Knowledge-Based Risk Assessment Under Uncertainty for Species Invasion

Management of invasive species depends on developing prevention and control strategies through comprehensive risk assessment frameworks that need a thorough analysis of exposure to invasive species. However, accurate exposure analysis of invasive species can be a daunting task because of the inherent uncertainty in invasion processes. Risk assessment of invasive species under uncertainty requires potential integration of expert judgment with empirical information, which often can be incomplete, imprecise, and fragmentary. The representation of knowledge in classical risk models depends on the formulation of a precise probabilistic value or well-defined joint distribution of unknown parameters. However, expert knowledge and judgments are often represented in value-laden terms or preference-ordered criteria. We offer a novel approach to risk assessment by using a dominance-based rough set approach to account for preference order in the domains of attributes in the set of risk classes. The model is illustrated with an example showing how a knowledge-centric risk model can be integrated with the dominance-based principle of rough set to derive minimal covering "if ... , then...," decision rules to reason over a set of possible invasion scenarios. The inconsistency and ambiguity in the data set is modeled using the rough set concept of boundary region adjoining lower and upper approximation of risk classes. Finally, we present an extension of rough set to evidence a theoretic interpretation of risk measures of invasive species in a spatial context. In this approach, the multispecies interactions in an invasion risk are approximated with imprecise probability measures through a combination of spatial neighborhood information of risk estimation in terms of belief and plausibility.     

Health Risk,Inequality Indexes,and Environmental Justice

Inequality indexes have long been used to analyze distributions of income. Studies have recently begun to use these tools to evaluate the equity of distributions of environmental harm. In response, issues have been raised regarding the appropriateness of using income-based measures in the context of undesirable outcomes. We begin from first principles, identifying a theoretical preference structure under which income-based tools can be appropriate for ranking distributions of “bads.” While some critiques of existing applications are valid, they are not a justification for rejecting the approach altogether. Instead, we show how standard income-based measures can be adjusted to accommodate bad outcomes. Rather than inequality indexes, we argue that equally distributed equivalents (EDEs) are well-suited for this purpose since they account for levels and dispersion of outcome distributions. The Kolm–Pollak EDE is particularly useful, having the advantage of consistently evaluating both bads and their complementary goods (e.g., mortality risk and survival probability). As an illustration, we show how these tools can inform an environmental justice analysis of a proposed Environmental Protection Agency (EPA) rule addressing indoor air pollution.     

Cautious Expected Utility and the Certainty Effect

Many violations of the independence axiom of expected utility can be traced to subjects' attraction to risk‐free prospects. The key axiom in this paper, negative certainty independence ([Dillenberger, 2010]), formalizes this tendency. Our main result is a utility representation of all preferences over monetary lotteries that satisfy negative certainty independence together with basic rationality postulates. Such preferences can be represented as if the agent were unsure of how to evaluate a given lottery p; instead, she has in mind a set of possible utility functions over outcomes and displays a cautious behavior: she computes the certainty equivalent of p with respect to each possible function in the set and picks the smallest one. The set of utilities is unique in a well defined sense. We show that our representation can also be derived from a “cautious” completion of an incomplete preference relation.     

COMMON COMPONENTS OF RISK AND UNCERTAINTY ATTITUDES ACROSS CONTEXTS AND DOMAINS: EVIDENCE FROM 30 COUNTRIES

Attitudes towards risk and uncertainty have been indicated to be highly context‐dependent, and to be sensitive to the measurement technique employed. We present data collected in controlled experiments with 2,939 subjects in 30 countries measuring risk and uncertainty attitudes through incentivized measures as well as survey questions. Our data show clearly that measures correlate not only within decision contexts or measurement methods, but also across contexts and methods. This points to the existence of one underlying “risk preference”, which influences attitudes independently of the measurement method or choice domain. We furthermore find that answers to a general and a financial survey question correlate with incentivized lottery choices in most countries. Incentivized and survey measures also correlate significantly between countries. This opens the possibility to conduct cultural comparisons on risk attitudes using survey instruments.     

