On Funtowicz and Ravetz's “Decision Stake—System Uncertainties” Structure and Recently Developed Risk Perspectives

The Funtowicz and Ravetz model for classifying problem‐solving strategies into applied sciences, professional consultancy, and postnormal sciences is well known in the social science risk literature. The model is illustrated in a diagram based on the two axes: (i) decision stakes—the value dimension (costs, benefits) and (ii) the system uncertainties—the knowledge dimension. These axes resemble the same two dimensions that characterize some recently developed risk perspectives: (a) consequences and the severity of these consequences and (b) associated uncertainties. In this article, we make a detailed comparison of these two types of risk frameworks. We point to similarities and differences in motivation and use. A main conclusion of the article is that these risk perspectives all provide adequate scientific bases for the Funtowicz and Ravetz model. New insights are provided on the understanding of what the outcome stakes/consequences and uncertainty dimensions really capture in these perspectives and frameworks.     

Promoting Seismic Retrofit Implementation Through “Nudge”: Using Warranty as a Driver

This article proposes a new type of warranty policy that applies the “nudge” concept developed by Thaler and Sunstein to encourage homeowners in Japan to implement seismic retrofitting. Homeowner adaptation to natural disasters through loss reduction measures is known to be inadequate. To encourage proactive risk management, the “nudge” approach capitalizes on how choice architecture can influence human decision‐making tendencies. For example, people tend to place more value on a warranty for consumer goods than on actuarial value. This article proposes a “warranty for seismic retrofitting” as a “nudge” policy that gives homeowners the incentive to adopt loss reduction measures. Under such a contract, the government guarantees all repair costs in the event of earthquake damage to the house if the homeowner implements seismic retrofitting. To estimate the degree to which a warranty will increase the perceived value of seismic retrofitting, we use field survey data from 1,200 homeowners. Our results show that a warranty increases the perceived value of seismic retrofitting by an average of 33%, and an approximate cost‐benefit analysis indicates that such a warranty can be more economically efficient than an ex ante subsidy. Furthermore, we address the failure of the standard expected utility model to explain homeowners’ decisions based on warranty evaluation, and explore the significant influence of ambiguity aversion on the efficacy of seismic retrofitting and nonanalytical factors such as feelings or trust.     

The Use of Simulation to Reduce the Domain of “Black Swans” with Application to Hurricane Impacts to Power Systems

Recently, the concept of black swans has gained increased attention in the fields of risk assessment and risk management. Different types of black swans have been suggested, distinguishing between unknown unknowns (nothing in the past can convincingly point to its occurrence), unknown knowns (known to some, but not to relevant analysts), or known knowns where the probability of occurrence is judged as negligible. Traditional risk assessments have been questioned, as their standard probabilistic methods may not be capable of predicting or even identifying these rare and extreme events, thus creating a source of possible black swans. In this article, we show how a simulation model can be used to identify previously unknown potentially extreme events that if not identified and treated could occur as black swans. We show that by manipulating a verified and validated model used to predict the impacts of hazards on a system of interest, we can identify hazard conditions not previously experienced that could lead to impacts much larger than any previous level of impact. This makes these potential black swan events known and allows risk managers to more fully consider them. We demonstrate this method using a model developed to evaluate the effect of hurricanes on energy systems in the United States; we identify hurricanes with potentially extreme impacts, storms well beyond what the historic record suggests is possible in terms of impacts.     

“Chemophobia” Today: Consumers’ Knowledge and Perceptions of Chemicals

This mixed‐methods study investigated consumers’ knowledge of chemicals in terms of basic principles of toxicology and then related this knowledge, in addition to other factors, to their fear of chemical substances (i.e., chemophobia). Both qualitative interviews and a large‐scale online survey were conducted in the German‐speaking part of Switzerland. A Mokken scale was developed to measure laypeople's toxicological knowledge. The results indicate that most laypeople are unaware of the similarities between natural and synthetic chemicals in terms of certain toxicological principles. Furthermore, their associations with the term “chemical substances” and the self‐reported affect prompted by these associations are mostly negative. The results also suggest that knowledge of basic principles of toxicology, self‐reported affect evoked by the term “chemical substances,” risk‐benefit perceptions concerning synthetic chemicals, and trust in regulation processes are all negatively associated with chemophobia, while general health concerns are positively related to chemophobia. Thus, to enhance informed consumer decisionmaking, it might be necessary to tackle the stigmatization of the term “chemical substances” as well as address and clarify prevalent misconceptions.     

