeavors due to the extreme operating environment. To control the risk of loss, existing risk analyses approaches tend to focus more on the AUV's technical aspects and neglect the role of soft factors, such as organizational and human influences. In addition, the dynamic and complex interrelationships of risk variables are also often overlooked due to uncertainties and challenges in quantification. To overcome these shortfalls, a hybrid fuzzy system dynamics risk analysis (FuSDRA) is proposed. In the FuSDRA framework, system dynamics models the interrelationships between risk variables from different dimensions and considers the time‐dependent nature of risk while fuzzy logic accounts for uncertainties. To demonstrate its application, an example based on an actual Antarctic AUV program is presented. Focusing on funding and experience of the AUV team, simulation of the FuSDRA risk model shows a declining risk of loss from 0.293 in the early years of the Antarctic AUV program, reaching a minimum of 0.206 before increasing again in later years. Risk control policy recommendations were then derived from the analysis. The example demonstrated how FuSDRA can be applied to inform funding and risk management strategies, or broader application both within the AUV domain and on other complex technological systems.

Understanding the Ecological Validity of Relying Practice as a Basis for Risk Identification

27 March 2020 - 4:30pm
Abstract

Understanding the reliability of hazardous organizations and their protective systems is central to understanding the risk they produce. Work on “high reliability organization” has done much to illuminate the conditions in which social organization becomes reliable in highly demanding conditions. But risk depends just as much on how relying entities do their relying as it does on the reliability of the entities they rely on. Patterns of relying are often opaque in sociotechnical systems, and processes of relying and being relied on are mutually influencing in complex ways, so the relationship between relying and risk may not be at all obvious. This study was an attempt to study relying as a social practice, in particular analyzing how it had ecological validity in a social organization—how practice was responsive to the conditions in which it took place. This involved observational fieldwork and inductive, qualitative analysis on an offshore oil and gas production platform that was nearing the end of its design life and undergoing refurbishment. The analysis produced four main categories of ecological validity: responsiveness to formal organization, responsiveness to situational contingency, responsiveness to information asymmetry, and responsiveness to sociomateriality. This ecological validity of relying practice should be a primary focus of risk identification, assessing how relying can become mismatched to reliability in certain ways, both when relying practice is responsive to circumstances and when it is not.

A Solar‐Centric Approach to Improving Estimates of Exposure Processes for Coronal Mass Ejections

22 March 2020 - 1:15am
Abstract

We present a solar‐centric approach to estimating the probability of extreme coronal mass ejections (CME) using the Solar and Heliospheric Observatory (SOHO)/Large Angle and Spectrometric Coronagraph Experiment (LASCO) CME Catalog observations updated through May 2018 and an updated list of near‐Earth interplanetary coronal mass ejections (ICME). We examine robust statistical approaches to the estimation of extreme events. We then assume a variety of time‐independent distributions fitting, and then comparing, the different probability distributions to the relevant regions of the cumulative distributions of the observed CME speeds. Using these results, we then obtain the probability that the velocity of a CME exceeds a particular threshold by extrapolation. We conclude that about 1.72% of the CMEs recorded with SOHO LASCO arrive at the Earth over the time both data sets overlap (November 1996 to September 2017). Then, assuming that 1.72% of all CMEs pass the Earth, we can obtain a first‐order estimate of the probability of an extreme space weather event on Earth. To estimate the probability over the next decade of a CME, we fit a Poisson distribution to the complementary cumulative distribution function. We inferred a decadal probability of between 0.01 and 0.09 for an event of at least the size of the large 2012 event, and a probability between 0.0002 and 0.016 for the size of the 1859 Carrington event.

