Risk Decision-making Process Research Articles

The impact of stress on the risk decision-making process.

The effect of stress on risk-taking or risk-averse behavior in decision-making has been inconclusive in previous research, with few studies revealing the underlying neural mechanisms. This study employed event-related potentials technique and combined a social cold pressor assessment test with a mental arithmetic task to induce stress responses, aiming to investigate the influence of exogenous stress on the risk decision-making process. Stress induction results indicated that, in addition to raising heart rate and blood pressure, stress responses were accompanied by enhanced negative emotions, diminished positive emotions, and alterations in neural activity. The outcomes of risk decision-making showed that stress did not significantly affect risk preference or time of choice but did reduce the feedback-related negativity/reward positivity, with a particularly significant effect observed for large outcomes. Stress also altered the amplitude of the P3 component, with stress decreasing the P3 value for winning outcomes relative to losing outcomes. The study suggests that understanding how stress affects risk preference should consider the emotional valence induced by stress. Contrary to the reward sensitivity hypothesis, stress weakened reward sensitivity. Stress led to changes in the allocation of cognitive resources for outcome evaluation: compared to negative outcomes, stress reduced cognitive resources for positive outcomes, which might be related to the enhanced negative emotions induced by stress. The study highlights the importance of focusing on the subjective emotional experience induced by stress in future research on stress and risk decision-making.

Big data-driven risk decision-making and safety management in agricultural supply chains

In the era of digitization, the integration of big data technologies has become instrumental in advancing agricultural supply chain management and bolstering risk decision-making processes. Agricultural supply chains, critical to ensuring food security and bolstering rural economies, face vulnerabilities stemming from a myriad of internal and external elements, including natural disasters and market dynamics. Consequently, the urgency to adopt effective risk management strategies is paramount. Contemporary studies have explored the utilization of big data in decision-making processes specific to agricultural supply chain risks, predominantly concentrating on preliminary risk prediction and characterization. Nonetheless, there exists a shortfall in comprehensively analyzing the intricate interplay among risk factors and establishing a holistic risk management decision-making framework based on such analyses. This research addresses these deficiencies through two principal investigative components. First, this research explores the analysis of risk factors and their interrelationships in the agricultural supply chain based on a decision tree algorithm with a transition structure. This algorithm enhances decision-makers’ understanding of risk factors and their interrelationships, and guide the implementation of effective risk mitigation measures and the formulation of contingency plans. Subsequently, the research constructs a corresponding data-driven multi-criteria decision-making method, assisting managers in balancing different risk management strategies in a volatile supply chain environment, considering costs, benefits, and feasibility to formulate the optimal strategy. The innovation of this research lies in the development of a novel risk analysis tool based on the transition decision tree algorithm. This is the first time that such advanced algorithms are applied to agricultural supply chain risk management, filling a gap in the current research. The outcomes of this study not only contribute to enhancing risk management practices within agricultural supply chains but also offer novel insights and methodological tools that are applicable in research and practices across related domains.

Influence in the right-of-way: Assessing landowners’ risk decision-making in negotiating oil and gas pipeline easements

Pipeline rights-of-way (ROW) are the result of complicated easement negotiations between pipeline operators and landowners, where landowners must frequently weigh the financial benefits of lucrative contracts against the potential risk of living alongside oil and gas infrastructure projects. This study analyzes a survey of landowners both directly and proximally involved in a pipeline construction project to better understand their risk decision-making processes. We examine how landowners in a ROW understand risks across personal, property, and environment related risk domains, then compare these results to neighboring landowners. We additionally assess how landowners’ relationships to pipelines have changed due to direct positive and negative experiences with these projects. Findings suggest that landowners’ assembled knowledge of pipelines and resulting risk calculations are, in part, determined by personal exposure to pipeline development and negotiations with pipeline operators. We also find evidence of risk discounting in instances where landowners benefit from the pipeline project, or seek to justify their historical risk calculations. Results of the study reveal why landowners assume risks of proximity to pipelines. These findings can inform the practices of community interest groups engaging with landowners, as well as policymakers pursuing rulemaking to protect landowner interests.

Time Pressure Affects the Risk Preference and Outcome Evaluation.

