The Role of Decision Analysis in Risk Analysis: A Retrospective

In commemorating the 40th anniversary of Risk Analysis, this article takes a retrospective look at some of the ways in which decision analysis (as a “sibling field”) has contributed to the development both of the journal, and of risk analysis as a field. I begin with some early foundational papers from the first decade of the journal's history. I then review a number of papers that have applied decision analysis to risk problems over the years, including applications of related methods such as influence diagrams, multicriteria decision analysis, and risk matrices. The article then reviews some recent trends, from roughly the last five years, and concludes with observations about the parallel evolution of risk analysis and decision analysis over the decades—especially with regard to the importance of representing multiple stakeholder perspectives, and the importance of behavioral realism in decision models. Overall, the extensive literature surveyed here supports the view that the incorporation of decision-analytic perspectives has improved the practice of risk analysis.     

Temporal Variability and Ignorance in Monte Carlo Contaminant Bioaccumulation Models: A Case Study with Selenium in Mytilus edulis

Although the parameters for contaminant bioaccumulation models most likely vary over time, lack of data makes it impossible to quantify this variability. As a consequence, Monte Carlo models of contaminant bioaccumulation often treat all parameters as having fixed true values that are unknown. This can lead to biased distributions of predicted contaminant concentrations. This article demonstrates this phenomenon with a case study of selenium accumulation in the mussel Mytilus edulis in San Francisco Bay. "Ignorance-only" simulations (in which phytoplankton and bioavailable selenium concentrations are constant over time, but sampled from distributions of field measurements taken at different times), which an analyst might be forced to use due to lack of data, were compared with "variability and ignorance" simulations (sampling phytoplankton and bioavailable selenium concentrations each month). It was found that ignorance-only simulations may underestimate or overestimate the median predicted contaminant concentration at any time, relative to variability and ignorance simulations. However, over a long enough time period (such as the complete seasonal cycle in a seasonal model), treating temporal variability as if it were ignorance at least gave a range of predicted concentrations that enclosed the range predicted by explicit treatment of temporal variability. Comparing the temporal variability in field data with that predicted by simulations may indicate whether the right amount of temporal variability is being included in input variables. Sensitivity analysis combined with biological knowledge suggests which parameters might make important contributions to temporal variability. Temporal variability is potentially more complicated to deal with than other types of stochastic variability, because of the range of time scales over which parameters may vary.     

Underestimating our influence over others at work

Employees at all organizational levels have influence over their subordinates, their colleagues, and even their bosses. But are they aware of this influence? We present evidence suggesting that employees are constrained by cognitive biases that lead them to underestimate their influence over others in the workplace. As a result of this underestimation of influence, employees may be reluctant to spearhead organizational change, discount their own role in subordinates’ performance failures, and fail to speak up in the face of wrongdoing. In addition to reviewing evidence for this bias, we propose five moderators that, when present, may reverse or attenuate the underestimation effect (namely, comparative judgments, the objectification or dehumanization of an influence target, the actual degree of influence any one influencer has, the means of influence, and culture). Finally, we offer some practical solutions to help employees more fully recognize their influence over other members of the organization.     

服务补救方式对消费者情绪和行为意向的影响

本文采用情景模拟方法,以饭店服务失误和补救为例,分别测评了主动补救和被动补救对消费者情绪和行为意向的影响,结果发现,服务补救方式对消费者情绪和行为意向的影响存在显著差别,其中主动补救对消费者正面情绪、口碑传播和重购意向的影响显著高于被动补救,而对消费者负面情绪的影响显著低于被动补救;获得服务补救消费者的正面情绪与口碑传播和重购意向呈显著正相关,而消费者负面情绪与口碑传播和重购意向呈显著负相关。     

THE IMPACT OF INCOME SHOCKS ON HEALTH: EVIDENCE FROM COHORT DATA

We study the effect of permanent income innovations on health for a prime‐aged population. Using information on more than half a million individuals sampled over a 25‐year period in three different cross‐sectional surveys we aggregate data by date‐of‐birth cohort to construct a “synthetic cohort” data set with details of income, expenditure, socio‐demographic factors, health outcomes, and selected risk factors. We then exploit structural and arguably exogenous changes in cohort incomes over the 1980s and 1990s to uncover causal effects of permanent income shocks on health. We find that such income innovations have little effect on a wide range of health measures, but do lead to increases in mortality and risky health behaviour. (JEL: I10, D31)     

