Psychological Mediators of the Sport Injury—Perceived Risk Relationship

Drawing upon both heuristic‐ and threat‐based approaches, we sought to examine whether perceived similarity with injury‐prone people and perceived control over injury occurrence would directly contribute to perceived risk and whether these variables would mediate the previous injury–perceived risk relationship. Judokas (n = 207) reported the number of injuries experienced in the past year and then completed measures of perceived similarity, perceived control, and injury risk perception. Analyses revealed that perceived similarity and perceived control directly contributed to perceived risk of injury; only perceived similarity acted as a partial mediator of the injury–perceived risk relationship. These findings are discussed in relation to the potential influence of the sport context, which universally involves the acceptance of a high risk of injury.     

同群效应对创业活动影响的模型构建与实证

本文从社会个体与群体行为互动的视角研究个体的创业活动决策机理,分析认为,个体因受到周围创业者冒险拼搏精神的影响而进行创业,或者通过与周围创业者交往的社会学习得到与创业相关的技术或者经验从而自身也投入创业,二者均使得创业活动具有同群效应。进一步基于中国家庭追踪调查（CFPS）截面数据进行检验的结果显示：（1）个体的创业活动存在显著同群效应,即邻里的创业活动对个体产生了明显的示范作用,带动其投入到创业活动中;（2）信息时代因电视、互联网等媒体传播应用对于传统物理意义上的同群效应存在一定程度的替代。文章采用了工具变量法解决了关联效应和反射性问题对同群效应的估计带来的影响,以确保同群效应的有效识别。上述发现说明,尽管创业活动是市场参与个体的自由选择行为,但各种途径的宣传,包括社区宣传和各种媒体宣传对于推动大众创业有不可或缺的作用。     

LinkIT: A Ludic Elicitation Game for Eliciting Risk Perceptions

The mental models approach, a leading strategy to develop risk communications, involves a time‐ and labor‐intensive interview process and a lengthy questionnaire to elicit group‐level risk perceptions. We propose that a similarity ratings approach for structural knowledge elicitation can be adopted to assist the risk mental models approach. The LinkIT game, inspired by games with a purpose (GWAP) technology, is a ludic elicitation tool designed to elicit group understanding of the relations between risk factors in a more enjoyable and productive manner when compared to traditional approaches. That is, consistent with the idea of ludic elicitation, LinkIT was designed to make the elicitation process fun and enjoyable in the hopes of increasing participation and data quality in risk studies. Like the mental models approach, the group mental model obtained via the LinkIT game can hence be generated and represented in a form of influence diagrams. In order to examine the external validity of LinkIT, we conducted a study to compare its performance with respect to a more conventional questionnaire‐driven approach. Data analysis results conclude that the two group mental models elicited from the two approaches are similar to an extent. Yet, LinkIT was more productive and enjoyable than the questionnaire. However, participants commented that the current game has some usability concerns. This presentation summarizes the design and evaluation of the LinkIT game and suggests areas for future work.     

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.     

Risk Aversion,Collective Bargaining,and Wages in Germany

We develop an open‐shop model of trade union membership in which workers differ in their risk attitudes, and derive conditions under which the bargained wage will fall and union membership will increase with a general rise in risk aversion. Using data from the German Socio‐Economic Panel we define broad bargaining units and show that wages decline as average risk aversion of union members in these units rises, controlling for individual effects of risk aversion. Given a negative relationship between wages and employment, this suggests that secular changes in risk attitudes, because of an aging workforce or greater female labour force participation, can help to explain variations in the employment performance of unionized economies.     

Identification and Estimation of a Discrete Game of Complete Information

We discuss the identification and estimation of discrete games of complete information. Following Bresnahan and Reiss (1990, 1991), a discrete game is a generalization of a standard discrete choice model where utility depends on the actions of other players. Using recent algorithms to compute all of the Nash equilibria to a game, we propose simulation‐based estimators for static, discrete games. We demonstrate that the model is identified under weak functional form assumptions using exclusion restrictions and an identification at infinity approach. Monte Carlo evidence demonstrates that the estimator can perform well in moderately sized samples. As an application, we study entry decisions by construction contractors to bid on highway projects in California. We find that an equilibrium is more likely to be observed if it maximizes joint profits, has a higher Nash product, uses mixed strategies, and is not Pareto dominated by another equilibrium.     

