Managing Wildfire Events: Risk‐Based Decision Making Among a Group of Federal Fire Managers

Managing wildfire events to achieve multiple management objectives involves a high degree of decision complexity and uncertainty, increasing the likelihood that decisions will be informed by experience‐based heuristics triggered by available cues at the time of the decision. The research reported here tests the prevalence of three risk‐based biases among 206 individuals in the USDA Forest Service with authority to choose how to manage a wildfire event (i.e., line officers and incident command personnel). The results indicate that the subjects exhibited loss aversion, choosing the safe option more often when the consequences of the choice were framed as potential gains, but this tendency was less pronounced among those with risk seeking attitudes. The subjects also exhibited discounting, choosing to minimize short‐term over long‐term risk due to a belief that future risk could be controlled, but this tendency was less pronounced among those with more experience. Finally, the subjects, in particular those with more experience, demonstrated a status quo bias, choosing suppression more often when their reported status quo was suppression. The results of this study point to a need to carefully construct the decision process to ensure that the uncertainty and conflicting objectives inherent in wildfire management do not result in the overuse of common heuristics. Individual attitudes toward risk or an agency culture of risk aversion may counterbalance such heuristics, whereas increased experience may lead to overconfident intuitive judgments and a failure to incorporate new and relevant information into the decision.     

零售商长期后悔规避的供应链网络动态均衡

本文研究了基于时变随机需求的供应链网络动态均衡。由于需求的不确定性和季节性,网络中的零售商会考虑缺货后悔和存货后悔的负效用,基于长期后悔规避效用做出最优决策。利用变分不等式,刻画了网络中制造商层、考虑长期后悔规避效用的零售商层和需求市场层的均衡决策行为。通过数值算例,基于三种长期后悔情形分析了零售商长期后悔规避行为对其均衡订购量、利润、后悔值的影响。研究结果表明,零售商的长期后悔规避程度在不断增加、保持不变,不断减少这三种情况下,对其均衡订购量、利润、后悔值的影响不同。研究结论将指导供应链网络中企业,基于长期后悔规避制定最优决策。     

Improving Risk Management: From Lame Excuses to Principled Practice

The three classic pillars of risk analysis are risk assessment (how big is the risk and how sure can we be?), risk management (what shall we do about it?), and risk communication (what shall we say about it, to whom, when, and how?). We propose two complements as important parts of these three bases: risk attribution (who or what addressable conditions actually caused an accident or loss?) and learning from experience about risk reduction (what works, and how well?). Failures in complex systems usually evoke blame, often with insufficient attention to root causes of failure, including some aspects of the situation, design decisions, or social norms and culture. Focusing on blame, however, can inhibit effective learning, instead eliciting excuses to deflect attention and perceived culpability. Productive understanding of what went wrong, and how to do better, thus requires moving past recrimination and excuses. This article identifies common blame‐shifting “lame excuses” for poor risk management. These generally contribute little to effective improvements and may leave real risks and preventable causes unaddressed. We propose principles from risk and decision sciences and organizational design to improve results. These start with organizational leadership. More specifically, they include: deliberate testing and learning—especially from near‐misses and accident precursors; careful causal analysis of accidents; risk quantification; candid expression of uncertainties about costs and benefits of risk‐reduction options; optimization of tradeoffs between gathering additional information and immediate action; promotion of safety culture; and mindful allocation of people, responsibilities, and resources to reduce risks. We propose that these principles provide sound foundations for improving successful risk management.     

On the Preference to Avoid Ex Post Inventory Errors

The value of demand information underlies many supply chain strategies that aim at better matching supply and demand. This study reports on the results of a laboratory experiment designed to estimate the behavioral value of demand information. Relative to the commonly assumed benchmark of a rational risk‐neutral decision maker, we find that decision makers are consistently willing to pay too much for the option to eliminate the risk of supply not matching demand. Contrary to intuition, we show that risk aversion does not explain this result. We posit that demand information provides behavioral value because it mitigates regret from ex post inventory errors.     

Split‐Award Procurement Auctions—Can Bayesian Equilibrium Strategies Predict Human Bidding Behavior in Multi‐Object Auctions?

