基于一致性度量的概率模糊语言多属性群决策方法

人们通常习惯用语言术语来表达他们的偏好，因此概率型语言术语集（Probabilistic Linguistic Term Set，PLTS）在决策过程中有着十分重要的作用。目前PLTS的研究刚刚起步，有关PLTS的相关研究没有关注到一致性度量的问题，对于PLTS的多属性群决策方法有待进一步研究。首先，给出了一种新的PLTS的集结方法，并且在已有的PLTS可能度公式的基础上，构建了PLTS的相似度量方法，在此基础上，进一步提出了基于PLTS一致性度量的多属性群决策方法。该方法在各决策者权重未知的情况下，考虑到各决策者之间的一致性。首先，定义PLTS的一致性度量公式，确定决策者权重；并根据PLTS的集结方法，集结各决策者的评价信息；最后，利用可能度公式对PLTS进行排序。通过案例分析验证了该方法的可行性和优越性。     

Climate Change Risk Perception and Communication: Addressing a Critical Moment?

Climate change is an increasingly salient issue for societies and policy‐makers worldwide. It now raises fundamental interdisciplinary issues of risk and uncertainty analysis and communication. The growing scientific consensus over the anthropogenic causes of climate change appears to sit at odds with the increasing use of risk discourses in policy: for example, to aid in climate adaptation decision making. All of this points to a need for a fundamental revision of our conceptualization of what it is to do climate risk communication. This Special Collection comprises seven papers stimulated by a workshop on “Climate Risk Perceptions and Communication” held at Cumberland Lodge Windsor in 2010. Topics addressed include climate uncertainties, images and the media, communication and public engagement, uncertainty transfer in climate communication, the role of emotions, localization of hazard impacts, and longitudinal analyses of climate perceptions. Climate change risk perceptions and communication work is critical for future climate policy and decisions.     

A Multicriteria Decision Analysis Model and Risk Assessment Framework for Carbon Capture and Storage

Multicriteria decision analysis (MCDA) has been applied to various energy problems to incorporate a variety of qualitative and quantitative criteria, usually spanning environmental, social, engineering, and economic fields. MCDA and associated methods such as life‐cycle assessments and cost‐benefit analysis can also include risk analysis to address uncertainties in criteria estimates. One technology now being assessed to help mitigate climate change is carbon capture and storage (CCS). CCS is a new process that captures CO₂ emissions from fossil‐fueled power plants and injects them into geological reservoirs for storage. It presents a unique challenge to decisionmakers (DMs) due to its technical complexity, range of environmental, social, and economic impacts, variety of stakeholders, and long time spans. The authors have developed a risk assessment model using a MCDA approach for CCS decisions such as selecting between CO₂ storage locations and choosing among different mitigation actions for reducing risks. The model includes uncertainty measures for several factors, utility curve representations of all variables, Monte Carlo simulation, and sensitivity analysis. This article uses a CCS scenario example to demonstrate the development and application of the model based on data derived from published articles and publicly available sources. The model allows high‐level DMs to better understand project risks and the tradeoffs inherent in modern, complex energy decisions.     

Hypotheses for an Interpretative Model of Organizational Decision-making at Strategic Level

Abstract. Many problems that decision makers have to solve concern strategic environments. In such situations, the structural uncertainty they must face is overcome by using their representations of reality: people take decisions on the basis of their cognitive maps. In collective high level decision-making, conflicts can rise among different frames and definitions of the problem. The process through which the actors can reach a joint solution has a bargaining nature. The decision-makers' cognitive maps contain, besides the personal view of the problem to be solved, the perception of the position and bargaining power of the other actors. Strategic moves (commitments, threats…) can modify the partners' perceptions of the possibilities of success of their policy. As such, the decision-makers' cognitive maps finally become “complementary”, permitting an agreement.     

Optimality conditions for a bilevel matroid problem

In bilevel programming there are two decision makers, the leader and the follower, who act in a hierarchy. In this paper we deal with a weighted matroid problem where each of the decision makers has a different set of weights. The independent set of the matroid that is chosen by the follower determines the payoff to both the leader and the follower according to their different weights. The leader can increase his payoff by changing the weights of the follower, thus influencing the follower’s decision, but he has to pay a penalty for this. We want to find an optimum strategy for the leader. This is a bilevel programming problem with continuous variables in the upper level and a parametric weighted matroid problem in the lower level. We analyze the structure of the lower level problem. We use this structure to develop local optimality criteria for the bilevel problem that can be verified in polynomial time.     

