Sensitivity Analysis and Estimation of Extreme Tail Behavior in Two‐Dimensional Monte Carlo Simulation

Two‐dimensional Monte Carlo simulation is frequently used to implement probabilistic risk models, as it allows for uncertainty and variability to be quantified separately. In many cases, we are interested in the proportion of individuals from a variable population exceeding a critical threshold, together with uncertainty about this proportion. In this article we introduce a new method that can accurately estimate these quantities much more efficiently than conventional algorithms. We also show how those model parameters having the greatest impact on the probabilities of rare events can be quickly identified via this method. The algorithm combines elements from well‐established statistical techniques in extreme value theory and Bayesian analysis of computer models. We demonstrate the practical application of these methods with a simple example, in which the true distributions are known exactly, and also with a more realistic model of microbial contamination of milk with seven parameters. For the latter, sensitivity analysis (SA) is shown to identify the two inputs explaining the majority of variation in distribution tail behavior. In the subsequent prediction of probabilities of large contamination events, similar results are obtained using the new approach taking 43 seconds or the conventional simulation that requires more than 3 days.     

Of those who plan: A meta-analysis of the relationship between human capital and business planning

Business planning is a popular managerial practice, especially in the SME context. Yet, literature presents diverging perspectives regarding business planning and the role it fulfills for the individuals leading these organizations. To advance research, we focus on evidence regarding factors that determine whether and how individuals engage in business planning. Drawing on human capital concepts and the theory of planned behavior, we scrutinize how education and different prominent work experience types influence business planning behaviors. We use meta-analysis to aggregate data on 8095 observations leading SMEs from 31 independent data sets. We find critical differences in planning behaviors relating to human capital types, as well as to whether individuals engage in substantive planning processes or the specific preparation of a formal business plan. Implications of our findings for research on business planning are discussed.     

Exposure Assessment for Pesticide Intake from Multiple Food Products: A Bayesian Latent‐Variable Approach

Pesticide risk assessment for food products involves combining information from consumption and concentration data sets to estimate a distribution for the pesticide intake in a human population. Using this distribution one can obtain probabilities of individuals exceeding specified levels of pesticide intake. In this article, we present a probabilistic, Bayesian approach to modeling the daily consumptions of the pesticide Iprodione though multiple food products. Modeling data on food consumption and pesticide concentration poses a variety of problems, such as the large proportions of consumptions and concentrations that are recorded as zero, and correlation between the consumptions of different foods. We consider daily food consumption data from the Netherlands National Food Consumption Survey and concentration data collected by the Netherlands Ministry of Agriculture. We develop a multivariate latent‐Gaussian model for the consumption data that allows for correlated intakes between products. For the concentration data, we propose a univariate latent‐t model. We then combine predicted consumptions and concentrations from these models to obtain a distribution for individual daily Iprodione exposure. The latent‐variable models allow for both skewness and large numbers of zeros in the consumption and concentration data. The use of a probabilistic approach is intended to yield more robust estimates of high percentiles of the exposure distribution than an empirical approach. Bayesian inference is used to facilitate the treatment of data with a complex structure.     

Integrating a Top‐Down and a Bottom‐Up Approach: Formal and Informal Risk‐Handling Strategies in a Utility Company

In this study, we bring together a top‐down and a bottom‐up approach of risk handling. We do so by conceptualizing and qualitatively and quantitatively measuring formal and informal risk‐handling strategies in a Dutch utility company. We conceive of formal risk handling as regulating, training, and educating safety and enforcing rule compliance, while we distinguish three different informal risk‐handling strategies: discretionary specialization, tacit knowledge, and taking personal responsibility. We show that the formal risk‐handling strategy and the three informal risk‐handling strategies can be measured separately. Hence, we have validated the measurement of all four strategies derived from two different risk‐handling approaches. Moreover, we have demonstrated that the perceived use of the four strategies has different effects on unsafe behavior: formal risk handling and tacit knowledge decrease it, discretion increases it, and taking personal responsibility has no effect on unsafe behavior.     

Morality rebooted: Exploring simple fixes to our moral bugs

Ethics research developed partly in response to calls from organizations to understand and solve unethical behavior. We examine two approaches to mitigating unethical behavior: (1) values-oriented approaches that broadly appeal to individuals’ preferences to be more moral, and (2) structure-oriented approaches that redesign specific incentives, tasks, and decisions to reduce temptations to cheat in the environment. This paper explores how these approaches can change behavior. We argue that integrating both approaches while avoiding incompatible strategies can reduce the risk of adverse effects that arise from taking a single approach and leverage the strengths of both approaches.     