A Methodology for Assessing the Probability of Occurrence of Undesired Events in the Tietê–Paraná Inland Waterway Based on Expert Opinion

22 March 2020 - 1:15am
Abstract

The market share of Tietê–Paraná inland waterway (TPIW) in the transport matrix of the São Paulo state, Brazil, is currently only 0.6%, but it is expected to increase to 6% over the next 20 years. In this scenario, to identify and explore potential undesired events a risk assessment is necessary. Part of this involves assigning the probability of occurrence of events, which usually is accomplished by a frequentist approach. However, in many cases, this approach is not possible due to unavailable or nonrepresentative data. This is the case of the TPIW that even though an expressive accident history is available, a frequentist approach is not suitable due to differences between current operational conditions and those met in the past. Therefore, a subjective assessment is an option as allows for working independently of the historical data, thus delivering more reliable results. In this context, this article proposes a methodology for assessing the probability of occurrence of undesired events based on expert opinion combined with fuzzy analysis. This methodology defines a criterion to weighting the experts and, using the fuzzy logic, evaluates the similarities among the experts’ beliefs to be used in the aggregation process before the defuzzification that quantifies the probability of occurrence of the events based on the experts’ opinion. Moreover, the proposed methodology is applied to the real case of the TPIW and the results obtained from the elicited experts are compared with a frequentist approach evidencing the impact on the results when considering different interpretations of the probability.

Application of a Risk Analysis Tool to Middle East Respiratory Syndrome Coronavirus (MERS‐CoV) Outbreak in Saudi Arabia

22 March 2020 - 1:15am
Abstract

The Grunow–Finke assessment tool (GFT) is an accepted scoring system for determining likelihood of an outbreak being unnatural in origin. Considering its high specificity but low sensitivity, a modified Grunow–Finke tool (mGFT) has been developed with improved sensitivity. The mGFT has been validated against some past disease outbreaks, but it has not been applied to ongoing outbreaks. This study is aimed to score the outbreak of Middle East respiratory syndrome coronavirus (MERS‐CoV) in Saudi Arabia using both the original GFT and mGFT. The publicly available data on human cases of MERS‐CoV infections reported in Saudi Arabia (2012–2018) were sourced from the FluTrackers, World Health Organization, Saudi Ministry of Health, and published literature associated with MERS outbreaks investigations. The risk assessment of MERS‐CoV in Saudi Arabia was analyzed using the original GFT and mGFT criteria, algorithms, and thresholds. The scoring points for each criterion were determined by three researchers to minimize the subjectivity. The results showed 40 points of total possible 54 points using the original GFT (likelihood: 74%), and 40 points of a total possible 60 points (likelihood: 67%) using the mGFT, both tools indicating a high likelihood that human MERS‐CoV in Saudi Arabia is unnatural in origin. The findings simply flag unusual patterns in this outbreak, but do not prove unnatural etiology. Proof of bioattacks can only be obtained by law enforcement and intelligence agencies. This study demonstrated the value and flexibility of the mGFT in assessing and predicting the risk for an ongoing outbreak with simple criteria.

Simulating the Linkages Between Economy and Armed Conflict in India With a Long Short‐Term Memory Algorithm

22 March 2020 - 1:15am
Abstract

This article analyzes the linkages between the economy and armed conflict in India using annual frequency data for the period 1989–2016, the maximum time period for which consistent data are available for the country. An adequate set of economic indicators was established to fully reflect the economic condition. Long short‐term memory (LSTM), which is a machine‐learning algorithm for time series, was employed to simulate the relationship between the economy and armed conflict events. In addition, LSTM was applied to predict the trend of armed conflict with two strategies: multiyear predictions and yearly predictions. The results show that both strategies can adequately simulate the relationship between the economy and armed conflict, with all simulation accuracies above 90%. The accuracy of the yearly prediction is higher than that of the multiyear prediction. Theoretically, the future state and trend of armed conflict can be predicted with LSTM and future economic data if future economic data can be predicted.

A Discourse on the Incorporation of Organizational Factors into Probabilistic Risk Assessment: Key Questions and Categorical Review