Decision Making Using Rating Systems: When Scale Meets Binary

This article investigates how different decisions can be reached when decision makers consult a binary rating system and a scale rating system. Since typically decision makers use rating information to make binary decisions, it is particularly important to compare the scale system to the binary system. We show that the only N‐point scale system that reports a rater's opinion consistently with the binary system is one where N is odd and is not divisible by 4. At the aggregate level, however, we illustrate that inconsistencies persist regardless of the choice of N. In addition, we provide simple tools that can determine whether the systems lead decision makers to the same decision outcomes.     

Modeling Insurer‐Homeowner Interactions in Managing Natural Disaster Risk

The current system for managing natural disaster risk in the United States is problematic for both homeowners and insurers. Homeowners are often uninsured or underinsured against natural disaster losses, and typically do not invest in retrofits that can reduce losses. Insurers often do not want to insure against these losses, which are some of their biggest exposures and can cause an undesirably high chance of insolvency. There is a need to design an improved system that acknowledges the different perspectives of the stakeholders. In this article, we introduce a new modeling framework to help understand and manage the insurer's role in catastrophe risk management. The framework includes a new game‐theoretic optimization model of insurer decisions that interacts with a utility‐based homeowner decision model and is integrated with a regional catastrophe loss estimation model. Reinsurer and government roles are represented as bounds on the insurer‐insured interactions. We demonstrate the model for a full‐scale case study for hurricane risk to residential buildings in eastern North Carolina; present the results from the perspectives of all stakeholders—primary insurers, homeowners (insured and uninsured), and reinsurers; and examine the effect of key parameters on the results.     

基于后悔理论的混合型多属性案例决策方法

本文针对决策者的后悔规避行为对案例决策的影响,提出了一种基于后悔理论的混合型多属性案例决策方法。首先,针对案例属性的相似度测算,将案例属性的范围拓展到定性数据、清晰数、区间数、语言变量和区间直觉模糊数五种类型;然后,在案例原有效用基础上考虑决策者的后悔-欣喜值来测算各案例的感知效用;进一步,通过案例相似度对感知效用加权求和得到备选方案的综合感知效用,并依据综合感知效用大小对备选方案进行排序;最后通过一个实例,验证了上述方法的可行性和有效性。     

Dangerous News: Media Decision Making about Climate Change Risk

This article explores the role of broadcast news media decision makers in shaping public understanding and debate of climate change risks. It locates the media within a "tangled web" of communication and debate between sources, media, and publics. The article draws on new qualitative research in the British context. The main body of it focuses on media source strategies, on climate change storytelling in news, and the "myth of detachment" sustained by many news decision makers. The empirical evidence, gathered between 1997 and 2004, is derived primarily from recordings and notes drawn from a series of seminars that has brought together equal numbers of BBC news and television decision makers and environment/development specialists. The seminars have created a rare space for extended dialogue between media and specialist perspectives on the communication of complex climate change science and policy. While the article acknowledges the distinctive nature of the BBC as a public sector broadcaster, the evidence confirms and extends current understanding of the career of climate change within the media more broadly. The working group discussions have explored issues arising out of how stories are sourced and, in the context of competitive and time-pressured newsrooms, shaped and presented in short news pieces. Particularly significant is the disjuncture between ways of talking about uncertainty within science and policy discourse and media constructions of objectivity, truth, and balance. The article concludes with a summary of developments in media culture, technology, and practice that are creating opportunities for enhanced public understanding and debate of climate change risks. It also indicates the need for science and policy communities to be more active critics and sources of news.     

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.     

A Framework to Guide Thinking and Analysis Regarding Climate Change Policies

The potential impacts from climate change, and climate change policies, are massive. Careful thinking about what we want climate change policies to achieve is a crucial first step for analysts to help governments make wise policy choices to address these concerns. This article presents an adaptive framework to help guide comparative analysis of climate change policies. The framework recognizes the inability to forecast long-term impacts (due in part to path dependance) as a constraint on the use of standard policy analysis, and stresses learning over time as a fundamental concern. The framework focuses on the objectives relevant for climate change policy in North America over the near term (e.g., the next 20 years). For planning and evaluating current climate policy alternatives, a combination of fundamental objectives for the near term and proxy objectives for characterizing the state of the climate problem and the ability to address it at the end of that term is suggested. Broad uses of the framework are discussed, along with some concrete examples. The framework is intended to provide a basis for policy analysis that explicitly considers the benefits of learning over time to improve climate change policies.     