It is ubiquitous that food delivery riders do not have unlimited periods of time for deliberation to make decisions. Time pressure plays a significant role in decision-making processes. This study investigated how time pressure affected risk preference and outcome evaluation through behavioral and electrophysiological responses during decision-making. Participants finished a simple gambling task under three different time constraint conditions (high/medium/low). Behavioral and event-related potentials (ERPs) data were recorded during the experiment. The results showed that the decision time of people was shorter under high time pressure than under medium and low time pressures. People tend to make more risky choices when under high time pressure. The feedback-related negativity (FRN) amplitude was smaller in the high time pressure than in medium and low time pressure conditions. These findings provided evidence that time pressure has an impact on the risk decision-making process.

Risk and uncertainty of plastic mulch adoption in raspberry production systems

AbstractAgriculture plays a central role in providing food security and essential goods globally. Producers must consider and manage risk to ensure that the production system and its associated individuals are capable of enduring unexpected and disruptive events. Analyzing the different types of risk and accompanying uncertainties that growers experience can be essential to better reflect and understand the realities of their circumstances, but these concepts are not always accounted for in the adoption process. Drawing on the importance of risk and uncertainty, this study aims to assess the different types of risk and uncertainties involved in the risk decision-making process of the processed raspberry industry, where plastic mulch is a new production technique. Semi-structured interviews were conducted with participants involved in the use, research, outreach, manufacturing, and distribution of plastic mulch, specifically polyethylene (PE) mulch and soil-biodegradable mulch (BDM). Findings indicate that risk can be present in various forms including production, price, and hidden risks, with production and price risks being the most significant to all participants. When accounting for overall risk, PE mulch was considered riskier to industry representatives but less risky to growers and most research and outreach specialists. BDM was considered risky due to the uncertainties about durability, degradability, and the unknown impacts on the environment if BDM fragments do not degrade readily. The application of PE mulch and/or BDM can be beneficial for the raspberry production systems but will require time for additional research and effort to disseminate information to a wider agricultural audience.

An Extended Prospect Theory for Robot Evaluation and Selection considering Risk Preferences and Interactive Criteria

The evaluation and selection process can be regarded as a complex multiple criteria decision analysis (MCDA) problem which involves various interaction relationships among criteria under high uncertain environment. In addition, the decision-makers are always bounded rational in the risk decision-making process. However, the current robot evaluation and selection approach seldom considers the decision-maker’s risk preference and interactive criteria under high uncertain environment. Thus, the purpose of this paper is to develop a hybrid MCDA approach for solving the robot evaluation and selection problem. In the proposed framework, the interval type-2 fuzzy set is used to express the uncertain evaluation information provided by decision-makers. Next, the distance measure of interval type-2 fuzzy numbers is developed to determine the fuzzy measure of each criterion. Then, the extended prospect theory based on developed Choquet integral is proposed to evaluate and prioritize the robot by considering the decision-maker’s risk preference and interactive criteria. Finally, a case study of robot evaluation and selection in the auto industry is selected to exemplify the application of the proposed framework. After that, comparison and sensitivity studies are conducted to further demonstrate the robustness, effectiveness, and reasonableness of the developed approach.

Application of the Oriented Fuzzy Numbers in Credit Risk Assessment

Over the years, banks have faced many difficulties, related mainly to lax credit standards for borrowers and counterparties. The goal of credit risk management is to maintain the volume of credit risk at acceptable level as it is a vital feature in risk management. Credit analysts take into consideration factors of a wider spectrum, e.g., the prospects of the line of business, the experience of board members, credibility of suppliers, etc. Those factors are often considered on the linguistic scale, which includes such imprecise and inaccurate phrases, for instance, such as: more/less experienced, better/worse prospects, etc., which, for the experts and decision makers, are justified and result from their personal experience, preferences and human nature. The paper presents the approach of supporting methods in the credit risk decision-making process. It presents evaluation scales of imprecise phrases commonly used during the process of credit risk assessment based on experts’ preferences. Due to the imprecision, the oriented fuzzy numbers are a useful tool. For such described evaluation scales, we use a scoring function determined with the use of an adapted Simple Additive Weighting (SAW) method.

Temporal detection of sharp landslide deformation with ensemble-based LSTM-RNNs and Hurst exponent

The sharp slope deformation which often contains seasonal patterns is the major source of the landslide hazard with respect to the local community, which it is a serious geological environment problem. In this paper, a long short-term memory-based deep learning framework has been proposed to model the deformation behaviors especially the sharp deformation of the landslide. First, the Box–Cox transformation is applied to normalize the dataset that includes time-series deformation, precipitation, and reservoir water level. Then, an elastic net (EN)-based ensemble of long short-term memory recurrent neural networks (LSTM-RNNs) is applied to forecast landslide deformation by using month forward-chaining nested cross-validation. This method is performed on the time-series data as our training strategy. Last, the Hurst exponent is formulated to identify incoming sharp deformation. The computational results demonstrated that this approach can accurately identify future sharp deformation. The Hurst exponent illustrates the abnormal patterns in the prediction errors which indicate sharp deformation. As a result, the proposed framework would assist the on-site risk analysis and decision-making process for geological engineers to prevent the landslide hazard in the future.