22 March 2020 - 1:15am
Abstract

This article presents a discourse on the incorporation of organizational factors into probabilistic risk assessment (PRA)/probabilistic safety assessment (PSA), a topic of debate since the 1980s that has spurred discussions among industry, regulatory agencies, and the research community. The main contributions of this article include (1) identifying the four key open questions associated with this topic; (2) framing ongoing debates by considering differing perspectives around each question; (3) offering a categorical review of existing studies on this topic to justify the selection of each question and to analyze the challenges related to each perspective; and (4) highlighting the directions of research required to reach a final resolution for each question. The four key questions are: (I) How significant is the contribution of organizational factors to accidents and incidents? (II) How critical, with respect to improving risk assessment, is the explicit incorporation of organizational factors into PRA? (III) What theoretical bases are needed for explicit incorporation of organizational factors into PRA? (IV) What methodological bases are needed for the explicit incorporation of organizational factors into PRA? Questions I and II mainly analyze PRA literature from the nuclear domain. For Questions III and IV, a broader review and categorization is conducted of those existing cross‐disciplinary studies that have evaluated the effects of organizational factors on safety (not solely PRA‐based) to shed more light on future research needs.

Human Error in Autonomous Underwater Vehicle Deployment: A System Dynamics Approach

22 March 2020 - 1:15am
Abstract

The use of autonomous underwater vehicles (AUVs) for various applications have grown with maturing technology and improved accessibility. The deployment of AUVs for under‐ice marine science research in the Antarctic is one such example. However, a higher risk of AUV loss is present during such endeavors due to the extremities in the Antarctic. A thorough analysis of risks is therefore crucial for formulating effective risk control policies and achieving a lower risk of loss. Existing risk analysis approaches focused predominantly on the technical aspects, as well as identifying static cause and effect relationships in the chain of events leading to AUV loss. Comparatively, the complex interrelationships between risk variables and other aspects of risk such as human errors have received much lesser attention. In this article, a systems‐based risk analysis framework facilitated by system dynamics methodology is proposed to overcome existing shortfalls. To demonstrate usefulness of the framework, it is applied on an actual AUV program to examine the occurrence of human error during Antarctic deployment. Simulation of the resultant risk model showed an overall decline in human error incident rate with the increase in experience of the AUV team. Scenario analysis based on the example provided policy recommendations in areas of training, practice runs, recruitment policy, and setting of risk tolerance level. The proposed risk analysis framework is pragmatically useful for risk analysis of future AUV programs to ensure the sustainability of operations, facilitating both better control and monitoring of risk.

Insights From Modeling Preventive Supplemental Immunization Activities as a Strategy to Eliminate Wild Poliovirus Transmission in Pakistan and Afghanistan

22 March 2020 - 1:15am
Abstract

Many countries use supplemental immunization activities (SIAs) with oral poliovirus vaccine (OPV) to keep their population immunity to transmission high using preventive, planned SIAs (pSIAs) and outbreaks response SIAs (oSIAs). Prior studies suggested that investment in pSIAs saved substantial health and financial costs due to avoided outbreaks. However, questions remain about the benefits of SIAs, particularly with the recent introduction of inactivated poliovirus vaccine (IPV) into routine immunization in all OPV‐using countries. The mounting costs of polio eradication activities and the need to respond to oSIAs threatens the use of limited financial resources for pSIAs, including in the remaining countries with endemic transmission of serotype 1 wild poliovirus (WPV1) (i.e., Pakistan and Afghanistan). A recent updated global poliovirus transmission model suggested that the Global Polio Eradication Initiative (GPEI) is not on track to stop transmission of WPV1 in Pakistan and Afghanistan. We use the updated global model to explore the role of pSIAs to achieve WPV1 eradication. We find that unless Pakistan and Afghanistan manage to increase the quality of bivalent OPV (bOPV) pSIAs, which we model as intensity (i.e., sufficiently high‐coverage bOPV pSIAs that reach missed children), the model does not lead to successful eradication of WPV1. Achieving WPV1 eradication, the global objectives of the GPEI, and a successful polio endgame depend on effective and sufficient use of OPV. IPV use plays a negligible role in stopping transmission in Pakistan and Afghanistan and most other countries supported by the GPEI, and more IPV use will not help to stop transmission.

