Summarizing risk using risk measures and risk indices

Trend effects on perceived avalanche hazard

Hazard-level forecasts constitute an important risk mitigation tool to reduce loss of economic values and human life. Avalanche forecasts represent an example of this. As for many other domains, avalanche risk is communicated using a color-coded, categorical risk scale aimed at informing the public about past, current, and future risk. We report the results from three experiments in which we tested if an irrelevant past trend in forecasted avalanche danger affects perceptions of current and future avalanche risk. Our sample consisted of individuals from three different populations targeted by national avalanche warning services. All three experiments showed that the perception of avalanche risk is influenced by the trend, but that the effect is opposite for perceptions of current and expectations of future avalanche risk. While future avalanche risk is extrapolated in the same direction as the change from the previous day, we found that perceived current risk appears to be based on an average of past and current risk. These effects diminish when we provide participants with a scale indicating the exact level of avalanche danger. For most of our measurement instruments, however, the effects remain significant. These results imply that targeted populations may consider historic information more than was intended by the sender. As such, our results have implications for both avalanche warning services and risk communication in general.

Sharing Knowledge to an Entrant for Production Investment Confronting COVID-19: Incentive Alignment and Lose-Lose Dilemma

Facing the urgent demand of medical devices for COVID-19 treatment, many automakers have recently begun manufacturing ventilators, even though they are inefficient in production and uninformed of demand variability. To help them, some incumbent ventilator manufacturers have chosen to share knowledge, such as production techniques and demand information. Clearly, the incumbent ventilator manufacturers are fulfilling social responsibility, but is their knowledge sharing rewarding, especially when the automakers are entrant rivals? If possible, are win-win situations in the sense of social responsibility and firms' profitability identifiable? In this work, we develop a game-theoretic model in which an incumbent and an entrant ventilator manufacturer engage in two-dimensional competition in production investment and sales volume. We examine the incumbent manufacturer's profitability with and without knowledge sharing by formulating the tradeoffs among supply expansion, intensified competition, and the entrant's production efficiency improvement and demand variance reduction. We identify both "win-win" and "lose-lose" situations for the two competing manufacturers. Specifically, we find that free knowledge could be harmful for the entrant manufacturer, but the incumbent manufacturer benefits from knowledge sharing when market competition is intense, or when market competition is mild but the production investment efficiency varies.

Risk Management in the Import/Export Process of an Automobile Company: A Contribution for Supply Chain Sustainability

Supply chain risk management (SCRM) is imperative to achieve business sustainability in the long-term perspective and also to increase companies’ competitiveness. ISO 31000—Risk Management explains in its latest versions the need for companies to integrate a risk management process into their business models. Complementary to this standard, ISO 31010 presents 31 risk tools to guide companies in this task. However, a question of practical application arises as to which tools should be used for each stage of the SCRM process. In view of the raised question, the main objective of this research is to select ISO 31010 tools that can be used in each of the stages (identify, analyze, and evaluate) of the risk management of the import and export process of an automotive industry located in Brazil. For this, the analytic hierarchy process (AHP method) was used in a case study to prioritize the tools to compose the SCRM. As practical implications, this study resulted in the suggestion of a structured risk management process, considering the assessment of key professionals of the department studied in the company.

Spatiotemporal Drought Risk Assessment Considering Resilience and Heterogeneous Vulnerability Factors: Lempa Transboundary River Basin in The Central American Dry Corridor

Drought characterization and risk assessment are of great significance due to drought’s negative impact on human health, economy, and ecosystem. This paper investigates drought characterization and risk assessment in the Lempa River basin in Central America. We applied the Standardized Evapotranspiration Deficit Index (SEDI) for drought characterization and drought hazard index (DHI) calculation. Although SEDI’s applicability is theoretically proven, it has been rarely applied. Drought risk is generally derived from the interactions between drought hazard (DHI) and vulnerability (DVI) indices but neglects resilience’s inherent impact. Accordingly, we propose incorporating DHI, DVI, and drought resilience index (DREI) to calculate drought risk index (DRI). Since system factors are not equally vulnerable, i.e., they are heterogeneous, our methodology applies the Analytic Hierarchy Process (AHP) to find the weights of the selected factors for the DVI computation. Finally, we propose a geometric mean method for DRI calculation. Results show a rise in DHI during 2006–2010 that affected DRI. We depict the applicability of SEDI via its relationship with El Nino-La Nina and El Salvador’s cereal production. This research provides a systematic drought risk assessment approach that is useful for decision-makers to allocate resources more smartly or intervene in Drought Risk Reduction (DRR). This research is also useful for those interested in socioeconomic drought.