MANUFACTURING STRATEGY ON THE “S” CURVE

The concept and techniques of “manufacturing strategy” offer managers the opportunity to use their production function as a strategic weapon in competition, an apparently attractive objective. Yet after about 25 years, the use of manufacturing in corporate strategy (MCS) as a management practice is not widespread. In contrast, however, in academic literature it appears to be flourishing and rapidly growing in popularity. This paper seeks to answer this apparent paradox, beginning with the history of MCS as it was developed as a theory of design to enable a manufacturing system to be focused on a key competitive task. Common criticisms of MCS, such as “tradeoffs,” “focus” and “undynamic,” are examined and refuted as valid reasons for its only modest usage. Instead, three “new” problems in the MCS concept and its techniques are suggested as genuine needs for the completion of the theory and for its becoming more universally understood and used by industrial managers.     

THE TIMING OF LABOR TRANSFERS IN DUAL RESOURCE-CONSTRAINED SYSTEMS: “PUSH” VS. “PULL” RULES*

Altough the dual resource-constrained (DRC) system has been studied, the decision rule used to determine when workers are eligible for transfer largely has been ignored. Some earlier studies examined the impact of this rule [5] [12] [15] but did not include labor-transfer times in their models. Gunther [6] incorporated labour-transfer times into his model, but the model involved only one worker and two machines. No previous study has examined decision rules that initiate labor transfers based on labor needs (“pull” rules). Labor transfers always have been initiated based on lack of need (“push” rules). This study examines three “pull” variations of the “When” labor-assignment decision rule. It compares their performances to the performances of two “push” rules and a comparable machine-limited system. A nonparametric statistical test, Jonckheere's S statistic, is used to test for significance of the rankings of the rules: a robust parametric multiple-comparison statistical test, Tukey's B statistic, is used to test the differences. One “pull” and one “push” decision rule provide similar performances and top the rankings consistently. Decision rules for determining when labor should be transferred from one work area to another are valuable aids for managers. This especially is true for the ever-increasing number of managers operating in organizations that recognize the benefits of a cross-trained work force. Recently there has been much interest in cross-training workers, perhaps because one of the mechanisms used in just-in-time systems to handle unbalanced work loads is to have cross-trained workers who can be shifted as demand dictates [8]. If management is to take full advantage of a cross-trained work force, it need to know when to transfer workers.     

The Global Food Industry and “Creative Capitalism”: The Partners in Food Solutions Sustainable Business Model

Rising global food prices have driven 44 million additional people into extreme poverty—and malnutrition—in developing countries since June 2010. Partners in Food Solutions (PFS), a nonprofit social enterprise affiliated with General Mills, is proposed as the conduit for food industry managers, engineers, and scientists to initially advise small‐ and medium‐sized African mills and food processors—and later other developing countries—on improving supply chain management by addressing manufacturing problems, developing products, improving packaging, extending product shelf, and finding new product markets. In this article, the “creative capitalism” model of sustainability and social and environmental responsibility is applied to the food manufacturing industry's efforts supporting PFS. Furthermore, the evolution of the sustainable business model developed by PFS is thoroughly described, explained, and analyzed as a generic model of social enterprise to be “scaled up” by the global food manufacturing industry. A summary of salient points conclude the article.     

“What—Me Worry?”1“Why So Serious?”:2 A Personal View on the Fukushima Nuclear Reactor Accidents

Infrequently, it seems that a significant accident precursor or, worse, an actual accident, involving a commercial nuclear power reactor occurs to remind us of the need to reexamine the safety of this important electrical power technology from a risk perspective. Twenty‐five years since the major core damage accident at Chernobyl in the Ukraine, the Fukushima reactor complex in Japan experienced multiple core damages as a result of an earthquake‐induced tsunami beyond either the earthquake or tsunami design basis for the site. Although the tsunami itself killed tens of thousands of people and left the area devastated and virtually uninhabitable, much concern still arose from the potential radioactive releases from the damaged reactors, even though there was little population left in the area to be affected. As a lifelong probabilistic safety analyst in nuclear engineering, even I must admit to a recurrence of the doubt regarding nuclear power safety after Fukushima that I had experienced after Three Mile Island and Chernobyl. This article is my attempt to “recover” my personal perspective on acceptable risk by examining both the domestic and worldwide history of commercial nuclear power plant accidents and attempting to quantify the risk in terms of the frequency of core damage that one might glean from a review of operational history.     