Reflective Listening Visualization: Enhancing Interdisciplinary Disaster Research through the Use of Visualization Techniques

22 March 2020 - 1:15am
Abstract

Reflective Listening Visualization is an interdisciplinary research method and iterative process that includes the participation and involvement of multiple team members of different disciplines when (1) conducting in‐depth interviews, (2) performing thematic analysis of the interview data, (3) using the emergent themes as basis to design visual representations of the themes, (4) presenting the visuals back to the interviewee for feedback, and (5) using the feedback to confirm the visual, refine the existing visual, or to create a new visual. Fundamentally, the Reflective Listening Visualization technique is an enhanced communication tool that aims to increase the community member's voice and to verify the understanding of their experiences, ideas, and concerns while also enhancing the interdisciplinary learning potential of the research team. In this article, we describe the Reflective Listening Visualization process and discuss how the Reflective Listening Visualization process allows for (1) improved communication between interdisciplinary team members, (2) understanding of residents’ wants and needs for their community, (3) increasing rapport with residents, (4) building trust between residents and between residents and researchers, (5) accurate representation of residents ideas, and (6) community members to become motivated about the possibilities of the future of their community.

Behavioral Determinants of Target Shifting and Deterrence in an Analog Cyber‐Attack Game

22 March 2020 - 1:15am
Abstract

This study examines how exploiting biases in probability judgment can enhance deterrence using a fixed allocation of defensive resources. We investigate attacker anchoring heuristics for conjunctive events with missing information to distort attacker estimates of success for targets with equal defensive resources. We designed and conducted a behavioral experiment functioning as an analog cyber attack with multiple targets requiring three stages of attack to successfully acquire a target. Each stage is associated with a probability of successfully attacking a layer of defense, reflecting the allocation of resources for each layer. There are four types of targets that have nearly equal likelihood of being successfully attacked, including one type with equally distributed success probabilities over every layer and three types with success probabilities that are concentrated to be lowest in the first, second, or third layer. Players are incentivized by a payoff system that offers a reward for successfully attacked targets and a penalty for failed attacks. We collected data from a total of 1,600 separate target selections from 80 players and discovered that the target type with the lowest probability of success on the first layer was least preferred among attackers, providing the greatest deterrent. Targets with equally distributed success probabilities across layers were the next least preferred among attackers, indicating greater deterrence for uniform‐layered defenses compared to defenses that are concentrated at the inner (second or third) levels. This finding is consistent with both attacker anchoring and ambiguity biases and an interpretation of failed attacks as near misses.

Assessing the Benefits and Costs of Homeland Security Research: A Risk‐Informed Methodology with Applications for the U.S. Coast Guard

22 March 2020 - 1:15am
Abstract

This article describes a methodology for risk‐informed benefit–cost analyses of homeland security research products. The methodology is field‐tested with 10 research products developed for the U.S. Coast Guard. Risk‐informed benefit–cost analysis is a tool for risk management that integrates elements of risk analysis, decision analysis, and benefit–cost analysis. The cost analysis methodology includes a full‐cost accounting of research projects, starting with initial fundamental research costs and extending to the costs of implementation of the research products and, where applicable, training, maintenance, and upgrade costs. The benefits analysis methodology is driven by changes in costs and risks leading to five alternative models: cost savings at the same level of security, increased security at the same cost, signal detection improvements, risk reduction by deterrence, and value of information. The U.S. Coast Guard staff selected 10 research projects to test and generalize the methodology. Examples include tools to improve the detection of explosives, reduce the costs of harbor patrols, and provide better predictions of hurricane wind speeds and floods. Benefits models and estimates varied by research project and many input parameters of the benefit estimates were highly uncertain, so risk analysis for sensitivity testing and simulation was important. Aggregating across the 10 research products, we found an overall median net present value of about $385 million, with a range from $54 million (5th percentile) to $877 million (95th percentile). Lessons learned are provided for future applications.

Identification of Protective Actions to Reduce the Vulnerability of Safety‐Critical Systems to Malevolent Intentional Acts: An Optimization‐Based Decision‐Making Approach

22 March 2020 - 1:15am
Abstract

An empirical classification model based on the Majority Rule Sorting (MR‐Sort) method has been previously proposed by the authors to evaluate the vulnerability of safety‐critical systems (in particular, nuclear power plants [NPPs]) with respect to malevolent intentional acts. In this article, the model serves as the basis for an analysis aimed at determining a set of protective actions to be taken (e.g., increasing the number of monitoring devices, reducing the number of accesses to the safety‐critical system) in order to effectively reduce the level of vulnerability of the safety‐critical systems under consideration.