Multiple Interacting Risk Factors: On Methods for Allocating Risk Factor Interactions

A persistent problem in health risk analysis where it is known that a disease may occur as a consequence of multiple risk factors with interactions is allocating the total risk of the disease among the individual risk factors. This problem, referred to here as risk apportionment, arises in various venues, including: (i) public health management, (ii) government programs for compensating injured individuals, and (iii) litigation. Two methods have been described in the risk analysis and epidemiology literature for allocating total risk among individual risk factors. One method uses weights to allocate interactions among the individual risk factors. The other method is based on risk accounting axioms and finding an optimal and unique allocation that satisfies the axioms using a procedure borrowed from game theory. Where relative risk or attributable risk is the risk measure, we find that the game‐theory‐determined allocation is the same as the allocation where risk factor interactions are apportioned to individual risk factors using equal weights. Therefore, the apportionment problem becomes one of selecting a meaningful set of weights for allocating interactions among the individual risk factors. Equal weights and weights proportional to the risks of the individual risk factors are discussed.     

A Diversity Index for Model Space Selection in the Estimation of Benchmark and Infectious Doses via Model Averaging

In chemical and microbial risk assessments, risk assessors fit dose‐response models to high‐dose data and extrapolate downward to risk levels in the range of 1–10%. Although multiple dose‐response models may be able to fit the data adequately in the experimental range, the estimated effective dose (ED) corresponding to an extremely small risk can be substantially different from model to model. In this respect, model averaging (MA) provides more robustness than a single dose‐response model in the point and interval estimation of an ED. In MA, accounting for both data uncertainty and model uncertainty is crucial, but addressing model uncertainty is not achieved simply by increasing the number of models in a model space. A plausible set of models for MA can be characterized by goodness of fit and diversity surrounding the truth. We propose a diversity index (DI) to balance between these two characteristics in model space selection. It addresses a collective property of a model space rather than individual performance of each model. Tuning parameters in the DI control the size of the model space for MA.     

Too Scared or Too Capable? Why Do College Students Stay Away from the H1N1 Vaccine?

Although college students were among the populations that had the highest frequency of infection for H1N1 influenza, only 8% of them received H1N1 vaccine this past flu season nationwide. During the peak of this pandemic, information about H1N1 vaccine was widely available. However, knowledge test and behavioral data indicated that most college students were not equipped with basic facts about H1N1 and the H1N1 vaccine. To investigate socio‐psychological factors that might have deterred this high‐risk population from learning about and getting the H1N1 vaccine, this study tested the utility of a risk information seeking model in addressing this health communication problem. Data collected from an online survey of 371 college students showed that respondents seemed to overestimate how much they knew about the vaccine. Risk information seeking, however, positively influenced their intentions to get the vaccine. Results suggested that to communicate effectively to this population, it is important to emphasize the difference between perceived knowledge and actual knowledge, monitor emotional responses to potential risks, and promote getting flu vaccination as a socially desirable behavior.     

Using Theory to Identify Beliefs Associated with Intentions to Follow Fish Consumption Advisories Among Anglers Living in the Great Lakes Region