We analyze if and when symmetric Bayes Nash equilibrium predictions can explain human bidding behavior in multi‐object auctions. We focus on two sealed‐bid split‐award auctions with ex ante split decisions as they can be regularly found in procurement practice. These auction formats are straightforward multi‐object extensions of the first‐price sealed‐bid auction. We derive the risk‐neutral symmetric Bayes Nash equilibrium strategies and find that, although the two auction mechanisms yield the same expected costs to the buyer, other aspects of the two models, including the equilibrium bidding strategies, differ significantly. The strategic considerations in these auction formats are more involved than in single‐lot first‐price sealed‐bid auctions, and it is questionable whether expected utility maximization can explain human bidding behavior in such multi‐object auctions. Therefore, we analyzed the predictive accuracy of our equilibrium strategies in the laboratory. In human subject experiments we found underbidding, which is in line with earlier experiments on single‐lot first‐price sealed‐bid auctions. To control for regret, we organize experiments against computerized bidders, who play the equilibrium strategy. In computerized experiments where bid functions are only used in a single auction, we found significant underbidding on low‐cost draws. In experiments where the bid function is reused in 100 auctions, we could also control effectively for risk aversion, and there is no significant difference of the average bidding behavior and the risk‐neutral Bayes Nash equilibrium bid function. The results suggest that strategic complexity does not serve as an explanation for underbidding in split‐award procurement auctions, but risk aversion does have a significant impact.     

考虑消费者预期后悔的价格歧视策略研究

基于行为的价格歧视（BPD）受到越来越多的关注,企业为了更好地服务于各细分市场,需要更细致的考虑消费者事前估值的不确定性,以及因此出现的消费者预期后悔。本文在双寡头垄断市场中建立模型,探讨消费者预期后悔对企业动态价格竞争和利润的影响。研究结果表明,消费者预期后悔会对自身购买决策产生显著影响;当高值实现概率相对较低,高低值差异大且切换后悔的厌恶较小或者重复后悔的厌恶较大时,企业奖励重复购买的客户,否则奖励切换者;预期后悔对企业的利润既可以有正面的影响也可以有负面的影响。     

Alpha as Ambiguity: Robust Mean‐Variance Portfolio Analysis

We derive the analogue of the classic Arrow–Pratt approximation of the certainty equivalent under model uncertainty as described by the smooth model of decision making under ambiguity of Klibanoff, Marinacci, and Mukerji (2005). We study its scope by deriving a tractable mean‐variance model adjusted for ambiguity and solving the corresponding portfolio allocation problem. In the problem with a risk‐free asset, a risky asset, and an ambiguous asset, we find that portfolio rebalancing in response to higher ambiguity aversion only depends on the ambiguous asset's alpha, setting the performance of the risky asset as benchmark. In particular, a positive alpha corresponds to a long position in the ambiguous asset, a negative alpha corresponds to a short position in the ambiguous asset, and greater ambiguity aversion reduces optimal exposure to ambiguity. The analytical tractability of the enhanced Arrow–Pratt approximation renders our model especially well suited for calibration exercises aimed at exploring the consequences of model uncertainty on equilibrium asset prices.     

销售成本信息不对称下供应商风险规避的回购契约

在价格随机条件下,销售成本信息不对称且供应商规避风险时,本文探讨回购契约协调供应链的最优决策。在前提假设的基础上构建新的回购契约模型,求解并用算例进行仿真验证,考虑信息不对称与风险规避共同发生耦合作用后对供应链相关决策变量的影响。研究结果表明：在价格随机条件下,不管信息是否对称,只要供应商有风险规避意识,供应链相关决策变量均发生分岔突变;不管市场价格是否随机,也不管供应商是否风险规避,只要零售商隐瞒私人销售成本信息,就会给自己带来额外的收益,但会给供应商与整个供应链带来损害;供应链上的信息越不对称,在分岔突变区域,相关决策变量的振荡幅度越大。分岔突变现象是市场价格随机和供应商风险规避耦合作用后特有的现象;零售商能够利用信息不对称给自己带来额外的好处,但会损害供应商和供应链的利益;供应商防范零售商这种损人利己行为的最好对策,就是通过设计一种合作机制,以最低成本的方式来促使零售商将销售成本信息公开化;另外,供应商以平稳的心态(风险中性)应对外部风险,更有利于提高其自身决策的水平。     

Tiered Chemical Testing: A Value of Information Approach

In December 2000 the EPA initiated the Voluntary Children's Chemical Evaluation Program (VCCEP) by asking manufacturers to voluntarily sponsor toxicological testing in a tiered process for 23 chemicals selected for the pilot phase. The tiered nature of the VCCEP pilot program creates the need for clearly defined criteria for determining when information is sufficient to assess the potential risks to children. This raises questions about how to determine the "adequacy" of the existing information and assess the need to undertake efforts to reduce uncertainty (through further testing). This article applies a value of information analysis approach to determine adequacy by modeling how toxicological and exposure data collected through the VCCEP may be used to inform risk management decisions. The analysis demonstrates the importance of information about the exposure level and control costs in making decisions regarding further toxicological testing. This article accounts for the cost of delaying control action and identifies the optimal testing strategy for a constrained decisionmaker who, absent applicable human data, cannot regulate without bioassay data on a specific chemical. It also quantifies the differences in optimal testing strategy for three decision criteria: maximizing societal net benefits, ensuring maximum exposure control while net benefits are positive (i.e., benefits outweigh costs), and controlling to the maximum extent technologically feasible while the lifetime risk of cancer exceeds a specific level of risk. Finally, this article shows the large differences that exist in net benefits between the three criteria for the range of exposure levels where the optimal actions differ.     