In particular, the problem is here tackled within an optimization framework: the set of protective actions to implement is chosen as the one minimizing the overall level of vulnerability of a group of safety‐critical systems. In this context, three different optimization approaches have been explored: (i) one single classification model is built to evaluate and minimize system vulnerability; (ii) an ensemble of compatible classification models, generated by the bootstrap method, is employed to perform a “robust” optimization, taking as reference the “worst‐case” scenario over the group of models; (iii) finally, a distribution of classification models, still obtained by bootstrap, is considered to address vulnerability reduction in a “probabilistic” fashion (i.e., by minimizing the “expected” vulnerability of a fleet of systems). The results are presented and compared with reference to a fictitious example considering NPPs as the safety‐critical systems of interest.

Review and Evaluation of the J100‐10 Risk and Resilience Management Standard for Water and Wastewater Systems

22 March 2020 - 1:15am
Abstract

Risk analysis standards are often employed to protect critical infrastructures, which are vital to a nation's security, economy, and safety of its citizens. We present an analysis framework for evaluating such standards and apply it to the J100‐10 risk analysis standard for water and wastewater systems. In doing so, we identify gaps between practices recommended in the standard and the state of the art. While individual processes found within infrastructure risk analysis standards have been evaluated in the past, we present a foundational review and focus specifically on water systems. By highlighting both the conceptual shortcomings and practical limitations, we aim to prioritize the shortcomings needed to be addressed. Key findings from this study include (1) risk definitions fail to address notions of uncertainty, (2) the sole use of “worst reasonable case” assumptions can lead to mischaracterizations of risk, (3) analysis of risk and resilience at the threat‐asset resolution ignores dependencies within the system, and (4) stakeholder values need to be assessed when balancing the tradeoffs between risk reduction and resilience enhancement.

A Game Theory Approach for Assessment of Risk and Deployment of Police Patrols in Response to Criminal Activity in San Francisco

22 March 2020 - 1:15am
Abstract

An efficient police patrol schedule must ensure the allocation of an appropriate number of officers sufficient to respond to the danger of criminal incidents, particularly in an urban environment, even when the available number of personnel is limited. This study proposes a framework that incorporates two game theory models designed for the allocation of police officers to patrol shifts. In the first step, the interactions of three factors between the criminal and the operation captain are modeled as a zero‐sum, noncooperative game, after which a mixed strategy Nash equilibrium method is used to derive the risk value for each district to be patrolled. In the second step, the risk values are used to compute the Shapley value for all 10 districts, for three different threat levels. A fair allocation of police personnel based on the Shapley value is made with a minimum set of personnel deployment costs. The efficacy of the proposed method is verified using openly available data from the San Francisco City Police detailing incidents from the year 2016. The experimental results show that police planners can use this framework to quantitatively evaluate the criminal threat in each district when deciding upon the deployment of patrol officers for three shifts per day.

Robustness of Optimal Investment Decisions in Mixed Insurance/Investment Cyber Risk Management

22 March 2020 - 1:15am
Abstract

An integrated risk management strategy, combining insurance and security investments, where the latter contribute to reduce the insurance premium, is investigated to assess whether it can lead to reduced overall security expenses. The optimal investment for this mixed strategy is derived under three insurance policies, covering, respectively, all the losses (total coverage), just those below the limit of maximum liability (partial coverage), and those above a threshold but below the maximum liability (partial coverage with deductibles). Under certain conditions (e.g., low potential loss, or either very low or very high vulnerability), the mixed strategy reverts however to insurance alone, because investments do not provide an additional benefit. When the mixed strategy is the best choice, the dominant component in the overall security expenses is the insurance premium in most cases. Optimal investment decisions require an accurate estimate of the vulnerability, whereas larger estimation errors may be tolerated for the investment‐effectiveness coefficient.