A Probabilistic Analysis of Surface Water Flood Risk in London

Flooding in urban areas during heavy rainfall, often characterized by short duration and high‐intensity events, is known as “surface water flooding.” Analyzing surface water flood risk is complex as it requires understanding of biophysical and human factors, such as the localized scale and nature of heavy precipitation events, characteristics of the urban area affected (including detailed topography and drainage networks), and the spatial distribution of economic and social vulnerability. Climate change is recognized as having the potential to enhance the intensity and frequency of heavy rainfall events. This study develops a methodology to link high spatial resolution probabilistic projections of hourly precipitation with detailed surface water flood depth maps and characterization of urban vulnerability to estimate surface water flood risk. It incorporates probabilistic information on the range of uncertainties in future precipitation in a changing climate. The method is applied to a case study of Greater London and highlights that both the frequency and spatial extent of surface water flood events are set to increase under future climate change. The expected annual damage from surface water flooding is estimated to be to be £171 million, £343 million, and £390 million/year under the baseline, 2030 high, and 2050 high climate change scenarios, respectively.     

The Quality of Stakeholder-Based Decisions

The increased use of stakeholder processes in environmental decision making has raised concerns about the quality of decisions these processes produce. Some claim that stakeholders make inadequate use of scientific information and analysis and are all too ready to sacrifice technical quality for political expediency. This article looks to the case study record to examine the quality of the decisions from stakeholder-based processes. The data for the analysis come from a "case survey," in which researchers coded information from 239 published case studies of stakeholder involvement in environmental decision making. These cases reflect a diversity of planning, management, and implementation activities carried out by environmental and natural resource agencies at many levels of government. Overall, the case-study record suggests that there should be little concern that stakeholder processes are resulting in low-quality decisions. The majority of cases contain evidence of stakeholders improving decisions over the status quo; adding new information, ideas, and analysis; and having adequate access to technical and scientific resources. Indeed, data suggest that it is the more intensive stakeholder processes--precisely those that have aroused recent concern--that are more likely to result in higher-quality decisions.     

Using Expert Judgments to Explore Robust Alternatives for Forest Management under Climate Change

We develop and apply a judgment‐based approach to selecting robust alternatives, which are defined here as reasonably likely to achieve objectives, over a range of uncertainties. The intent is to develop an approach that is more practical in terms of data and analysis requirements than current approaches, informed by the literature and experience with probability elicitation and judgmental forecasting. The context involves decisions about managing forest lands that have been severely affected by mountain pine beetles in British Columbia, a pest infestation that is climate‐exacerbated. A forest management decision was developed as the basis for the context, objectives, and alternatives for land management actions, to frame and condition the judgments. A wide range of climate forecasts, taken to represent the 10–90% levels on cumulative distributions for future climate, were developed to condition judgments. An elicitation instrument was developed, tested, and revised to serve as the basis for eliciting probabilistic three‐point distributions regarding the performance of selected alternatives, over a set of relevant objectives, in the short and long term. The elicitations were conducted in a workshop comprising 14 regional forest management specialists. We employed the concept of stochastic dominance to help identify robust alternatives. We used extensive sensitivity analysis to explore the patterns in the judgments, and also considered the preferred alternatives for each individual expert. The results show that two alternatives that are more flexible than the current policies are judged more likely to perform better than the current alternatives on average in terms of stochastic dominance. The results suggest judgmental approaches to robust decision making deserve greater attention and testing.     

The Influences of User Characteristics on Performance in a Logistics DSS Application

Decision Support Systems (DSS) are widely used in logistics decision applications, and a large number and variety of systems are commercially available. We investigate the contributions of user characteristics including experiences, data preferences, intuition, and effort to decision performance in a logistics DSS context. The study includes a laboratory experiment in which decision makers with varied experiences used a DSS to make facility network design decisions for problems of varying complexity. Two variants of the DSS are utilized in order to examine the interactions of a DSS decision aid with user characteristics. We find that intuition and effort are associated with decision-making performance. High analytic ability is not related to intuition, however. Education and previous experience are associated with performance. Yet these characteristics are also unrelated to intuition. Decision makers who highly value disaggregated data provided by the DSS tend to perform poorly. Also, the results suggest that the effects of users' experiences and preferences on performance are influenced by an analytical decision aid.     

“I feel and think,therefore I am”: An Affect-Cognitive Theory of management decisions

I propose an Affect-Cognitive Theory to comprehensively understand how decisions occur in organizations. To this aim, I first review the assumptions of sensemaking and decision-making streams of research, especially the influence of bounded rationality, affective states and their relationships with cognition; then, I integrate them on the common basis of socially situated cognition. This new theory emphasizes the role of affective states in determining/being determined by cognition and its errors, pointing out decision makers’ affect as the result of multi-level adaptations to the physical and social environment. Management decisions are path dependent but not immutable; they, indeed, bank on the predominant feeling resulting from the modifying interactions and regulations of decision makers with their physical and social environment. Here, decision makers are proposed as “emotional cognizers” overcoming the thinking-feeling dichotomy that has often featured in the study of management decisions. This theory is beneficial for behavioral strategy, offering the needed assumptions to intertwine human cognition, emotions, and social behavior.     