Reconsidering the Effect of Market Experience on the “Endowment Effect”

Simple exchange experiments have revealed that participants trade their endowment less frequently than standard demand theory would predict. List (2003a) found that the most experienced dealers acting in a well functioning market are not subject to this exchange asymmetry, suggesting that a significant amount of market experience is required to overcome it. To understand this market‐experience effect, we introduce a distinction between two types of uncertainty—choice uncertainty and trade uncertainty—both of which could lead to exchange asymmetry. We conjecture that trade uncertainty is most important for exchange asymmetry. To test this conjecture, we design an experiment where the two treatments impact differently on trade uncertainty, while controlling for choice uncertainty. Supporting our conjecture, we find that “forcing” subjects to give away their endowment in a series of exchanges eliminates exchange asymmetry in a subsequent test. We discuss why markets might not provide sufficient incentives for learning to overcome exchange asymmetry.     

Insight into “Calculated Risk”: An Application to the Prioritization of Emerging Infectious Diseases for Blood Transfusion Safety

Increasing identification of transmissions of emerging infectious diseases (EIDs) by blood transfusion raised the question which of these EIDs poses the highest risk to blood safety. For a number of the EIDs that are perceived to be a threat to blood safety, evidence on actual disease or transmission characteristics is lacking, which might render measures against such EIDs disputable. On the other hand, the fact that we call them “emerging” implies almost by definition that we are uncertain about at least some of their characteristics. So what is the relative importance of various disease and transmission characteristics, and how are these influenced by the degree of uncertainty associated with their actual values? We identified the likelihood of transmission by blood transfusion, the presence of an asymptomatic phase of infection, prevalence of infection, and the disease impact as the main characteristics of the perceived risk of disease transmission by blood transfusion. A group of experts in the field of infectious diseases and blood transfusion ranked sets of (hypothetical) diseases with varying degrees of uncertainty associated with their disease characteristics, and used probabilistic inversion to obtain probability distributions for the weight of each of these risk characteristics. These distribution weights can be used to rank both existing and newly emerging infectious diseases with (partially) known characteristics. Analyses show that in case there is a lack of data concerning disease characteristics, it is the uncertainty concerning the asymptomatic phase and the disease impact that are the most important drivers of the perceived risk. On the other hand, if disease characteristics are well established, it is the prevalence of infection and the transmissibility of the disease by blood transfusion that will drive the perceived risk. The risk prioritization model derived provides an easy to obtain and rational expert assessment of the relative importance of an (emerging) infectious disease, requiring only a limited amount of information. Such a model might be used to justify a rational and proportional response to an emerging infectious disease, especially in situations where little or no specific information is available.     

Context,Cultural Bias,and Health Risk Perception: The “Everyday” Nature of Pesticide Policy Preferences in London,Calgary, and Halifax

Risk perception and the cultural theory of risk have often been contrasted in relation to risk‐related policy making; however, the local context in which risks are experienced, an important component of everyday decision making, remains understudied. What is unclear is the extent to which localized community beliefs and behaviors depend on larger belief systems about risk (i.e., worldviews). This article reports on a study designed to understand the relative importance of health risk perceptions (threat of harm); risk‐related worldviews (cultural biases); and the experiences of local context (situated risk) for predicting risk‐related policy preferences regarding cosmetic pesticides. Responses to a random telephone questionnaire are used to compare residents’ risk perceptions, cultural biases, and pesticide bylaw preferences in Calgary (Alberta), Halifax (Nova Scotia), and London (Ontario), Canada. Logistic regression shows that the most important determinants of pesticide bylaw preference are risk perception, lack of benefit, and pesticide “abstinence.” Though perception of health risk is the best single predictor of differences in bylaw preferences, social factors such as gender and situated risk factors like conflict over chemical pesticides, are also important. Though cultural biases are not important predictors of pesticide bylaw preference, as in other studies, they are significant predictors of health risk perception. Pesticide bylaw preference is therefore more than just a health risk perception or worldview issue; it is also about how health risk becomes situated—contextually—in the experiences of residents’ everyday lives.     