Developing Bayesian networks in managing the risk of Legionella colonisation of groundwater aeration systems

An Australian water utility has developed a Legionella High Level Risk Assessment (LHLRA) which provides a semi-qualitative assessment of the risk of Legionella proliferation and human exposure in engineered water systems using a combination of empirical observation and expert knowledge. Expanding on this LHLRA, we propose two iterative Bayesian network (BN) models to reduce uncertainty and allow for a probabilistic representation of the mechanistic interaction of the variables, built using data from 25 groundwater treatment plants. The risk of Legionella exposure in groundwater aeration units was quantified as a function of five critical areas including hydraulic conditions, nutrient availability and growth, water quality, system design (and maintenance), and location and access. First, the mechanistic relationship of the variables was conceptually mapped into a fishbone diagram, parameterised deterministically using an expert elicited weighted scoring system and translated into BN. The "sensitivity to findings" analysis of the BN indicated that system design was the most influential variable while elemental accumulation thresholds were the least influential variable for Legionella exposure. The diagnostic inference was used in high and low-risk scenarios to demonstrate the capabilities of the BNs to examine probable causes for diverse conditions. Subsequently, the causal relationship of Legionella growth and human exposure were improved through a conceptual bowtie representation. Finally, an improved model developed the predictors of Legionella growth and the risk of human exposure through the interaction of operational, water quality monitoring, operational parameters, and asset conditions. The use of BNs modelling based on risk estimation and improved functional decision outputs offer a complementary and more transparent alternative approach to quantitative analysis of uncertainties than the current LHLRA.

Safety Regulations and the Uncertainty of Work-Related Road Accident Loss: The Triple Identity of Chinese Local Governments Under Principal-Agent Framework

This study examines how government safety regulations affect the uncertainty of work-related road accident loss (UWRAL) by considering the multi-identity of local governments in the relationship among the central government, the local governments, and enterprises. Fixed effects panel models and mediation analyses with bootstrapping were conducted to test the hypotheses using Chinese provincial panel data from 2008 to 2014. Given the complexity and nonlinear characteristics of road safety systems, a new approach based on self-organized criticality theory is proposed to measure the uncertainty of road accident loss from a complex system perspective. We find that a regional government with detailed safety work planning (SWP), high safety supervision intensity (SSI), and safety information transparency (SIT) can decrease the UWRAL. Furthermore, our findings suggest that SSI and SIT partially mediate the relationship between the SWP of regional governments and the UWRAL, with 19.7% and 23.6% indirect effects, respectively. This study also provides the government with managerial implications by linking the results of risk assessment to decision making for risk management.

Total risk evaluation framework

Purpose

The purpose of this paper is to generalize the traditional risk evaluation methods and to specify a multi-level risk evaluation framework, in order to prepare customized risk evaluation and to enable effectively integrating the elements of risk evaluation.

Design/methodology/approach

A real case study of an electric motor manufacturing company is presented to illustrate the advantages of this new framework compared to the traditional and fuzzy failure mode and effect analysis (FMEA) approaches.

Findings

The essence of the proposed total risk evaluation framework (TREF) is its flexible approach that enables the effective integration of firms’ individual requirements by developing tailor-made organizational risk evaluation.