Fish consumption advisories are issued by states, tribes, and federal agencies to provide guidance to consumers about eating sport‐caught fish potentially affected by chemical contaminants. Previous work has found that while anglers report being aware that advisories are available, awareness and use of specific advisory recommendations is low. This study uses the Integrative Model of Behavioral Prediction (IMBP) to identify beliefs with potential to increase intentions to follow fish consumption advisories in Great Lakes states. We conducted a mail survey of 1,712 licensed anglers in seven of eight Great Lakes states (excluding Ohio) to gauge advisory awareness, cognitive factors influencing fish consumption behaviors (informed by the IMBP), and sociodemographic characteristics. Results show that most anglers reported being generally or vaguely aware of fish consumption advisories and try to follow them, but far fewer report being aware of specific advice needed to decide whether or not to consume different types of sport‐caught fish. Informed by the IMBP, we also identify several behavioral, normative, and control beliefs that have sufficient room to change, strong associations with intentions to follow the advisories, and potential to be modified if targeted with strategic risk messages. Targeting these beliefs with strategic communication holds potential to increase the proportion of anglers intending to follow fish consumption advisory recommendations in choosing which fish to eat.     

Nonparametric Identification of Risk Aversion in First‐Price Auctions Under Exclusion Restrictions

This paper studies the nonparametric identification of the first‐price auction model with risk averse bidders within the private value paradigm. First, we show that the benchmark model is nonindentified from observed bids. We also derive the restrictions imposed by the model on observables and show that these restrictions are weak. Second, we establish the nonparametric identification of the bidders' utility function under exclusion restrictions. Our primary exclusion restriction takes the form of an exogenous bidders' participation, leading to a latent distribution of private values that is independent of the number of bidders. The key idea is to exploit the property that the bid distribution varies with the number of bidders while the private value distribution does not. We then extend these results to endogenous bidders' participation when the exclusion restriction takes the form of instruments that do not affect the bidders' private value distribution. Though derived for a benchmark model, our results extend to more general cases such as a binding reserve price, affiliated private values, and asymmetric bidders. Last, possible estimation methods are proposed.     

Experientia Docet: Professionals Play Minimax in Laboratory Experiments

We study how professional players and college students play zero‐sum two‐person strategic games in a laboratory setting. We first ask professionals to play a 2 × 2 game that is formally identical to a strategic interaction situation that they face in their natural environment. Consistent with their behavior in the field, they play very close to the equilibrium of the game. In particular, (i) they equate their strategies' payoffs to the equilibrium ones and (ii) they generate sequences of choices that are serially independent. In sharp contrast, however, we find that college students play the game far from the equilibrium predictions. We then study the behavior of professional players and college students in the classic O'Neill 4 × 4 zero‐sum game, a game that none of the subjects has encountered previously, and find the same differences in the behavior of these two pools of subjects. The transfer of skills and experience from the familiar field to the unfamiliar laboratory observed for professional players is relevant to evaluate the circumstances under which behavior in a laboratory setting may be a reliable indicator of behavior in a naturally occurring setting. From a cognitive perspective, it is useful for research on recognition processes, intuition, and similarity as a basis for inductive reasoning.     

Exposure to Traffic and Risk of Hospitalization Due to Injuries

Research on the risk of motor vehicle injuries and their relationship with the amount of travel has been only partially analyzed. The few individual exposure assessments are related to very specific subsets of the driving and traveling populations. This study analyzes the relationship between kilometers traveled and hospitalization due to motor vehicle injuries. Twelve thousand three hundred and sixty nine Spanish university graduates from the Seguimiento Universidad de Navarra multipurpose cohort study were evaluated. They had not been hospitalized due to motor vehicle injuries at baseline and were followed up to eight years. Biannual questionnaires allowed for self‐reporting of kilometers traveled in motor vehicles, together with incidence of hospitalization. Covariates in the Cox regression models included age and gender and baseline use of safety belt while driving, driving a vehicle with driver‐side airbag, driving a motorcycle, and drinking and driving. There were 49,766 participant‐years with an average yearly travel of 7,828 km per person‐year. Thirty‐six subjects reported a first hospitalization event during this time. The adjusted hazard ratio per additional kilometer traveled was 1.00005 (95% confidence interval 1.000013 to 1.000086). Even the smallest of reductions in the amount of kilometers traveled (from an average of 3,250 km per year to 1,000) has a statistically significant protective effect on the likelihood of sustaining hospitalization due to motor vehicle injury (aHR 0.9, 95% CI 0.78 to 0.98). In light of current policies aimed to reduce motorized traffic due to environmental concerns, it may be appropriate to consider the additional health benefit related to reductions in injuries.     