基于信号传递过程中学习效应的联合创业投资决策

由于创业投资项目的产出具有较强的不确定性,单个创业投资公司受自身资源的制约很难对项目做出准确的评估,通常需要联合其他创业投资公司进行联合投资。文章从信息经济学的视角出发,通过引入创业投资家的风险规避成本、努力成本和战略收益,建立联合创业投资决策的三阶段模型,应用信号传递中的学习效应分析了创业投资中的联合投资决策过程。主导型创业投资公司通过接收跟随型创业投资公司对投资项目的评估信号,利用贝叶斯法则修正先验概率,然后再重新调整对项目的评估,通过联合投资伙伴间信号传递的学习效应寻找联合创业投资的合作区间,进而提高创业投资的成功率。最后,通过算例分析验证了理论模型的有效性。     

考虑随机需求与收入共享的风险规避型V2G备用决策模型

在CVaR风险度量准则下,构建了考虑随机需求与收入共享的风险规避型V2G备用决策模型,推导了集中和分散两种决策下渠道成员最优决策行为的解析解,并进一步比较分析了随机需求变量服从均匀分布时的均衡策略。研究发现,集中决策下的最优V2G备用预留因子与渠道整体的风险规避度正相关,而均衡时的V2G备用销售价格与渠道整体的风险规避度的相关性不确定,且受到随机需求变量的分布函数影响;分散决策下的最优V2G备用预留因子仅与电网公司的风险规避度有关,而均衡时的V2G备用销售价格受到电网公司的风险规避度、购电价格以及电动汽车用户收入共享系数等的共同影响;电动汽车用户的最优V2G备用收入共享系数与其风险规避度正相关,而与电网公司的风险规避度负相关。数值仿真结果表明,在绝大多数情形下收入共享合约并不能完美协调此类V2G备用渠道的分散决策行为。     

Confronting Deep Uncertainties in Risk Analysis

How can risk analysts help to improve policy and decision making when the correct probabilistic relation between alternative acts and their probable consequences is unknown? This practical challenge of risk management with model uncertainty arises in problems from preparing for climate change to managing emerging diseases to operating complex and hazardous facilities safely. We review constructive methods for robust and adaptive risk analysis under deep uncertainty. These methods are not yet as familiar to many risk analysts as older statistical and model‐based methods, such as the paradigm of identifying a single “best‐fitting” model and performing sensitivity analyses for its conclusions. They provide genuine breakthroughs for improving predictions and decisions when the correct model is highly uncertain. We demonstrate their potential by summarizing a variety of practical risk management applications.     

Farmers’ Risk‐Based Decision Making Under Pervasive Uncertainty: Cognitive Thresholds and Hazy Hedging

Researchers in judgment and decision making have long debunked the idea that we are economically rational optimizers. However, problematic assumptions of rationality remain common in studies of agricultural economics and climate change adaptation, especially those that involve quantitative models. Recent movement toward more complex agent‐based modeling provides an opportunity to reconsider the empirical basis for farmer decision making. Here, we reconceptualize farmer decision making from the ground up, using an in situ mental models approach to analyze weather and climate risk management. We assess how large‐scale commercial grain farmers in South Africa (n = 90) coordinate decisions about weather, climate variability, and climate change with those around other environmental, agronomic, economic, political, and personal risks that they manage every day. Contrary to common simplifying assumptions, we show that these farmers tend to satisfice rather than optimize as they face intractable and multifaceted uncertainty; they make imperfect use of limited information; they are differently averse to different risks; they make decisions on multiple time horizons; they are cautious in responding to changing conditions; and their diverse risk perceptions contribute to important differences in individual behaviors. We find that they use two important nonoptimizing strategies, which we call cognitive thresholds and hazy hedging, to make practical decisions under pervasive uncertainty. These strategies, evident in farmers' simultaneous use of conservation agriculture and livestock to manage weather risks, are the messy in situ performance of naturalistic decision‐making techniques. These results may inform continued research on such behavioral tendencies in narrower lab‐ and modeling‐based studies.     