基于应用扩展和网络论坛的领先用户识别方法研究

回顾了领先用户识别的电话网络法,分析了它的应用局限性。电话网络法只适合领先用户相对分散的情况下,企业需要找到部分领先用户为企业新产品概念开发贡献产品创意。但当领先用户的应用扩展到产品推广之后,为了使领先用户在市场中的领导地位能影响普通用户,并进一步带来更好的新产品绩效,需要找到目标群体中的全部领先用户。网络论坛的设立使得领先用户在产品开发论坛中相对集中,从而问卷法识别领先用户的效率能够得到保证。确立了识别领先用户的三大指标:市场或技术趋势、客户收益、信息复杂性。用统计学与概率论的方法建立了领先用户的识别模型。     

序列投资下的石油勘探投资最优时机选择

综合考虑石油勘探投资的不可逆性、序列性和不确定性，结合市场需求和勘探储量的不确定性刻画勘探储量转让价格的随机动态变化，运用实物期权思想下的序列投资决策方法探究了石油勘探项目的最优投资时机选择问题，得出各阶段最优投资时机临界值的解析表达式，并与一次性完成投资的决策结果进行对比，进而剖析了不确定性参数对各阶段最优投资时机的影响。结果表明，序列投资决策模型能够弥补一次性投资决策模型容易错失投资机会的局限性；各阶段最优投资时机临界值同市场需求波动率、勘探主体价格控制能力呈同向变动，同市场需求漂移率呈反向变动；随勘探进程的不断深入，最优勘探投资时机临界值对以上参数的敏感性程度不断增强。     

Influence triggers: A framework for understanding follower compliance

This article proposes a framework for understanding follower compliance. The framework draws from the leadership, influence, and motivation literatures to identify follower-based influence triggers and the intervening variables that lead to follower compliance. The article discusses the proposed relationships and provides some directions for future research.     

Facial appearance and leader choice in different contexts: Evidence for task contingent selection based on implicit and learned face-behaviour/face-ability associations

Facial appearance plays a role in leader selection and some facial traits are more valued in certain contexts. Here, I examined associations between facial appearance and perceptions of leadership. In Study 1, male faces were rated for several traits and leadership ability under general, war-time, and peace-time scenarios. Masculinity was found to be favoured in war-time over peace-time, however, this association was diminished when controlling for dominance. In Study 2, cues to physical ability or cooperative personality were associated with different face traits. When subsequently asked to select the best leader for a physically competitive task, participants chose faces with the trait associated with physical ability. For a cooperative task, participants chose faces with the trait associated with cooperation. These data suggest that leaders may be chosen based on their visual characteristics because certain characteristics suggest that they possess abilities that make them well suited to lead in particular situations.     

一类N人广告竞争微分对策模型

通过建立N人广告投入动态博弈模型,借助于HJB方程,得到了企业广告投入的显式闭环纳什均衡解,并据此分析了相关因素对企业最优策略及其值函数的影响。研究结果表明,除了广告的边际收益、成本等常见参数之外,企业对参与者未来发展潜力的重视程度、对策时域以及产品之间的替代程度等也对最优广告投入有重要影响。     

Improved Approximation Algorithms for Maximum Graph Partitioning Problems

We consider the design of approximation algorithms for a number of maximum graph partitioning problems, among others MAX-k-CUT, MAX-k-DENSE-SUBGRAPH, and MAX-k-DIRECTED-UNCUT. We present a new version of the semidefnite relaxation scheme along with a better analysis, extending work of Halperin and Zwick (2002). This leads to an improvement over known approximation factors for such problems. The key to the improvement is the following new technique: It was already observed by Han et al. (2002) that a parameter-driven choice of the random hyperplane can lead to better approximation factors than obtained by Goemans and Williamson (1995). But it remained difficult to find a “good” set of parameters. In this paper, we analyze random hyperplanes depending on several new parameters. We prove that a sub-optimal choice of these parameters can be obtained by the solution of a linear program which leads to the desired improvement of the approximation factors. In this fashion a more systematic analysis of the semidefinite relaxation scheme is obtained.     