Game Theory and Risk Analysis

Risk analysts often analyze adversarial risks from terrorists or other intelligent attackers without mentioning game theory. Why? One reason is that many adversarial situations—those that can be represented as attacker‐defender games, in which the defender first chooses an allocation of defensive resources to protect potential targets, and the attacker, knowing what the defender has done, then decides which targets to attack—can be modeled and analyzed successfully without using most of the concepts and terminology of game theory. However, risk analysis and game theory are also deeply complementary. Game‐theoretic analyses of conflicts require modeling the probable consequences of each choice of strategies by the players and assessing the expected utilities of these probable consequences. Decision and risk analysis methods are well suited to accomplish these tasks. Conversely, game‐theoretic formulations of attack‐defense conflicts (and other adversarial risks) can greatly improve upon some current risk analyses that attempt to model attacker decisions as random variables or uncertain attributes of targets (“threats”) and that seek to elicit their values from the defender's own experts. Game theory models that clarify the nature of the interacting decisions made by attackers and defenders and that distinguish clearly between strategic choices (decision nodes in a game tree) and random variables (chance nodes, not controlled by either attacker or defender) can produce more sensible and effective risk management recommendations for allocating defensive resources than current risk scoring models. Thus, risk analysis and game theory are (or should be) mutually reinforcing.     

Lessons learned from Toulouse and Buncefield disasters: from risk analysis failures to the identification of atypical scenarios through a better knowledge management

The recent occurrence of severe major accidents has brought to light flaws and limitations of hazard identification (HAZID) processes performed for safety reports, as in the accidents at Toulouse (France) and Buncefield (UK), where the accident scenarios that occurred were not captured by HAZID techniques. This study focuses on this type of atypical accident scenario deviating from normal expectations. The main purpose is to analyze the examples of atypical accidents mentioned and to attempt to identify them through the application of a well-known methodology such as the bow-tie analysis. To these aims, the concept of atypical event is accurately defined. Early warnings, causes, consequences, and occurrence mechanisms of the specific events are widely studied and general failures of risk assessment, management, and governance isolated. These activities contribute to outline a set of targeted recommendations, addressing transversal common deficiencies and also demonstrating how a better management of knowledge from the study of past events can support future risk assessment processes in the identification of atypical accident scenarios. Thus, a new methodology is not suggested; rather, a specific approach coordinating a more effective use of experience and available information is described, to suggest that lessons to be learned from past accidents can be effectively translated into actions of prevention.     

Construction Safety Risk Modeling and Simulation

By building on a genetic‐inspired attribute‐based conceptual framework for safety risk analysis, we propose a novel approach to define, model, and simulate univariate and bivariate construction safety risk at the situational level. Our fully data‐driven techniques provide construction practitioners and academicians with an easy and automated way of getting valuable empirical insights from attribute‐based data extracted from unstructured textual injury reports. By applying our methodology on a data set of 814 injury reports, we first show the frequency‐magnitude distribution of construction safety risk to be very similar to that of many natural phenomena such as precipitation or earthquakes. Motivated by this observation, and drawing on state‐of‐the‐art techniques in hydroclimatology and insurance, we then introduce univariate and bivariate nonparametric stochastic safety risk generators based on kernel density estimators and copulas. These generators enable the user to produce large numbers of synthetic safety risk values faithful to the original data, allowing safety‐related decision making under uncertainty to be grounded on extensive empirical evidence. One of the implications of our study is that like natural phenomena, construction safety may benefit from being studied quantitatively by leveraging empirical data rather than strictly being approached through a managerial perspective using subjective data, which is the current industry standard. Finally, a side but interesting finding is that in our data set, attributes related to high energy levels (e.g., machinery, hazardous substance) and to human error (e.g., improper security of tools) emerge as strong risk shapers.     