Risk Assessment Can Be a Game‐Changing Information Technology—But Too Often It Isn't

The printing press was a game‐changing information technology. Risk assessment could be also. At present, risk assessments are commonly used as one‐time decision aids: they provide justification for a particular decision, and afterwards usually sit on a shelf. However, when viewed as information technologies, their potential uses are much broader. Risk assessments: (1) are repositories of structured information and a medium for communication; (2) embody evaluative structures for setting priorities; (3) can preserve information over time and permit asynchronous communication, thus encouraging learning and adaptation; and (4) explicitly address uncertain futures. Moreover, because of their “what‐if” capabilities, risk assessments can serve as a platform for constructive discussion among parties that hold different values. The evolution of risk assessment in the nuclear industry shows how such attributes have been used to lower core‐melt risks substantially through improved templates for maintenance and more effective coordination with regulators (although risk assessment has been less commonly used in improving emergency‐response capabilities). The end result of this evolution in the nuclear industry has been the development of “living” risk assessments that are updated more or less in real time to answer even routine operational questions. Similar but untapped opportunities abound for the use of living risk assessments to reduce risks in small operational decisions as well as large policy decisions in other areas of hazard management. They can also help improve understanding of and communication about risks, and future risk assessment and management. Realization of these opportunities will require significant changes in incentives and active promotion by the risk analytic community.     

Public Perceptions of Regulatory Costs,Their Uncertainty and Interindividual Distribution

Public perceptions of both risks and regulatory costs shape rational regulatory choices. Despite decades of risk perception studies, this article is the first on regulatory cost perceptions. A survey of 744 U.S. residents probed: (1) How knowledgeable are laypeople about regulatory costs incurred to reduce risks? (2) Do laypeople see official estimates of cost and benefit (lives saved) as accurate? (3) (How) do preferences for hypothetical regulations change when mean‐preserving spreads of uncertainty replace certain cost or benefit? and (4) (How) do preferences change when unequal interindividual distributions of hypothetical regulatory costs replace equal distributions? Respondents overestimated costs of regulatory compliance, while assuming agencies underestimate costs. Most assumed agency estimates of benefits are accurate; a third believed both cost and benefit estimates are accurate. Cost and benefit estimates presented without uncertainty were slightly preferred to those surrounded by “narrow uncertainty” (a range of costs or lives entirely within a personally‐calibrated zone without clear acceptance or rejection of tradeoffs). Certain estimates were more preferred than “wide uncertainty” (a range of agency estimates extending beyond these personal bounds, thus posing a gamble between favored and unacceptable tradeoffs), particularly for costs as opposed to benefits (but even for costs a quarter of respondents preferred wide uncertainty to certainty). Agency‐acknowledged uncertainty in general elicited mixed judgments of honesty and trustworthiness. People preferred egalitarian distributions of regulatory costs, despite skewed actual cost distributions, and preferred progressive cost distributions (the rich pay a greater than proportional share) to regressive ones. Efficient and socially responsive regulations require disclosure of much more information about regulatory costs and risks.     

Competing on Time: An Integrated Framework to Optimize Dynamic Time‐to‐Market and Production Decisions

This study develops a comprehensive framework to optimize new product introduction timing and subsequent production decisions faced by a component supplier. Prior to market entry, the supplier performs process design activities, which improve manufacturing yield and the chances of getting qualified for the customer's product. However, a long delay in market entry allows competitors to enter the market and pass the customer's qualification process before the supplier, reducing the supplier's share of the customer's business. After entering the market and if qualified, the supplier also needs to decide how much to produce for a finite planning horizon by considering several factors such as manufacturing yield and stochastic demand, both of which depend on the earlier time‐to‐market decision. To capture this dependency, we develop a sequential, nested, two‐stage decision framework to optimize the time‐to‐market and production decisions in relation to each other. We show that the supplier's optimal market entry and qualification timing decision need to be revised in real time based on the number of qualified competitors at the time of market‐entry decision. We establish the optimality of a threshold policy. Following this policy, at the beginning of each decision epoch, the supplier should optimally stop preparing for qualification and decide whether to enter the market if her order among qualified competitors exceeds a predetermined threshold. We also prove that the supplier's optimal production policy is a state‐dependent, base‐stock policy, which depends on the time‐to‐market and qualification decisions. The proposed framework also enables a firm to quantify how market conditions (such as price and competitor entry behavior) and operating conditions (such as the rate of learning and inventory/production‐related costs) affect time‐to‐market strategy and post‐entry production decisions.     