DPIA in Context: Applying DPIA to Assess Privacy Risks of Cyber Physical Systems

Cyber Physical Systems (CPS) seamlessly integrate physical objects with technology, thereby blurring the boundaries between the physical and virtual environments. While this brings many opportunities for progress, it also adds a new layer of complexity to the risk assessment process when attempting to ascertain what privacy risks this might impose on an organisation. In addition, privacy regulations, such as the General Data Protection Regulation (GDPR), mandate assessment of privacy risks, including making Data Protection Impact Assessments (DPIAs) compulsory. We present the DPIA Data Wheel, a holistic privacy risk assessment framework based on Contextual Integrity (CI), that practitioners can use to inform decision making around the privacy risks of CPS. This framework facilitates comprehensive contextual inquiry into privacy risk, that accounts for both the elicitation of privacy risks, and the identification of appropriate mitigation strategies. Further, by using this DPIA framework we also provide organisations with a means of assessing privacy from both the perspective of the organisation and the individual, thereby facilitating GDPR compliance. We empirically evaluate this framework in three different real-world settings. In doing so, we demonstrate how CI can be incorporated into the privacy risk decision-making process in a usable, practical manner that will aid decision makers in making informed privacy decisions.

An intelligent approach framework for decision-making risk analysis of Chinese hydropower corporations

An intelligent decision analytic framework for dealing with complex decision-making risk system is presented and Bayesian network (BN) approach is utilized to evaluate the influence of multilevel uncertainty in various risks (e.g., social, natural, economic, intracompany risks) on decision-making deviation of Chinese hydropower corporations. The technique of fuzzy probability is approached to calculate intricate parameters to the question of inference learning through the sensitivity and influence power analysis, the results of back inference show that there exists the risk transformation mechanism from external uncertain risks (e.g., social risks, ecological environment factors) to hydropower corporations’ internal uncertainties closely relating to economic uncertainties through strategic planning. The study concerning identification and intelligent analysis of uncertain risks in decision-making process illustrates the feasibility and validity of applying BN and its pragmatic implications on hydropower corporations strategic planning and guidance in operational management.

Three-way decision making approach to conflict analysis and resolution using probabilistic rough set over two universes

Conflict analysis aims to identify the intrinsic reasons and find a feasible consensus strategy for a conflict situation. Rough set theory was used to study conflict analysis decision-making in the late 90s. The basic way to express the attitudes of every agent are against, favorable and neutral for any issue in the original Pawlak conflict analysis model. The notion of three-way decision (3WD) was initially developed as a means to interpret decision rules induced in probabilistic rough sets. In this paper, we first present the framework of three-way decision (3WD) using probabilistic rough set over two universes. With respect to the probabilistic positive, negative and boundary regions over two universes, we build the rules for making a decision of acceptance, rejection and non-commitment, respectively. So, there is an one-to-one correspondence between the three attitudes of every agent for any issue in a conflict situation and the three decisions in the probabilistic rough set over two universes. Based on this, we present an improved Pawlak conflict analysis model by using the principle of three-way decision based on probabilistic rough set over two universes. We construct the conflict decision-making information system under the framework of two universes. Then we define the favorable issues set and against issues set of any agent between the agent set and the dispute set over conflict decision-making information system, respectively. Furthermore, according to the principle of Bayesian risk decision-making process over two universes, we calculate the threshold value parameters used in the lower and upper approximations of a feasible consensus strategy over conflict decision-making information system. Finally, we present the decision rules and the algorithm of finding a feasible consensus strategy for conflict situation based on three-way decision-making with the probabilistic approximations over two universes. Compared with the original Pawlak conflict analysis model, the proposed model not only provides a new perspective and methodology to handle the conflict analysis problems but also overcomes the limitations of the original model. Lastly, we illustrate the idea and basic principles established in this paper by analyzing a conflict decision-making scenario.