CSR as Value Attunement within Governance Processes: Stakeholder Dialogue,Corporate Principles and Regulation

I argue that a governance perspective on corporate social responsibility (CSR) makes it possible to explain why the concept will always be under‐defined, is normative and thus political by nature, and is and should be difficult to measure. The perspective also makes it possible to understand the interaction between corporate values and stakeholders values. In processes of dialogue within governance systems and governance structures, changing insights into the principles of CSR can lead to regulation or its adjustment. Power is important in these dialogues. Principles are at least partly shaped within governance systems and governance structures, and they influence the outcomes of corporate policies. Changes within the regulatory framework could also lead to changes in the principles of CSR. Value attunement processes could lead to regulation, which again influences the governance structures and thus the power of stakeholders within the dialogue. The theoretical model provided helps to analyze why CSR is different in companies, cultures and academic traditions.     

Small Theories and Large Risks—Is Risk Analysis Relevant for Epistemology?

Ought we to take seriously large risks predicted by “exotic” or improbable theories? We routinely assess risks on the basis or either common sense, or some developed theoretical framework based on the best available scientific explanations. Recently, there has been a substantial increase of interest in the low‐probability “failure modes” of well‐established theories, which can involve global catastrophic risks. However, here I wish to discuss a partially antithetical situation: alternative, low‐probability (“small”) scientific theories predicting catastrophic outcomes with large probability. I argue that there is an important methodological issue (determining what counts as the best available explanation in cases where the theories involved describe possibilities of extremely destructive global catastrophes), which has been neglected thus far. There is no simple answer to the correct method for dealing with high‐probability high‐stakes risks following from low‐probability theories that still cannot be rejected outright, and much further work is required in this area. I further argue that cases like these are more numerous than usually assumed, for reasons including cognitive biases, sociological issues in science and the media image of science. If that is indeed so, it might lead to a greater weight of these cases in areas such as moral deliberation and policy making.     

Combined demand and lead time uncertainty with back-ordering in a multi-level product structure environment

The performance of two lot-sizing algorithms incorporating back-orders is evaluated and compared with several traditional lot-sizing rules. This comparison is undertaken for a multi-level product structure environment operating under a rolling horizon in the presence of both demand and lead time uncertainties. By means of simulation and statistical analysis of the simulation output, the impact on system performance of a variety of demand and lead time scenarios, product structures, cost parameters and lot-sizing, is evaluated. The conclusions drawn emphasize important differences in the influence of the factors on system performance given the presence or absence of uncertainty.     

The optimal operation planning of a Kanban system with variable lead times

In the majority of the previous research on Kanban systems delivery lead time is treated as a fixed value. In many practical situations, however, the lead time is variable. In this paper, optimal operation planning of a fixed interval withdrawal Kanban with variable lead times is proposed. A cost is incurred for inventory level less than the safety inventory level, since the risk of shortage of inventory must be considered. Behaviour of the optimal number of Kanbans and the withdrawal interval of Kanbans are investigated in terms of various parameters such as standard deviation and mean lead time.     

The influence of peer honesty and anonymity on managerial reporting

In certain situations, managers are able to observe their peers’ honesty in the budget setting process. This disclosed peer honesty may subsequently influence the individual managers’ honesty. Therefore, this study examines how different types of peer honesty disclosure (no, anonymous, and non-anonymous) influence managerial reporting in a budgeting setting. Non-anonymous disclosure refers to situations where both the honesty of a budget report and the reporter’s identity is disclosed. In the case of anonymous disclosure, only the budget report’s honesty can be observed. Based on different behavioral theories, I predict that honesty decreases over time when peer honesty is disclosed both anonymously and non-anonymously. Moreover, I investigate the research question whether non-anonymous disclosure affects honesty differently than anonymous disclosure does. The results of an experimental investigation show that the disclosure of peer honesty, as predicted, decreases honesty over time for both anonymous and non-anonymous disclosure. With regard to the research question, I find no difference between anonymous and non-anonymous disclosure. The results underline the necessity for management accountants to determine the ability of employees to infer the honesty of their peers. In addition, the implementation of budget-based incentive systems should be reconsidered when peer honesty is disclosed.     