PRODUCTION AND OPERATIONS MANAGEMENT'S NEW “REQUISITE VARIETY”

This essay is based on my plenary address at the second annual meeting of the Production and Operations Management Society on November 11, 1991. I propose that as the competitive environment in which production and operations management (POM) practitioners operate becomes ever more demanding and the problems about which POM academics study and teach become more complex and interrelated, we need new approaches both in our teaching and our research. I describe five ways of expanding our “requisite variety” of capabilities.     

Risk as Analysis and Risk as Feelings: Some Thoughts about Affect, Reason, Risk, and Rationality

Modern theories in cognitive psychology and neuroscience indicate that there are two fundamental ways in which human beings comprehend risk. The “analytic system” uses algorithms and normative rules, such as probability calculus, formal logic, and risk assessment. It is relatively slow, effortful, and requires conscious control. The “experiential system” is intuitive, fast, mostly automatic, and not very accessible to conscious awareness. The experiential system enabled human beings to survive during their long period of evolution and remains today the most natural and most common way to respond to risk. It relies on images and associations, linked by experience to emotion and affect (a feeling that something is good or bad). This system represents risk as a feeling that tells us whether it is safe to walk down this dark street or drink this strange‐smelling water. Proponents of formal risk analysis tend to view affective responses to risk as irrational. Current wisdom disputes this view. The rational and the experiential systems operate in parallel and each seems to depend on the other for guidance. Studies have demonstrated that analytic reasoning cannot be effective unless it is guided by emotion and affect. Rational decision making requires proper integration of both modes of thought. Both systems have their advantages, biases, and limitations. Now that we are beginning to understand the complex interplay between emotion and reason that is essential to rational behavior, the challenge before us is to think creatively about what this means for managing risk. On the one hand, how do we apply reason to temper the strong emotions engendered by some risk events? On the other hand, how do we infuse needed “doses of feeling” into circumstances where lack of experience may otherwise leave us too “coldly rational”? This article addresses these important questions.     

Confidence deficits and reducibility: Toward a coherent conceptualization of uncertainty level

Outside of the field of risk analysis, an important theoretical conversation on the slippery concept of uncertainty has unfolded over the last 40 years within the adjacent field of environmental risk. This literature has become increasingly standardized behind the tripartite distinction between uncertainty location, the nature of uncertainty, and uncertainty level, popularized by the “W&H framework.” This article introduces risk theorists and practitioners to the conceptual literature on uncertainty with the goal of catalyzing further development and clarification of the uncertainty concept within the field of risk analysis. It presents two critiques of the W&H framework's dimension of uncertainty level—the dimension that attempts to define the characteristics separating greater uncertainties from lesser uncertainties. First, I argue the framework's conceptualization of uncertainty level lacks a clear and consistent epistemological position and fails to acknowledge or reconcile the tensions between Bayesian and frequentist perspectives present within the framework. This article reinterprets the dimension of uncertainty level from a Bayesian perspective, which understands uncertainty as a mental phenomenon arising from “confidence deficits” as opposed to the ill-defined notion of “knowledge deficits” present in the framework. And second, I elaborate the undertheorized concept of uncertainty “reducibility.” These critiques inform a clarified conceptualization of uncertainty level that can be integrated with risk analysis concepts and usefully applied by modelers and decisionmakers engaged in model-based decision support.     

TOWARD A “NEW ARCHITECTURE” FOR POM

The field of POM is steadily expanding its scope. This allows us to pursue interesting new fields of inquiry and makes us ever more relevant to the needs of practitioners. But it also increases the risk that over time our efforts will become fragmented and unfocused, causing us to slowly lose our sense of community and break up into separate camps. In this paper I ask, “What kind of new organizing framework, or ‘architecture,’ will allow us to maintain our sense of community as our interests diverge?” I describe the experience of one academic organization that faced a similar problem several years ago and how the situation was resolved. This experience leads us to propose a possible solution to Pom's emerging problem: engage with real operations managers and focus our efforts on helping them deal with the actual problems they are facing in today's complex and fast‐changing world.     

