不同风险偏好的过度自信代理人薪酬合同设计

本文将过度自信心理特征纳入到经典委托代理框架,从代理人过度自信程度、代理人的风险偏好、努力-产出不确定性、努力可观测性、保留效用几个角度,构建了多个不同的委托代理情景,分别分析了委托人提供不同形式合同、代理人付出不同努力水平时他们的期望效用,从而考察在各种情景下委托人应怎样为不同类型代理人设计薪酬合同,激励代理人付出努力,以实现委托人和代理人决策组合的帕雷托最优.结果表明,对不同过度自信程度及风险偏好的代理人,委托人提供的合同形式大不相同.这不仅从行为的角度拓展了经典委托代理框架,而且为委托人设计薪酬合同提供了非常有益的参考.     

投资者情绪如何影响股票定价?——基于IPO公司的实证研究

分析了投资者情绪如何影响股票定价,并以2006年—2011年间917家IPO公司为样本进行实证检验.结果发现:1)市场情绪和个股具体的情绪均显著影响IPO溢价.具体而言,市场情绪较高组比较低组的IPO溢价高36％(63％vs 27％),个股具体情绪较高组比较低组的IPO溢价高24％(56％ vs 32％);2)公司价值不确定性越高,市场情绪对IPO溢价的影响越大,公司投机风险越高,市场情绪对IPO溢价的影响越小;3)IPO溢价较高的公司,其股价在上市后会逐渐反转.     

基于延期交割费的我国燃料油期现货价格关系辨析

本文基于延期交割费的描述性统计分析,发现国内燃料油期现货市场间存在延期交割费。利用经验模态分解(EMD)方法,本文将延期交割费的影响因素分为短期市场波动、中期经济自然环境影响及长期趋势,分别对应EMD结果中的高频、低频和趋势分量。在此基础上,本文通过ARMA-GARCH模型度量燃料油市场价格风险,检验了延期交割费与燃料油市场价格风险之间的关系。结果显示延期交割费一定程度体现了市场价格风险,反映出上海燃料油期货市场的有效性。     

基于多目标加权灰靶决策模型的节能服务公司选择研究

用能方对节能服务公司（Energy Service Company,ESCO）的选择关系到合同能源管理（Energy Performance Contracting,EPC）能否顺利实施。从用能方的角度,采用灰色系统理论中的多目标加权灰靶决策模型,对存在多决策目标的ESCO选择问题进行研究。通过层次分析法确定ESCO的11个决策目标的决策权数,根据综合效果测度值的比较,最终实现最优对策的选择。本文为用能方的ESCO选择问题提供了一种新的思路。     

The Need to Reconcile Concepts that Characterize Systems Facing Threats

Desirable system performance in the face of threats has been characterized by various management concepts. Through semistructured interviews with editors of journals in the fields of emergency response and systems management, a literature review, and professional judgment, we identified nine related and often interchangeably used system performance concepts: adaptability, agility, reliability, resilience, resistance, robustness, safety, security, and sustainability. A better understanding of these concepts will allow system planners to pursue management strategies best suited to their unique system dynamics and specific objectives of good performance. We analyze expert responses and review the linguistic definitions and mathematical framing of these concepts to understand their applications. We find a lack of consensus on their usage between interview subjects, but by using the mathematical framing to enrich the linguistic definitions, we formulate comparative visualizations and propose distinct definitions for the nine concepts. We present a conceptual framing to relate the concepts for management purposes.     

Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction

Damage models for natural hazards are used for decision making on reducing and transferring risk. The damage estimates from these models depend on many variables and their complex sometimes nonlinear relationships with the damage. In recent years, data‐driven modeling techniques have been used to capture those relationships. The available data to build such models are often limited. Therefore, in practice it is usually necessary to transfer models to a different context. In this article, we show that this implies the samples used to build the model are often not fully representative for the situation where they need to be applied on, which leads to a “sample selection bias.” In this article, we enhance data‐driven damage models by applying methods, not previously applied to damage modeling, to correct for this bias before the machine learning (ML) models are trained. We demonstrate this with case studies on flooding in Europe, and typhoon wind damage in the Philippines. Two sample selection bias correction methods from the ML literature are applied and one of these methods is also adjusted to our problem. These three methods are combined with stochastic generation of synthetic damage data. We demonstrate that for both case studies, the sample selection bias correction techniques reduce model errors, especially for the mean bias error this reduction can be larger than 30%. The novel combination with stochastic data generation seems to enhance these techniques. This shows that sample selection bias correction methods are beneficial for damage model transfer.     