Let's Call it Quits: Break‐Even Effects in the Decision to Stop Taking Risks

“Chasing” behavior, whereby individuals, driven by a desire to break even, continue a risky activity (RA) despite incurring large losses, is a commonly observed phenomenon. We examine whether the desire to break even plays a wider role in decisions to stop engaging in financially motivated RA in a naturalistic setting. We test hypotheses, motivated by this research question, using a large data set: 707,152 transactions of 5,379 individual financial market spread traders between September 2004 and April 2013. The results indicate strong effects of changes in wealth around the break‐even point on the decision to cease an RA. An important mediating factor was the individual's historical long‐term performance. Those with a more profitable trading history were less affected by a fall in cash balance below the break‐even point compared to those who had been less profitable. We observe that break‐even points play an important role in the decision of nonpathological risk takers to stop RAs. It is possible, therefore, that these nonpathological cognitive processes, when occurring in extrema, may result in pathological gambling behavior such as “chasing.” Our data set focuses on RAs in financial markets and, consequently, we discuss the implications for institutions and regulators in the effective management of risk taking in markets. We also suggest that there may be a need to consider carefully the nature and role of “break‐even points” associated with a broader range of nonfinancially‐focused risk‐taking activities, such as smoking and substance abuse.     

How Should Autonomous Cars Drive? A Preference for Defaults in Moral Judgments Under Risk and Uncertainty

Autonomous vehicles (AVs) promise to make traffic safer, but their societal integration poses ethical challenges. What behavior of AVs is morally acceptable in critical traffic situations when consequences are only probabilistically known (a situation of risk) or even unknown (a situation of uncertainty)?  How do people retrospectively evaluate the behavior of an AV in situations in which a road user has been harmed? We addressed these questions in two empirical studies (N = 1,638) that approximated the real‐world conditions under which AVs operate by varying the degree of risk and uncertainty of the situation. In Experiment 1, subjects learned that an AV had to decide between staying in the lane or swerving. Each action could lead to a collision with another road user, with some known or unknown likelihood. Subjects’ decision preferences and moral judgments varied considerably with specified probabilities under risk, yet less so under uncertainty. The results suggest that staying in the lane and performing an emergency stop is considered a reasonable default, even when this action does not minimize expected loss. Experiment 2 demonstrated that if an AV collided with another road user, subjects’ retrospective evaluations of the default action were also more robust against unwanted outcome and hindsight effects than the alternative swerve maneuver. The findings highlight the importance of investigating moral judgments under risk and uncertainty in order to develop policies that are societally acceptable even under critical conditions.     

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

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.     

Establishing dominance between strategies with interval judgments of state probabilities

In decision-making under uncertainty, a decision-maker is required to specify, possibly with the help of decision analysts, point estimates of the probabilities of uncertain events. In this setting, it is often difficult to obtain very precise measurements of the decision-maker׳s probabilities on the states of nature particularly when little information is available to evaluate probabilities, available information is not specific enough, or we have to model the conflict case where several information sources are available.In this paper, imprecise probabilities are considered for representing the decision-maker׳s perception or past experience about the states of nature, to be specific, interval probabilities, which can be further categorized as (a) intervals of individual probabilities, (b) intervals of probability differences, and (c) intervals of ratio probabilities. We present a heuristic approach to modeling a wider range of types of probabilities as well as three types of interval probabilities. In particular, the intervals of ratio probabilities, which are widely used in the uncertain AHP context, are analyzed to find extreme points by the use of change of variables, not to mention the first two types of interval probabilities. Finally, we examine how these extreme points can be used to determine an ordering or partial ordering of the expected values of strategies.     

Using Concurrent Verbal Reports to Examine Data Analyst Verbal Behavior

Abstract

Evidence concerning effects of graphed data on the verbal behavior of data analysts was reviewed and revealed the fact that no verbal protocol analyses of this behavior appears in the OBM literature. A task analysis of data analysts' verbal behavior was conducted, verbal responses of two expert and two novice data analysts were collected as they viewed time-series data one data point at a time from three data sets, and these data were subjected to protocol analyses. Results were the following: (1) experienced data analysts made considerably more frequent references to data points in addition to the most recently presented data point for all three data sets than did novices, (2) experienced data analysts took more time to complete the task than did novice analysts, and (3) experienced data analysts were somewhat more accurate at identifying onset (location in time) of interventions. These results were discussed in terms of their implications for future research concerning analysis and development of visual inspection skills among data analysts. It was concluded that there exists a need to develop data sets or tasks that more reliably discriminate among expert and novice performances if verbal protocol methodology is to be fully exploited by OBM researchers.     