A Unique Costly Contemplation Representation

We study preferences over menus which can be represented as if the individual is uncertain of her tastes, but is able to engage in costly contemplation before selecting an alternative from a menu. Since contemplation is costly, our key axiom, aversion to contingent planning, reflects the individual's preference to learn the menu from which she will be choosing prior to engaging in contemplation about her tastes for the alternatives. Our representation models contemplation strategies as subjective signals over a subjective state space. The subjectivity of the state space and the information structure in our representation makes it difficult to identify them from the preference. To overcome this issue, we show that each signal can be modeled in reduced form as a measure over ex post utility functions without reference to a state space. We show that in this reduced‐form representation, the set of measures and their costs are uniquely identified. Finally, we provide a measure of comparative contemplation costs and characterize the special case of our representation where contemplation is costless.     

Consistency of Higher Order Risk Preferences

Risk aversion (a second‐order risk preference) is a time‐proven concept in economic models of choice under risk. More recently, the higher order risk preferences of prudence (third‐order) and temperance (fourth‐order) also have been shown to be quite important. While a majority of the population seems to exhibit both risk aversion and these higher order risk preferences, a significant minority does not. We show how both risk‐averse and risk‐loving behaviors might be generated by a simple type of basic lottery preference for either (1) combining “good” outcomes with “bad” ones, or (2) combining “good with good” and “bad with bad,” respectively. We further show that this dichotomy is fairly robust at explaining higher order risk attitudes in the laboratory. In addition to our own experimental evidence, we take a second look at the extant laboratory experiments that measure higher order risk preferences and we find a fair amount of support for this dichotomy. Our own experiment also is the first to look beyond fourth‐order risk preferences, and we examine risk attitudes at even higher orders.     

Explainable deep learning powered building risk assessment model for proactive hurricane response

Climate change and rapid urban development have intensified the impact of hurricanes, especially on the Southeastern Coasts of the United States. Localized and timely risk assessments can facilitate coastal communities’ preparedness and response to imminent hurricanes. Existing assessment methods focused on hurricane risks at large spatial scales, which were not specific or could not provide actionable knowledge for residents or property owners. Fragility functions and other widely utilized assessment methods cannot model the complex relationships between building features and hurricane risk levels effectively. Therefore, we develop and test a building-level hurricane risk assessment with deep feedforward neural network (DFNN) models. The input features of DFNN models cover the meta building characteristics, fine-grained meteorological, and hydrological environmental parameters. The assessment outcomes, that is, risk levels, include the probability and intensity of building/property damages induced by wind and surge hazards. We interpret the DFNN models with local interpretable model-agnostic explanations (LIME). We apply the DFNN models to a case building in Cameron County, Louisiana in response to a hypothetical imminent hurricane to illustrate how the building's risk levels can be timely assessed with the updating weather forecast. This research shows the potential of deep-learning models in integrating multi-sourced features and accurately predicting buildings’ risks of weather extremes for property owners and households. The AI-powered risk assessment model can help coastal populations form appropriate and updating perceptions of imminent hurricanes and inform actionable knowledge for proactive risk mitigation and long-term climate adaptation.     

Mean‐Deviation Analysis in the Theory of Choice

Mean‐deviation analysis, along with the existing theories of coherent risk measures and dual utility, is examined in the context of the theory of choice under uncertainty, which studies rational preference relations for random outcomes based on different sets of axioms such as transitivity, monotonicity, continuity, etc. An axiomatic foundation of the theory of coherent risk measures is obtained as a relaxation of the axioms of the dual utility theory, and a further relaxation of the axioms are shown to lead to the mean‐deviation analysis. Paradoxes arising from the sets of axioms corresponding to these theories and their possible resolutions are discussed, and application of the mean‐deviation analysis to optimal risk sharing and portfolio selection in the context of rational choice is considered.     

A Model of Utility Smoothing

Experimental studies have found that a decision maker prefers spreading good and bad outcomes evenly over time. We propose, in an axiomatic framework, a new model of discount factors that captures this preference for spread. The model provides a refinement of the discounted utility model while maintaining dynamic consistency. The derived discount factors incorporate gain/loss asymmetry recursively: the difference between average future utility and current utility defines a gain or a loss, and gains are discounted more than losses. This notion of utility smoothing can induce a preference for spread: if bad outcomes are concentrated on future periods, moving one of the bad outcomes to today would be beneficial because such an operation eliminates a large loss and replaces it with a small gain.     