STI-protective self-efficacy and binge drinking in a sample of university students in the United Kingdom

ObjectiveAlcohol use has consistently been shown to be related to sexual risk-taking behaviours. To assess what factors may contribute to the sexual risk decision-making process, this study examined the relationships among alcohol use (frequency, quantity, and binge drinking), cognitive appraisals of sexual risk taking, sex-related alcohol expectancies, and STI-protective self-efficacy. Method138 sexually-active university students who drink alcohol completed scales measuring alcohol consumption, appraisals of consequences, sex-related alcohol expectancies, and items regarding STI-protective self-efficacy. ResultsIncreasing levels of binge drinking were negatively associated with STI-protective self-efficacy. A moderated mediation analysis revealed that for binge drinkers, stronger appraisals of the positive consequences for having sexual intercourse while intoxicated predicted lower STI-protective self-efficacy indirectly through increasing rates of sex-related alcohol risk expectancies. ConclusionFindings provide evidence of a need to target binge drinkers and increase their STI-protective self-efficacy by shifting their focus from positive consequences to negative risk consequences of engaging in sexual intercourse while intoxicated.

Toward the validation of a National Risk Assessment against historical observations using a Bayesian approach: application to the Swiss case

National Risk Assessment (NRA) studies have recently received increased interest from governments, authorities and other involved stakeholders, e.g. civil protection agencies, emergency planners, etc. The NRA approach combines risk assessment methods and decision-making processes in a structured manner to support the prioritization and management of national hazards and threats. A NRA commonly builds upon an intensive information processing and judgmental exercise, therefore, in a qualitative or semi-quantitative way, requiring the cooperation of experts from various fields. The validation of a NRA is of great importance to ensure that the analysis of national hazard scenarios results in the implementation of adequate preparedness strategies and contingency planning. In this study, a first step toward a comprehensive framework for NRA validation is presented. The method is developed to evaluate the accuracy and consistency of establishing risk indicator values by expert elicitation by comparing it against historical experience. The proposed method is based on Bayesian analysis, and is tested in three hazard scenario case studies for the Swiss NRA. The results for the chosen Swiss NRA cases show that expert estimates and quantitative assessment of historical experience are overall in good agreement. However, scenario-specific assumptions and boundary conditions defined by the experts are not in all cases supported by evaluation of historical experience. Finally, the main drawback of the proposed approach is that it can only be applied to hazard scenarios with sufficient historical observations, while it has the advantage to be generic and thus applicable to other risk assessment contexts outside of NRA.

Neural Networks in Credit Risk Classification of Companies in the Construction Sector

The financial sector (banks, financial institutions, etc.) is the sector most exposed to financial and credit risk, as one of the basic objectives of banks' activity (as a specific enterprise) is granting credit and loans. Because credit risk is one of the problems constantly faced by banks, identification of potential good and bad customers is an extremely important task. This paper investigates the use of different structures of neural networks to support the preliminary credit risk decision-making process. The results are compared among the models and juxtaposed with real-world data. Moreover, different sets and subsets of entry data are analyzed to find the best input variables (financial ratios).

Classification of Trade Sector Entities in Credibility Assessment Using Neural Networks

One of the most valid tasks in credit risk evaluation is the proper classification of potential good and bad customers. Reduction of the number of loans granted to companies of questionable credibility can significantly influence banks’ performance. An important element in credit risk assessment is a prior identification of factors which affect companies’ standing. Since that standing has an impact on credibility and solvency of entities. The research presented in the paper has two main goals. The first is to identify the most important factors (chosen financial ratios) which determine company’s performance and consequently influence its credit risk level when granted financial resources. The question also arises whether the line of business has any impact on factors that should be included in the analysis as the input. The other aim was to compare the results of chosen neural networks with credit scoring system used in a bank during credit risk decision-making process.

风险决策研究述评

风险决策过程是一种复杂的心理过程，近年来，风险决策得到越来越多的心理学者们的高度重视，并进行了一系列有价值的研究。本文从风险决策的理论模型、影响因素等方面进行总结分析。 Risk decision-making process is a complex mental process. In recent years, more and more psy-chologists show the highly appreciated on risk decision, and conducted a series of valuable research. This article, from the theory of risk decision-making model, carries on the summary analysis of influence factors.