A Flexible Count Data Regression Model for Risk Analysis

In many cases, risk and reliability analyses involve estimating the probabilities of discrete events such as hardware failures and occurrences of disease or death. There is often additional information in the form of explanatory variables that can be used to help estimate the likelihood of different numbers of events in the future through the use of an appropriate regression model, such as a generalized linear model. However, existing generalized linear models (GLM) are limited in their ability to handle the types of variance structures often encountered in using count data in risk and reliability analysis. In particular, standard models cannot handle both underdispersed data (variance less than the mean) and overdispersed data (variance greater than the mean) in a single coherent modeling framework. This article presents a new GLM based on a reformulation of the Conway-Maxwell Poisson (COM) distribution that is useful for both underdispersed and overdispersed count data and demonstrates this model by applying it to the assessment of electric power system reliability. The results show that the proposed COM GLM can provide as good of fits to data as the commonly used existing models for overdispered data sets while outperforming these commonly used models for underdispersed data sets.     

When Opportunity Matters: Comparing the Risk‐Taking Attitudes of Prisoners and Recently Released Ex‐Prisoners

Risk‐taking tendencies and environmental opportunities to commit crime are two key features in understanding criminal behavior. Upon release from prison, ex‐prisoners have a much greater opportunity to engage in risky activity and to commit criminal acts. We hypothesized that ex‐prisoners would exhibit greater risk‐taking tendencies compared to prisoners who have fewer opportunities to engage in risky activity and who are monitored constantly by prison authorities. Using cumulative prospect theory to compare the risky choices of prisoners and ex‐prisoners our study revealed that ex‐prisoners who were within 16 weeks of their prison release made riskier choices than prisoners. Our data indicate that previous studies comparing prisoners behind bars with nonoffenders may have underestimated the risk‐taking tendencies of offenders. The present findings emphasize the central role played by risk‐taking attitudes in criminal offending and highlight a need to examine offenders after release from prison.     

Tolerance for Failure and Incentives for Collaborative Innovation

Most organizations employ collaborative teams to manage innovation projects. Although the use of collaborative innovation teams is a good starting point, an organization's ability to innovate can be enhanced by managing risk‐taking behavior through monetary incentive schemes and through an organizational culture that tolerates failure. This article reports the results of two controlled experiments aimed at understanding how tolerance for failure and incentives impact the decisions of individuals engaged in a collaborative innovation initiative. A key element of our experiments is the notion of endogenous project risk, which we define as the explicit link between resources allocated to a project and the likelihood of project success. We observe that when penalties are low, the amount of risk an individual assumes is fairly insensitive to the rewards that are offered. In an analogous result, when individuals make decisions alone (rather than collaboratively), higher tolerance for failure does little to increase the amount of risk an individual is willing to take. Taken together, these results highlight the importance of implicit incentives that are created as a result of project and organizational characteristics.     

Failure Probability Under Parameter Uncertainty

In many problems of risk analysis, failure is equivalent to the event of a random risk factor exceeding a given threshold. Failure probabilities can be controlled if a decisionmaker is able to set the threshold at an appropriate level. This abstract situation applies, for example, to environmental risks with infrastructure controls; to supply chain risks with inventory controls; and to insurance solvency risks with capital controls. However, uncertainty around the distribution of the risk factor implies that parameter error will be present and the measures taken to control failure probabilities may not be effective. We show that parameter uncertainty increases the probability (understood as expected frequency) of failures. For a large class of loss distributions, arising from increasing transformations of location‐scale families (including the log‐normal, Weibull, and Pareto distributions), the article shows that failure probabilities can be exactly calculated, as they are independent of the true (but unknown) parameters. Hence it is possible to obtain an explicit measure of the effect of parameter uncertainty on failure probability. Failure probability can be controlled in two different ways: (1) by reducing the nominal required failure probability, depending on the size of the available data set, and (2) by modifying of the distribution itself that is used to calculate the risk control. Approach (1) corresponds to a frequentist/regulatory view of probability, while approach (2) is consistent with a Bayesian/personalistic view. We furthermore show that the two approaches are consistent in achieving the required failure probability. Finally, we briefly discuss the effects of data pooling and its systemic risk implications.     