Market segmentation: A multiple criteria approach combining preference analysis and segmentation decision

We propose a new multiple criteria decision aiding approach for market segmentation that integrates preference analysis and segmentation decision within a unified framework. The approach employs an additive value function as the preference model and requires consumers to provide pairwise comparisons of some products as the preference information. To analyze each consumer’s preferences, the approach applies the disaggregation paradigm and the stochastic multicriteria acceptability analysis to derive a set of value functions according to the preference information provided by each consumer. Then, each consumer’s preferences can be represented by the distribution of possible rankings of products and associated support degrees by applying the derived value functions. On the basis of preference analysis, a new metric is proposed to measure the similarity between preferences of different consumers, and a hierarchical clustering algorithm is developed to perform market segmentation. To help firms serve consumers from different segments with targeted marketing policies and appropriate products, the approach proposes to work out a representative value function and the univocal ranking of products for each consumer so that products that rank in the front of the list can be presented to her/him. Finally, an illustrative example of a market segmentation problem details the application of the proposed approach.     

RUTA: A framework for assessing and selecting additive value functions on the basis of rank related requirements

In this paper, we present a new preference disaggregation method, called RUTA, which infers a set of additive value functions from the preference information referring to the desired ranks of some reference alternatives. Real-life experience indicates that people willingly refer to the range of allowed ranks that a particular alternative should attain, or to constraints on the final scores of the alternatives. We develop a mathematical model for incorporating such preference information via mixed-integer linear programming (MILP). Then, we discuss how decision making could be supported with the use of the already proposed extreme ranking analysis (ERA), which indicates the best and worst ranks gained by each alternative over the set of compatible preference model instances. We also introduce a new interactive UTA-like technique, which aims at selecting a single value function representing the outcomes of ERA. In the interactive process, the decision maker (DM) is assigning priorities to different pre-defined targets, which are built on results of ERA, and refer to the comparison of the best and/or worst ranks for pairs of alternatives. In particular, the DM may choose to emphasize or neglect the advantage of some alternatives over the others, in terms of results of ERA. In this way, one obtains a synthetic representation of extreme ranking analysis at a higher level of abstraction.     

Safety climate and safety outcomes: A meta-analytic comparison of universal vs. industry-specific safety climate predictive validity

Previous research has demonstrated that safety climate is a robust predictor of safety-related outcomes. However, there is little consensus about the optimal strategy to measure safety climate. One of the main issues has been whether safety climate measures should be universal or industry-specific. As such, this study was designed to examine the criterion-related validity of universal and industry-specific safety climate measures by conducting a meta-analytic comparison of their relationships with a variety of safety-related outcomes (i.e. safety behaviour, risk perceptions, accidents and injuries, and other adverse events). With 120 independent samples (N?=?81,213), we found that the industry-specific safety climate measures displayed better predictive power when predicting safety behaviour and risk perceptions than the universal safety climate measures. On the other hand, the universal safety climate measures displayed better predictive power when predicting other adverse events (but not accidents and injuries) than the industry-specific safety climate measures. We discuss these findings in light of the intended use of organisational safety climate surveys.     

基于期权的商业银行总体经济资本测度研究

本文基于期权定价方法和在险价值原理,提出了两种新的商业银行总体经济资本测度方法,并给出了相关参数的估计方法和计算步骤。与自下而上的经济资本测度相比,我们的测度方法不仅能够反映宏观经济和市场环境变化的影响,且能体现出银行的内部经营和风险管理能力,也能反映银行管理者的风险偏好。同时,它还与在险价值资本测度的目标一致,揭示了经济资本的期权本质。估计方法比较简单,所需数据容易获得,估算结果为商业银行风险管理和资本结构决策提供了参考。     

Supplier Selection Behavior Under Uncertainty: Contextual and Cognitive Effects on Risk Perception and Choice