A Hybrid Methodology for Modeling Risk of Adverse Events in Complex Health‐Care Settings

In spite of increased attention to quality and efforts to provide safe medical care, adverse events (AEs) are still frequent in clinical practice. Reports from various sources indicate that a substantial number of hospitalized patients suffer treatment‐caused injuries while in the hospital. While risk cannot be entirely eliminated from health‐care activities, an important goal is to develop effective and durable mitigation strategies to render the system “safer.” In order to do this, though, we must develop models that comprehensively and realistically characterize the risk. In the health‐care domain, this can be extremely challenging due to the wide variability in the way that health‐care processes and interventions are executed and also due to the dynamic nature of risk in this particular domain. In this study, we have developed a generic methodology for evaluating dynamic changes in AE risk in acute care hospitals as a function of organizational and nonorganizational factors, using a combination of modeling formalisms. First, a system dynamics (SD) framework is used to demonstrate how organizational‐level and policy‐level contributions to risk evolve over time, and how policies and decisions may affect the general system‐level contribution to AE risk. It also captures the feedback of organizational factors and decisions over time and the nonlinearities in these feedback effects. SD is a popular approach to understanding the behavior of complex social and economic systems. It is a simulation‐based, differential equation modeling tool that is widely used in situations where the formal model is complex and an analytical solution is very difficult to obtain. Second, a Bayesian belief network (BBN) framework is used to represent patient‐level factors and also physician‐level decisions and factors in the management of an individual patient, which contribute to the risk of hospital‐acquired AE. BBNs are networks of probabilities that can capture probabilistic relations between variables and contain historical information about their relationship, and are powerful tools for modeling causes and effects in many domains. The model is intended to support hospital decisions with regard to staffing, length of stay, and investments in safety, which evolve dynamically over time. The methodology has been applied in modeling the two types of common AEs: pressure ulcers and vascular‐catheter‐associated infection, and the models have been validated with eight years of clinical data and use of expert opinion.     

考虑复杂关联情境的城市典型生命线运行风险因素识别方法研究

城市典型生命线以供热、电力、燃气系统为代表,具有公共性强、风险性高、关联性显著的特点。研究城市典型生命线运行风险因素识别问题对于相关部门防范风险具有重要意义,但现实中由多系统、多风险因素以及各类关联形成的复杂关联情境增加了解决该问题的难度。为此,本文首先构建了具有层级网络结构的风险因素识别框架,然后提出了一种考虑复杂关联情境的风险因素识别方法,将各专家针对系统关联和风险因素关联给出的语言评价信息转化为二元语义,并将决策试验与评价实验室（DEMATEL）法和Two-Additive Choquet（TAC）积分算子扩展到二元语义环境,进而实现各类关联的综合集成,从而确定风险因素的排序和归类,便于决策者研判风险根源、明晰风险因素本质。最后,以北京某样区为例验证了所提方法的潜在应用价值,并根据识别结果制定了针对性的风险防范策略,能够为相关部门联调联动开展风险防范提供决策支持。     

More Than Fear Induction: Toward an Understanding of People's Motivation to Be Well‐Prepared for Emergencies in Flood‐Prone Areas

This article examines the extent and manner to which evaluations of flood‐related precautions are affected by an individual's motivation and perception of context. It argues that the relationship between risk perception and flood risk preparedness can be fruitfully specified in terms of vulnerability and efficacy if these concepts are put into the perspective of prevention‐focused motivation. This relationship was empirically examined in a risk communication experiment in a delta area of the Netherlands (n = 1,887). Prevention‐focused motivation was induced by contextualized risk information. The results showed that prevention‐focused individuals were more sensitive to the relevance of potential precautions for satisfying their needs in the context they found themselves in. The needs included, but were not limited to, fear reduction. Due to the heterogeneity of the residents, the evaluations reflected individual differences in the intensity and the selectivity of precautionary processes. Four types of persons could be distinguished according to their evaluation of precautionary measures: a high‐scoring minority, two more selective types, and a low‐scoring minority. For policymakers and risk communicators it is vital to consider the nature of prevention motivation and the context in which it is likely to be high.