An Experimental Investigation of Pull Contracts in Supply Chains

In this study, I investigate supply chain contracts in a setting where a supplier uses its inventory to directly satisfy a retailer's demand. These “pull” contracts have increased in popularity in practice but have not been studied experimentally. In a controlled laboratory setting, I evaluate a wholesale price contract and two coordinating contracts. The data suggest that the benefit of the two coordinating contracts over the wholesale price contract is less than the standard theory predicts, and that retailers, in the two coordinating contracts, exhibit a systematic bias of setting the coordinating parameter too low, and the wholesale price too high, relative to the normative benchmarks. In an effort to explain this deviation, I explore three behavioral models and find that loss aversion and reference dependence fit the data well. I empirically test this result in a follow‐up experiment, which directly controls for loss aversion and reference dependence, and observe that retailers make significantly better decisions. Lastly, I administer a number of experiments which reduce the complexity of the problem, curtail the amount of risk, and increase the level of decision support, and find that none improve decisions relative to the treatment that controls for loss aversion and reference dependence.     

Community Resilience and Decision Theory Challenges for Catastrophic Events

Extreme and catastrophic events pose challenges for normative models of risk management decision making. They invite development of new methods and principles to complement existing normative decision and risk analysis. Because such events are rare, it is difficult to learn about them from experience. They can prompt both too little concern before the fact, and too much after. Emotionally charged and vivid outcomes promote probability neglect and distort risk perceptions. Aversion to acting on uncertain probabilities saps precautionary action; moral hazard distorts incentives to take care; imperfect learning and social adaptation (e.g., herd‐following, group‐think) complicate forecasting and coordination of individual behaviors and undermine prediction, preparation, and insurance of catastrophic events. Such difficulties raise substantial challenges for normative decision theories prescribing how catastrophe risks should be managed. This article summarizes challenges for catastrophic hazards with uncertain or unpredictable frequencies and severities, hard‐to‐envision and incompletely described decision alternatives and consequences, and individual responses that influence each other. Conceptual models and examples clarify where and why new methods are needed to complement traditional normative decision theories for individuals and groups. For example, prospective and retrospective preferences for risk management alternatives may conflict; procedures for combining individual beliefs or preferences can produce collective decisions that no one favors; and individual choices or behaviors in preparing for possible disasters may have no equilibrium. Recent ideas for building “disaster‐resilient” communities can complement traditional normative decision theories, helping to meet the practical need for better ways to manage risks of extreme and catastrophic events.     

Capturing and Integrating Knowledge for Managing Risks in Tunnel Works

Risk‐related knowledge gained from past construction projects is regarded as potentially extremely useful in risk management. This article describes a proposed approach to capture and integrate risk‐related knowledge to support decision making in construction projects. To ameliorate the problem related to the scarcity of risks information often encountered in construction projects, Bayesian Belief Networks are used and expert judgment is elicited to augment available information. Particularly, the article provides an overview of judgment‐based biases that can appear in the elicitation of judgments for constructing Bayesian Networks and the provisos that can be made in this respect to minimize these types of bias. The proposed approach is successfully applied to develop six models for top risks in tunnel works. More than 30 tunneling experts in the Netherlands and Germany were involved in the investigation to provide information on identifying relevant scenarios than can lead to failure events associated with tunneling risks. The article has provided an illustration of the applicability of the developed approach for the case of “face instability in soft soils using slurry shields.”     

Consumer Responses to Integrated Risk‐Benefit Information Associated with the Consumption of Food

The risk analysis of the health impact of foods is increasingly focused on integrated risk‐benefit assessment, which will also need to be communicated to consumers. It therefore becomes important to understand how consumers respond to integrated risk‐benefit information. Quality‐adjusted‐life‐years (QALYs) is one measure that can be used to assess the balance between risks and benefits associated with a particular food. The effectiveness of QALYs for communicating both positive and negative health effects associated with food consumption to consumers was examined, using a 3 × 2 experiment varying information about health changes in terms of QALYs associated with the consumption of fish (n = 325). The effect of this information on consumer perceptions of the usefulness of QALYs for describing health effects, on risk and benefit perceptions, attitudes, and intentions to consume fish was examined. Results demonstrated that consumers perceived QALYs as useful for communicating health effects associated with food consumption. QALYs communicated as a net effect were preferred for food products associated with negative net effects on health, while separate communication of both risks and benefits may be preferred for food products associated with positive or zero net health effects. Information about health changes in terms of QALYs facilitated informed decision making by consumers, as indicated by the impact on risk and benefit perceptions as intended by the information. The impact of this information on actual food consumption choices merits further investigation.