Searching for predictors of the risk decision-making process in social situations in the adolescence

This study examined how well self-reported sensation seeking, empathy and resistance to peer influence, and performance on the computer-based measure to evaluate risk-taking behavior (Balloon Analogue Risk Task, BART) predicted the risk decision-making process on the computer-based Social Context Decision Task (SCDT). Participants were 256 early, mid- and late adolescents and young adults, distributed by age and gender. Early adolescents scored lower on sensation seeking and empathy than late adolescents and young adults. Men scored higher on sensation seeking whereas women scored higher on empathy and resistance to peer influence. Regression models showed that riskiness on the BART was positively related to the percentage of risk elections and shorter decision times in the SCDT task, with the BART parameters accounting for variance in these measures beyond that accounted for by age, gender and Disinhibition. These results contributed to a more comprehensive multimethod assessment of the process of risk decision making in social situations.

Feedback on health status and peer popularity influences social risk decision-making processes in adolescents and young adults

Cognitive and dual-processes models, involving cognitive and socio-emotional components, for adolescents’ risky behaviour have been proposed. This study tested their predictions by manipulating the presence or absence of feedback about gains and losses in health and peer popularity in a decision-making task with peers. Risky (e.g., taking or refusing a drug) and ambiguous decisions (e.g., eating hamburger or hotdog) were examined in 256 adolescents (aged 13–14; 15–16; 17–18) and young adults (aged 19–20). Participants made more risky choices and required less decision time when receiving feedback about the negative consequences on health and positive consequences on peer popularity. Decision times were comparatively larger for risky than for safe options in late adolescence and young adulthood. Results supported the dual-processes model showing that, though late cognitive changes were observed that could eventually lead to the selection of safe options, feedback gains in peer popularity outweighed unhealthy consequences leading to risky decisions.

Atlas‐guided volumetric diffuse optical tomography enhanced by generalized linear model analysis to image risk decision‐making responses in young adults

Diffuse optical tomography (DOT) is a variant of functional near infrared spectroscopy and has the capability of mapping or reconstructing three dimensional (3D) hemodynamic changes due to brain activity. Common methods used in DOT image analysis to define brain activation have limitations because the selection of activation period is relatively subjective. General linear model (GLM)‐based analysis can overcome this limitation. In this study, we combine the atlas‐guided 3D DOT image reconstruction with GLM‐based analysis (i.e., voxel‐wise GLM analysis) to investigate the brain activity that is associated with risk decision‐making processes. Risk decision‐making is an important cognitive process and thus is an essential topic in the field of neuroscience. The Balloon Analog Risk Task (BART) is a valid experimental model and has been commonly used to assess human risk‐taking actions and tendencies while facing risks. We have used the BART paradigm with a blocked design to investigate brain activations in the prefrontal and frontal cortical areas during decision‐making from 37 human participants (22 males and 15 females). Voxel‐wise GLM analysis was performed after a human brain atlas template and a depth compensation algorithm were combined to form atlas‐guided DOT images. In this work, we wish to demonstrate the excellence of using voxel‐wise GLM analysis with DOT to image and study cognitive functions in response to risk decision‐making. Results have shown significant hemodynamic changes in the dorsal lateral prefrontal cortex (DLPFC) during the active‐choice mode and a different activation pattern between genders; these findings correlate well with published literature in functional magnetic resonance imaging (fMRI) and fNIRS studies. Hum Brain Mapp 35:4249–4266, 2014. © 2014 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc.

European Parliament Working Group on Risk: A Commentary

Over the past 40 years or so, risk governance has become central to the EU project. Pan-EU risk governance frameworks have been critical in creating a level regulatory playing field for European business; they have provided the necessary coordination to tackle harms that cross the jurisdictional boundaries of member states; and they have brought harms under control that member state governments have neither capacity nor will in the political cycle to focus on. However, as Lofstedt points out in this issue, as the EU has extended its writ over ever more domains of risk, so have its risk decision-making processes and principles increasingly become the subject of controversy and challenge. In that context, the European Parliament’s Informal Working Group on Risk is a welcome opportunity to improve the understanding of, and provide advice on, “risk” in EU policy processes. That is an important objective because, as Lofstedt points out, the idea of “risk” stands at the center of a number of critical questions in policy making in the EU. How far should governments go to eliminate harms that confront society? How should governments act under uncertainty? How should governments manage the distributional conflicts of policies that secure safety for some but expose others to danger? These are, of course, universal questions to which there are no easy answers. Increasingly, however, governments around the world have started to pay more attention to risk ideas in an attempt to better understand the problems that they confront. By taking into account probability as well as the impact of potential adverse regulatory outcomes, risk-based approaches to policy making offer the promise of