Estimation of a Benchmark Dose in the Presence or Absence of Hormesis Using Posterior Averaging

U.S. Environment Protection Agency benchmark doses for dichotomous cancer responses are often estimated using a multistage model based on a monotonic dose‐response assumption. To account for model uncertainty in the estimation process, several model averaging methods have been proposed for risk assessment. In this article, we extend the usual parameter space in the multistage model for monotonicity to allow for the possibility of a hormetic dose‐response relationship. Bayesian model averaging is used to estimate the benchmark dose and to provide posterior probabilities for monotonicity versus hormesis. Simulation studies show that the newly proposed method provides robust point and interval estimation of a benchmark dose in the presence or absence of hormesis. We also apply the method to two data sets on carcinogenic response of rats to 2,3,7,8‐tetrachlorodibenzo‐p‐dioxin.     

Direct Inference, Probability, and a Conceptual Gulf in Risk Communication

Differences in the conceptual frameworks of scientists and nonscientists may create barriers to risk communication. This article examines two such conceptual problems. First, the logic of "direct inference" from group statistics to probabilities about specific individuals suggests that individuals might be acting rationally in refusing to apply to themselves the conclusions of regulatory risk assessments. Second, while regulators and risk assessment scientists often use an "objectivist" or "relative frequency" interpretation of probability statements, members of the public are more likely to adopt a "subjectivist" or "degree of confidence" interpretation when estimating their personal risks, and either misunderstand or significantly discount the relevance of risk assessment conclusions. If these analyses of inference and probability are correct, there may be a conceptual gulf at the center of risk communication that cannot be bridged by additional data about the magnitude of group risk. Suggestions are made for empirical studies that might help regulators deal with this conceptual gulf.     

Workers’ Risk Tolerance and Occupational Injuries

This study explores the relationship between individuals’ risk tolerance and occupational injuries. We analyze data from a national representative survey of U.S. workers that includes information about injuries, risk tolerance, cognitive and noncognitive attributes, and risky behaviors. We measure risk tolerance through questions regarding individuals’ willingness to gamble on their lifetime income. We estimate zero‐inflated count models to assess the role played by such measures on workers’ recurrent injuries. We discuss some implications of our results for future research and occupational safety policies. Our results highlight the concurrent and changing role played by individual, work, and environmental factors in explaining recurrent incidents. They show that risk tolerance affects recurrent injuries, although not in the direction that proponents of the concept of proneness would expect. Our measure of risk aversion shows that individuals who are somewhat more risk tolerant have fewer recurrent injuries than those who are risk averse. But the estimated relationship is U‐shaped, not monotonic and, therefore, not easy to predict. At the same time, we find that individuals’ “revealed risk preferences”—specific risky behaviors—are related to higher injury probabilities. Demanding working conditions, measures of socioeconomic status, health, and safety problems experienced by workers during their youth remain among the most important factors explaining the phenomena of recurrent injuries. So our results contribute also to the important debate about the relationship between health and socioeconomic status.     

An Expected Consequence Approach to Route Choice in the Maritime Transportation of Crude Oil

Maritime transportation is the major conduit of international trade, and the primary link for global crude oil movement. Given the volume of oil transported on international maritime links, it is not surprising that oil spills of both minor and major types result, although most of the risk‐related work has been confined to the local settings. We propose an expected consequence approach for assessing oil‐spill risk from intercontinental transportation of crude oil that not only adheres to the safety guidelines specified by the International Maritime Organization but also outlines a novel technique that makes use of coarse global data to estimate accident probabilities. The proposed estimation technique, together with four of the most popular cost‐of‐spill models from the literature, were applied to study and analyze a realistic size problem instance. Numerical analyses showed that: a shorter route may not necessarily be less risky; an understanding of the inherent oil‐spill risk of different routes could potentially facilitate tanker routing decisions; and the associated negotiations over insurance premium between the transport company and the not‐for‐profit prevention and indemnity clubs. Finally, we note that only the linear model should be used with one of the three nonlinear cost‐of‐spill models for evaluating tanker routes.     

A new approach to risk evaluation and management: risk-based, precaution-based, and discourse-based strategies.