Addressing Climate Change as an Emerging Risk to Infrastructure Systems

The consequences that climate change could have on infrastructure systems are potentially severe but highly uncertain. This should make risk analysis a natural framework for climate adaptation in infrastructure systems. However, many aspects of climate change, such as weak background knowledge and societal controversy, make it an emerging risk where traditional approaches for risk assessment and management cannot be confidently employed. A number of research developments aimed at addressing these issues have emerged in recent years, such as the development of probabilistic climate projections, climate services, and robust decision frameworks. However, additional research is needed to improve the suitability of these methods for infrastructure planning. In this perspective, we outline some of the challenges in addressing climate change risks to infrastructure and summarize new developments aimed at meeting these challenges. We end by highlighting needs for future research, many of which could be well‐served by expertise within the risk analysis community.     

Screening alternatives graphically by an extended case-based distance approach

Screening is a helpful process of multiple criteria decision aid (MCDA) to reduce a larger set of alternatives into a smaller one containing the best alternatives; thereby, decision makers are able to concentrate on evaluating alternatives within a smaller set. Therefore, determining how to assist decision makers in screening is an important issue for MCDA. This study proposes an extended case-based distance approach incorporating the advantages of a case-based distance method, a mixed-integer programming approach of discriminant analysis, and a multidimensional scaling technique to help decision makers screen alternatives visually in MCDA. The proposed approach can screen alternatives by evaluating sets of cases selected by decision makers, providing visual aids to observe decision context, reducing the number of misclassifications, and improving multiple solution problems. An interactive screening procedure is also developed to provide flexibility so that decision makers can check and adjust screening results iteratively.     

Belief in Public Efficacy, Trust, and Attitudes Toward Modern Genetic Science

Government and policymakers want to engage the public in a dialogue about the conduct and consequences of science and increasingly seek to actively involve citizens in decision-making processes. Implicit in this thinking is that greater transparency and public inclusion will help dispel fears associated with new scientific advancements, foster greater public trust in those accountable, and ultimately increase the acceptability of new technologies. Less understood, however, are public perceptions about such high-level involvement in science and how these map onto public trust and attitudes within a diverse population. This article uses the concept of public efficacy -- the extent to which people believe that the public might be able to affect the course of decision making -- to explore differences in trust, attentiveness, and attitudes toward modern genetic science. Using nationally representative data from the 2003 British Social Attitudes Survey, we begin by examining the characteristics of those who have a positive belief about public involvement in this area of scientific inquiry. We then focus on how this belief maps on to indicators of public trust in key stakeholder groups, including the government and genetic scientists. Finally, we consider the relationship between public efficacy and trust and attitudes toward different applications of genetic technology. Our findings run contrary to assumptions that public involvement in science will foster greater trust and lead to a climate of greater acceptance for genetic technology. A belief in public efficacy does not uniformly equate with more trusting attitudes toward stakeholders but is associated with less trust in government rules. Whereas trust is positively correlated with more permissive attitudes about technologies such as cloning and gene therapy, people who believe in high-level public involvement are less likely to think that these technologies should be allowed than those who do not.     

Risk Communication,Public Engagement,and Climate Change: A Role for Emotions

This article discusses the potential role that emotions might play in enticing a lifestyle that diminishes climate change. Climate change is an important challenge for society. There is a growing consensus that climate change is due to our behavior, but few people are willing to significantly adapt their lifestyle. Empirical studies show that people lack a sense of urgency: they experience climate change as a problem that affects people in distant places and in a far future. Several scholars have claimed that emotions might be a necessary tool in communication about climate change. This article sketches a theoretical framework that supports this hypothesis, drawing on insights from the ethics of risk and the philosophy of emotions. It has been shown by various scholars that emotions are important determinants in risk perception. However, emotions are generally considered to be irrational states and are hence excluded from communication and political decision making about risky technologies and climate change, or they are used instrumentally to create support for a position. However, the literature on the ethics of risk shows that the dominant, technocratic approach to risk misses the normative‐ethical dimension that is inherent to decisions about acceptable risk. Emotion research shows that emotions are necessary for practical and moral decision making. These insights can be applied to communication about climate change. Emotions are necessary for understanding the moral impact of the risks of climate change, and they also paradigmatically provide for motivation. Emotions might be the missing link in effective communication about climate change.