Stages of Supply Chain Disruption Response: Direct,Constraining, and Mediating Factors for Impact Mitigation

It is well established that supply chain disruptions can have a severe negative impact on firms and general wisdom suggests that this impact can be mitigated by quick responses. Aside from a few anecdotes, however, little is known about the decision‐making process that leads to speedy responses and about its impeding and supporting antecedents. Using the organizational information‐processing perspective, this empirical study unravels the disruption management process along a sequence of four stages—disruption recognition, disruption diagnosis, response development, and response implementation—and hypothesizes constraining and mediating effects of these stages. The findings contribute to an improved understanding of the role that the decision stages play in mitigating supply chain disruptions, and confirm the prediction that the speed with which information is processed and the stages are worked through positively affects supply chain performance. In addition, the findings suggest that one of the stages, diagnosis, acts as a constraining factor to the other stages. The stages also play a mediating role between the impact that the disruption has and a firm's readiness (prior to a disruption), dependence on a key supplier, and supply chain complexity. This provides guidance to decision makers in the application of resources both prior to a negative event and during a disruption recovery.     

Using In Vitro High‐Throughput Screening Data for Predicting Benzo[k]Fluoranthene Human Health Hazards

Today there are more than 80,000 chemicals in commerce and the environment. The potential human health risks are unknown for the vast majority of these chemicals as they lack human health risk assessments, toxicity reference values, and risk screening values. We aim to use computational toxicology and quantitative high‐throughput screening (qHTS) technologies to fill these data gaps, and begin to prioritize these chemicals for additional assessment. In this pilot, we demonstrate how we were able to identify that benzo[k]fluoranthene may induce DNA damage and steatosis using qHTS data and two separate adverse outcome pathways (AOPs). We also demonstrate how bootstrap natural spline‐based meta‐regression can be used to integrate data across multiple assay replicates to generate a concentration–response curve. We used this analysis to calculate an in vitro point of departure of 0.751 μM and risk‐specific in vitro concentrations of 0.29 μM and 0.28 μM for 1:1,000 and 1:10,000 risk, respectively, for DNA damage. Based on the available evidence, and considering that only a single HSD17B4 assay is available, we have low overall confidence in the steatosis hazard identification. This case study suggests that coupling qHTS assays with AOPs and ontologies will facilitate hazard identification. Combining this with quantitative evidence integration methods, such as bootstrap meta‐regression, may allow risk assessors to identify points of departure and risk‐specific internal/in vitro concentrations. These results are sufficient to prioritize the chemicals; however, in the longer term we will need to estimate external doses for risk screening purposes, such as through margin of exposure methods.     

Does Iconicity in Pictographs Matter? The Influence of Iconicity and Numeracy on Information Processing,Decision Making,and Liking in an Eye‐Tracking Study

Researchers recommend the use of pictographs in medical risk communication to improve people's risk comprehension and decision making. However, it is not yet clear whether the iconicity used in pictographs to convey risk information influences individuals’ information processing and comprehension. In an eye‐tracking experiment with participants from the general population (N = 188), we examined whether specific types of pictograph icons influence the processing strategy viewers use to extract numerical information. In addition, we examined the effect of iconicity and numeracy on probability estimation, recall, and icon liking. This experiment used a 2 (iconicity: blocks vs. restroom icons) × 2 (scenario: medical vs. nonmedical) between‐subject design. Numeracy had a significant effect on information processing strategy, but we found no effect of iconicity or scenario. Results indicated that both icon types enabled high and low numerates to use their default way of processing and extracting the gist of the message from the pictorial risk communication format: high numerates counted icons, whereas low numerates used large‐area processing. There was no effect of iconicity in the probability estimation. However, people who saw restroom icons had a higher probability of correctly recalling the exact risk level. Iconicity had no effect on icon liking. Although the effects are small, our findings suggest that person‐like restroom icons in pictographs seem to have some advantages for risk communication. Specifically, in nonpersonalized prevention brochures, person‐like restroom icons may maintain reader motivation for processing the risk information.     

Perception of Climate Risk among Rural Farmers in Vietnam: Consistency within Households and with the Empirical Record

Rural farmers in Vietnamese communes perceive climate risk and potential impacts on livelihood within a complex context that may influence individual and household decisions. In a primary survey of 1,145 residents of the Thach Ha district of Ha Tinh province, we gathered data regarding perception about stability in climate, potential risks to livelihood, demographic characteristics, orientation toward risk, and interest in expanding economic activity. Temporal analysis of meteorological and economic indicator data forms an empirical basis for comparison with human perception. We ask the basic question: Are rural farmers’ perceptions of climate consistent with the historical record and reproducible within households? We find that respondents do perceive climate anomalies, with some anchoring on recent extreme events as revealed by climate observational data, and further that spouses disproportionately share perceptions relative to randomly simulated pairings. To put climate‐related risk perception in a larger context, we examine patterns across a range of risks to livelihood faced by farmers (livestock disease, pests, markets, health), using dimension reduction techniques. We find that our respondents distinguish among potential causes of low economic productivity, with substantial emphasis on climate‐related impacts. They do not express uniform concern across risks, but rather average patterns reveal common modes and distinguish climate concern. Still, among those expressing concern about climate‐related risks to livelihood we do not find an association with expressed intention to pursue changes in economic activity as a risk management response.