Originality/value

Increasing product/service complexity has led to increasingly complex yet unique organizational operations; as a result, their risk evaluation is a very challenging task. Distinct structures, characteristics and processes within and between organizations require a flexible yet robust approach of evaluating risks efficiently. Most recent risk evaluation approaches are considered to be inadequate due to the lack of flexibility and an inappropriate structure for addressing the unique organizational demands and contextual factors. To address this challenge effectively, taking a crucial step toward customization of risk evaluation.

Autologistic models for benchmark risk or vulnerability assessment of urban terrorism outcomes

We develop a quantitative methodology to characterize vulnerability among 132 U.S. urban centers ('cities') to terrorist events, applying a place-based vulnerability index to a database of terrorist incidents and related human casualties. A centered autologistic regression model is employed to relate urban vulnerability to terrorist outcomes and also to adjust for autocorrelation in the geospatial data. Risk-analytic 'benchmark' techniques are then incorporated into the modeling framework, wherein levels of high and low urban vulnerability to terrorism are identified. This new, translational adaptation of the risk-benchmark approach, including its ability to account for geospatial autocorrelation, is seen to operate quite flexibly in this socio-geographic setting.

How to Design Rating Schemes of Risk Matrices: A Sequential Updating Approach

Risk matrices have been widely used as a risk evaluation tool in many fields due to their simplicity and intuitive nature. Designing a rating scheme, i.e., determining the number of ratings used in a risk matrix and assigning different ratings to different cells, is an essential part of risk matrix construction. However, most of the related literature has focused on applying a risk matrix to various fields, instead of researching how to design risk matrices. Based on the analysis of several current rules, we propose a new approach, namely, the sequential updating approach (SUA), to design the rating scheme of a risk matrix in a reliable way. In this article, we propose three principles and a rating algorithm based on these principles. The three principles, namely, adjusted weak consistency, consistent internality, and continuous screening, characterize a good rating scheme. The resulting rating scheme has been proven to be unique. A global rating algorithm is then proposed to create the design that satisfies the three principles. We then explore the performance of the SUA. An illustrative application is first given to explain the feasibility of our approach. The sensitivity analysis shows that our method captures a resolution-reliability tradeoff for decisionmakers in choosing an appropriate rating scheme for a risk matrix. Finally, we compare the designs based on the SUA and Cox's axioms, highlighting the advantages of the SUA.

Defending Against Advanced Persistent Threats Using Game-Theory

Advanced persistent threats (APT) combine a variety of different attack forms ranging from social engineering to technical exploits. The diversity and usual stealthiness of APT turns them into a central problem of contemporary practical system security, since information on attacks, the current system status or the attacker’s incentives is often vague, uncertain and in many cases even unavailable. Game theory is a natural approach to model the conflict between the attacker and the defender, and this work investigates a generalized class of matrix games as a risk mitigation tool for an advanced persistent threat (APT) defense. Unlike standard game and decision theory, our model is tailored to capture and handle the full uncertainty that is immanent to APTs, such as disagreement among qualitative expert risk assessments, unknown adversarial incentives and uncertainty about the current system state (in terms of how deeply the attacker may have penetrated into the system’s protective shells already). Practically, game-theoretic APT models can be derived straightforwardly from topological vulnerability analysis, together with risk assessments as they are done in common risk management standards like the ISO 31000 family. Theoretically, these models come with different properties than classical game theoretic models, whose technical solution presented in this work may be of independent interest.

Sensitivity Analysis Using Risk Measures

In a quantitative model with uncertain inputs, the uncertainty of the output can be summarized by a risk measure. We propose a sensitivity analysis method based on derivatives of the output risk measure, in the direction of model inputs. This produces a global sensitivity measure, explicitly linking sensitivity and uncertainty analyses. We focus on the case of distortion risk measures, defined as weighted averages of output percentiles, and prove a representation of the sensitivity measure that can be evaluated on a Monte Carlo sample, as a weighted average of gradients over the input space. When the analytical model is unknown or hard to work with, nonparametric techniques are used for gradient estimation. This process is demonstrated through the example of a nonlinear insurance loss model. Furthermore, the proposed framework is extended in order to measure sensitivity to constant model parameters, uncertain statistical parameters, and random factors driving dependence between model inputs.