Strengthening Safety Compliance in Nuclear Power Operations: A Role‐Based Approach

Safety compliance is of paramount importance in guaranteeing the safe running of nuclear power plants. However, it depends mostly on procedures that do not always involve the safest outcomes. This article introduces an empirical model based on the organizational role theory to analyze the influence of legitimate sources of expectations (procedures formalization and leadership) on workers’ compliance behaviors. The sample was composed of 495 employees from two Spanish nuclear power plants. Structural equation analysis showed that, in spite of some problematic effects of proceduralization (such as role conflict and role ambiguity), procedure formalization along with an empowering leadership style lead to safety compliance by clarifying a worker's role in safety. Implications of these findings for safety research are outlined, as well as their practical implications.     

SELECTIVE ATTENTION AND LEARNING

What do we notice and how does this affect what we learn and come to believe? I present a model of an agent who learns to make forecasts on the basis of readily available information, but is selective as to which information he attends to: he chooses whether to attend as a function of current beliefs about whether such information is predictive. If the agent does not attend to some piece of information, it cannot be recalled at a later date. He uses Bayes' rule to update his beliefs given attended‐to information, but does not attempt to fill in missing information. The model demonstrates how selective attention may lead the agent to persistently fail to recognize important empirical regularities, make systematically biased forecasts, and hold incorrect beliefs about the statistical relationship between variables. In addition, it identifies factors that make such errors more likely or persistent. The model is applied to shed light on stereotyping and discrimination, persistent learning failures and disagreement, and the process of discovery.     

Demand and Production Management with Uniform Guaranteed Lead Time

Recently, innovation‐oriented firms have been competing along dimensions other than price, lead time being one such dimension. Increasingly, customers are favoring lead time guarantees as a means to hedge supply chain risks. For a make‐to‐order environment, we explicitly model the impact of a lead time guarantee on customer demands and production planning. We study how a firm can integrate demand and production decisions to optimize expected profits by quoting a uniform guaranteed maximum lead time to all customers. Our analysis highlights the increasing importance of lead time for customers, as well as the tradeoffs in achieving a proper balance between revenue and cost drivers associated with lead‐time guarantees. We show that the optimal lead time has a closed‐form solution with a newsvendor‐like structure. We prove comparative statics results for the change in optimal lead time with changes in capacity and cost parameters and illustrate the insights using numerical experimentation.     

What Do We Learn from Emissions Reporting? Analytical Considerations and Comparison of Pollutant Release and Transfer Registers in the United States,Canada, England,and Australia

Pollutant release and transfer registers (PRTRs) are becoming a popular measure for addressing industrial pollution in many countries. PRTRs require reporting of emissions from specific industrial sectors and making the information publicly available. This article suggests a framework for comparing PRTRs in order to determine whether they attain their declared goals and which factors, if any, influence their effectiveness. The challenges to such a comparison can be divided into three groups. The first refers to changes that are directly linked to the characteristics of PRTRs: both the changes within a specific system over time and variations among different systems. The second refers to parameters that affect the outcomes of the systems without being directly a part of them. The third involves the relations between the emissions reported to the PRTRs and the associated environmental risk. We suggest an approach that relies on relative comparison, commensurate with the unique characteristics of each PRTR, that compares their actual outcomes. Such an approach is necessary both due to significant variations among current PRTRs as well as for following the unique policy objectives that are manifested in different PRTRs. Application of this comparative approach in the United States, England, Canada, and Australia demonstrates significant differences in PRTR systems across countries and suggests that the mere presence of a PRTR may not lead to reduced industrial emissions. The analysis also demonstrates that emission reductions do not correlate with reductions in risk-related measures. The article proposes several simple modifications to the composition of current PRTR databases that may facilitate more accurate analysis of results and effective oversight of implementation.