Our concept of nine risk evaluation criteria, six risk classes, a decision tree, and three management categories was developed to improve the effectiveness, efficiency, and political feasibility of risk management procedures. The main task of risk evaluation and management is to develop adequate tools for dealing with the problems of complexity, uncertainty. and ambiguity. Based on the characteristics of different risk types and these three major problems, we distinguished three types of management--risk-based, precaution-based, and discourse-based strategies. The risk-based strategy--is the common solution to risk problems. Once the probabilities and their corresponding damage potentials are calculated, risk managers are required to set priorities according to the severity of the risk, which may be operationalized as a linear combination of damage and probability or as a weighted combination thereof. Within our new risk classification, the two central components have been augmented with other physical and social criteria that still demand risk-based strategies as long as uncertainty is low and ambiguity absent. Risk-based strategies are best solutions to problems of complexity and some components of uncertainty, for example, variation among individuals. If the two most important risk criteria, probability of occurrence and extent of damage, are relatively well known and little uncertainty is left, the traditional risk-based approach seems reasonable. If uncertainty plays a large role, in particular, indeterminacy or lack of knowledge, the risk-based approach becomes counterproductive. Judging the relative severity of risks on the basis of uncertain parameters does not make much sense. Under these circumstances, management strategies belonging to the precautionary management style are required. The precautionary approach has been the basis for much of the European environmental and health protection legislation and regulation. Our own approach to risk management has been guided by the proposition that any conceptualization of the precautionary principle should be (1) in line with established methods of scientific risk assessments, (2) consistent and discriminatory (avoiding arbitrary results) when it comes to prioritization, and (3) at the same time, specific with respect to precautionary measures, such as ALARA or BACT, or the strategy of containing risks in time and space. This suggestion does, however, entail a major problem: looking only to the uncertainties does not provide risk managers with a clue about where to set priorities for risk reduction. Risks vary in their degree of remaining uncertainties. How can one judge the severity of a situation when the potential damage and its probability are unknown or contested? In this dilemma, we advise risk managers to use additional criteria of hazardousness, such as "ubiquity versibility," and "pervasiveness over time," as proxies for judging severity. Our approach also distinguishes clearly between uncertainty and ambiguity. Uncertainty refers to a situation of being unclear about factual statements; ambiguity to a situation of contested views about the desirability or severity of a given hazard. Uncertainty can be resolved in principle by more cognitive advances (with the exception of indeterminacy). ambiguity only by discourse. Discursive procedures include legal deliberations as well as novel participatory approaches. In addition, discursive methods of planning and conflict resolution can be used. If ambiguities are associated with a risk problem, it is not enough to demonstrate that risk regulators are open to public concerns and address the issues that many people wish them to take care ot The process of risk evaluation itself needs to be open to public input and new forms of deliberation. We have recommended a tested set of deliberative processes that are, at least in principle, capable of resolving ambiguities in risk debates (for a review, see Renn, Webler, & Wiedemaun. 1995). Deliberative processes are needed, however, for ail three types of management. Risk-based management relies on epistemiological, uncertainty-based management on reflective, and discourse-based management on participatory discourse forms. These three types of discourse could be labeled as an analytic-deliberative procedure for risk evaluation and management. We see the advantage of a deliberative style of regulation and management in a dynamic balance between procedure and outcome. Procedure should not have priority over the outcome; outcome should not have priority over the procedure. An intelligent combination of both can elaborate the required prerequisites of democratic deliberation and its substantial outcomes to enhance the legitimacy of political decisions (Guttman & Thompson, 1996; Bohman, 1997. 1998).     

Values and behavior orientation in leadership studies: reflections based on findings in three German-speaking countries

The article focuses on two sets of concepts commonly used in the explanation of leadership behavior, including values in one area and behavioral intentions in the other. Most leadership researches concentrate exclusively on one or the other area and there are few integrative approaches. The literature also shows that there are often implicit assumptions of a direct and linear relationship between the two sets of concepts. We introduce a model reflecting this research dichotomy by differentiating between “far-from-action” concepts like values or leadership ideals and “close-to-action” concepts like intended behavior. In a next step, we question the assumption of a direct and linear link between the two sets of concepts and discuss two studies in order to find out whether there is empirical support for our argument. We compare empirical findings on leadership ideals (GLOBE study) and intended leadership behavior (Vroom/Yetton study) collected from managers in the German-speaking countries Austria, Germany, and Switzerland. The results show inconsistencies that suggest quite complex intrapersonal processes influenced by cultural factors. We conclude that the link between “far-from-action” and “close-to-action” concepts is worth pursuing further and expands the initial model, thus paving the way for a more holistic understanding of the intrapersonal processes on which leadership behavior is based. Finally, we discuss the consequences of these processes for the stability of leadership behavior.