Characterization of Historical Methane Occurrence Frequencies from U.S. Underground Natural Gas Storage Facilities with Implications for Risk Management, Operations, and Regulatory Policy

22 March 2020 - 1:15am
Abstract

Defining a baseline for the frequency of occurrences at underground natural gas storage facilities is critical to maintaining safe operation and to the development of appropriate risk management plans and regulatory approaches. Currently used frequency‐estimation methods are reviewed and broadened in this article to include critical factors of cause, severity, and uncertainty that contribute to risk. A Bayesian probabilistic analysis characterizes the aleatoric historical occurrence frequencies given imperfect sampling. Frequencies for the three main storage facility types in the United States (depleted oil‐and‐gas field storage, aquifer storage, solution‐mined salt cavern storage) are generally on the order of 3 to 9 × 10–2 occurrences, of all causes (surface, well integrity, subsurface integrity) and severities (nuisance, serious, catastrophic), per facility‐year. Loss of well integrity is associated with many, but not all, occurrences either within the subsurface or from there up to the surface. The probability of one serious or catastrophic leakage occurrence to the ground surface within the next 10 years, assuming constant number of facilities, is approximately 0.1–0.3% for any facility type. Storage operators and industry regulators can use occurrence frequencies, their associated probabilities and uncertainties, and forecasts of severity magnitudes to better prioritize resources, establish a baseline against which progress toward achieving a reduction target could be measured, and develop more effective mitigation/monitoring/reduction programs in a risk management plan.

Persistent Risk‐Related Worry as a Function of Recalled Exposure to the Deepwater Horizon Oil Spill and Prior Trauma

22 March 2020 - 1:15am
Abstract

Large oil spills are disasters associated with psychological effects for exposed communities. The amount of worry that individuals experience after a disaster may be influenced by many factors, such as the type and extent of exposure to disaster impacts, prior trauma, and sociodemographic characteristics. This study examined the nature and predictors of worry about ongoing impacts of the 2010 Deepwater Horizon (DH) oil spill reported by Gulf of Mexico coastal residents. A random sample of 2,520 adult residents of Gulf of Mexico coastal counties were administered a telephone survey in 2016, including items about persistent worry and exposure to DH impacts, prior trauma, residence at the time of the spill, and sociodemographic characteristics. Respondents varied in the amount of worry they reported about ongoing health, social, and economic impacts. Controlling for sociodemographic characteristics, higher exposure to the DH oil spill was related to higher levels of worry about ongoing impacts, with past traumatic events related specifically to worry about health impacts. Unexpectedly, those who moved into the region after the spill showed similar levels of worry to residents exposed to the spill, and higher levels than residents who did not recall being exposed to the DH oil spill. This study highlights the impact of the DH oil spill on coastal residents many years after the DH disaster. The findings underscore the need to examine multiple pathways by which individuals experience disasters and for risk researchers to close knowledge gaps about long‐term impacts of oil spills within a multi‐dimensional framework.

Impact of Uncertainty Parameter Distribution on Robust Decision Making Outcomes for Climate Change Adaptation under Deep Uncertainty

22 March 2020 - 1:15am
Abstract

Deep uncertainty in future climatic and economic conditions complicates developing infrastructure designed to last several generations, such as water reservoirs. In response, analysts have developed multiple robust decision frameworks to help identify investments and policies that can withstand a wide range of future states. Although these frameworks are adept at supporting decisions where uncertainty cannot be represented probabilistically, analysts necessarily choose probabilistic bounds and distributions for uncertain variables to support exploratory modeling. The implications of these assumptions on the analytical outcomes of robust decision frameworks are rarely evaluated, and little guidance exists in terms of how to select uncertain variable distributions. Here, we evaluate the impact of these choices by following the robust decision‐making procedure, using four different assumptions about the probabilistic distribution of exogenous uncertainties in future climatic and economic states. We take a water reservoir system in Ethiopia as our case study, and sample climatic parameters from uniform, normal, extended uniform, and extended normal distributions; we similarly sample two economic parameters. We compute regret and satisficing robustness decision criteria for two performance measures, agricultural water demand coverage and net present value, and perform scenario discovery on the most robust reservoir alternative. We find lower robustness scores resulting from extended parameter distributions and demonstrate that parameter distributions can impact vulnerabilities identified through scenario discovery. Our results suggest that exploratory modeling within robust decision frameworks should sample from extended, uniform parameters distributions.

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