Buyers often make supplier selection decisions under conditions of uncertainty. Although the analytical aspects of supplier selection are well developed, the psychological aspects are less so. This article uses supply chain management and behavioral decision theories to propose that attributes of the purchasing situation (category difficulty, category importance, and contingent pay) affect cognition that, in turn, affects a supply manager's choice. We conducted a supplier selection behavioral experiment with practicing managers to test the model's hypotheses. When the context involves an important or difficult sourcing category, higher risk perceptions exist that increase preference for a supplier with more certain outcomes, even when that choice has a lower expected payoff. However, the presence of contingent pay decreases risk perceptions through higher perceived supplier control. We also find that a manager's risk propensity increases preferences for a supplier with less certain outcomes regardless of perceived risk. Our model and results provide a theoretical framework for further study into the cognitive aspects of supplier selection behavior and provide insight into biases that influence practicing supply chain managers.     

Implementation With Contingent Contracts

We study dominant strategy incentive compatibility in a mechanism design setting with contingent contracts where the payoff of each agent is observed by the principal and can be contracted upon. Our main focus is on the class of linear contracts (one of the most commonly used contingent contracts) which consist of a transfer and a flat rate of profit sharing. We characterize outcomes implementable by linear contracts and provide a foundation for them by showing that, in finite type spaces, every social choice function that can be implemented using a more general nonlinear contingent contract can also be implemented using a linear contract. We then qualitatively describe the set of implementable outcomes. We show that a general class of social welfare criteria can be implemented. This class contains social choice functions (such as the Rawlsian) which cannot be implemented using (uncontingent) transfers. Under additional conditions, we show that only social choice functions in this class are implementable.     

基于目标的风险度量方法

VaR和CVaR在国内外风险管理实践中得到了普遍应用,但监管者以概率置信水平作为其监管目标的方法对于实际投资者的风险度量而言并不是很直观,投资者更加关心的是资产目标价值能否实现的风险.将资产的目标价值以直观的方式加入到风险的定义中,提出了广义一致风险测度公理假设,并证明了广义一致风险测度也具有很好的性质.此外,看跌期权作为测度风险的有效方法,具有直观的经济含义,可以证明它满足广义一致风险测度公理假设.最后建立了看跌期权费风险测度和E-VaR/E-CVaR之间的数量关系.     

基于不公平厌恶或地位追求的创新顾客最优合同类型研究

本文首先从创新顾客的公平偏好程度、薪酬合同类型（最优激励系数）与激励机制效果的关系入手,引入公平偏好理论（不公平厌恶和地位追求）、相对绩效评估（RPE）和参与成本要素,构建顾客参与企业新产品开发的激励模型;其次,通过模型求解和分析,探讨分析各类薪酬合同中风险成本、不公平预期损失和激励效果之间的关系;最后,通过仿真实验验证并确定企业采用的创新顾客最优薪酬合同类型。研究发现：在同一薪酬合同下,创新顾客愈不在意其参与成本的投入,该合同的激励效果愈积极;关于创新顾客最优合同类型的选择,意味着求解一个三方面权衡,包括风险成本、不公平预期损失和公平偏好的激励效果;对于公平偏好程度较低的创新顾客,企业可采用相对绩效合同,例如锦标竞争合同;对于公平偏好程度较高的创新顾客,当其风险规避程度较高时,可采用团队报酬合同,当其风险规避程度较低时,可采用完美团队合同。     

“Birds of a Feather” Fail Together: Exploring the Nature of Dependency in SME Defaults

This article studies the effects of incorporating the interdependence among London small business defaults into a risk analysis framework using the data just before the financial crisis. We propose an extension from standard scoring models to take into account the spatial dimensions and the demographic characteristics of small and medium‐sized enterprises (SMEs), such as legal form, industry sector, and number of employees. We estimate spatial probit models using different distance matrices based only on the spatial location or on an interaction between spatial locations and demographic characteristics. We find that the interdependence or contagion component defined on spatial and demographic characteristics is significant and that it improves the ability to predict defaults of non–start‐ups in London. Furthermore, including contagion effects among SMEs alters the parameter estimates of risk determinants. The approach can be extended to other risk analysis applications where spatial risk may incorporate correlation based on other aspects.