On Different Types of Uncertainties in the Context of the Precautionary Principle

Few policies for risk management have created more controversy than the precautionary principle. A main problem is the extreme number of different definitions and interpretations. Almost all definitions of the precautionary principle identify “scientific uncertainties” as the trigger or criterion for its invocation; however, the meaning of this concept is not clear. For applying the precautionary principle it is not sufficient that the threats or hazards are uncertain. A stronger requirement is needed. This article provides an in‐depth analysis of this issue. We question how the scientific uncertainties are linked to the interpretation of the probability concept, expected values, the results from probabilistic risk assessments, the common distinction between aleatory uncertainties and epistemic uncertainties, and the problem of establishing an accurate prediction model (cause‐effect relationship). A new classification structure is suggested to define what scientific uncertainties mean.     

Improving Default Risk Prediction Using Bayesian Model Uncertainty Techniques

Credit risk is the potential exposure of a creditor to an obligor's failure or refusal to repay the debt in principal or interest. The potential of exposure is measured in terms of probability of default. Many models have been developed to estimate credit risk, with rating agencies dating back to the 19th century. They provide their assessment of probability of default and transition probabilities of various firms in their annual reports. Regulatory capital requirements for credit risk outlined by the Basel Committee on Banking Supervision have made it essential for banks and financial institutions to develop sophisticated models in an attempt to measure credit risk with higher accuracy. The Bayesian framework proposed in this article uses the techniques developed in physical sciences and engineering for dealing with model uncertainty and expert accuracy to obtain improved estimates of credit risk and associated uncertainties. The approach uses estimates from one or more rating agencies and incorporates their historical accuracy (past performance data) in estimating future default risk and transition probabilities. Several examples demonstrate that the proposed methodology can assess default probability with accuracy exceeding the estimations of all the individual models. Moreover, the methodology accounts for potentially significant departures from “nominal predictions” due to “upsetting events” such as the 2008 global banking crisis.     

“Not in (or Under) My Backyard”: Geographic Proximity and Public Acceptance of Carbon Capture and Storage Facilities

Carbon capture and storage (CCS) is an innovative technical approach to mitigate the problem of climate change by capturing carbon dioxide emissions and injecting them underground for permanent geological storage. CCS has been perceived both positively, as an innovative approach to facilitate a more environmentally benign use of fossil fuels while also generating local economic benefits, and negatively, as a technology that prolongs the use of carbon‐intensive energy sources and burdens local communities with prohibitive costs and ecological and human health risks. This article extends existing research on the “not in my backyard” (NIMBY) phenomenon in a direction that explores the public acceptance of CCS. We utilize survey data collected from 1,001 residents of the coal‐intensive U.S. state of Indiana. Over 80% of respondents express support for the general use of CCS technology. However, 20% of these initial supporters exhibit a NIMBY‐like reaction and switch to opposition as a CCS facility is proposed close to their communities. Respondents’ worldviews, their beliefs about the local economic benefits that CCS will generate, and their concerns about its safety have the greatest impact on increasing or decreasing the acceptance of nearby facilities. These results lend valuable insights into the perceived risks associated with CCS technology and the possibilities for its public acceptance at both a national and local scale. They may be extended further to provide initial insights into likely public reactions to other technologies that share a similar underground dimension, such as hydraulic fracturing.     

Mass Customization and Guardrails: “You Can Not Be All Things to All People”

A mass customization strategy enables a firm to match its product designs to unique consumer tastes. In a classic horizontal product‐differentiation framework, a consumer's utility is a decreasing function of the distance between their ideal taste and the taste defined by the most closely aligned product the firm offers. A consumer thus considers the taste mismatch associated with their purchased product, but otherwise the positioning of the firm's product portfolio (or, “brand image”) is immaterial. In contrast, self‐congruency theory suggests that consumers assess how well both the purchased product and its overall brand image match with their ideal taste. Therefore, we incorporate within the consumer utility function both product‐specific and brand‐level components. Mass customization has the potential to improve taste alignment with regard to a specific purchased product, but at the risk of increasing brand dilution. Absent brand dilution concerns, a firm will optimally serve all consumers’ ideal tastes at a single price. In contrast, by endogenizing dilution costs within the consumer utility model, we prove that a mass‐customizing firm optimally uses differential pricing. Moreover, we show that the firm offers reduced prices to consumers with extreme tastes (to stimulate consumer “travel”), with a higher and fixed price being offered to those consumers having more central (mainstream) tastes. Given that a continuous spectrum of prices will likely not be practical in application, we also consider the more pragmatic approach of augmenting the uniformly priced mass customization range with preset (non‐customized) outlying designs, which serve customers at the taste extremes. We prove this practical approach performs close to optimal.     

The Event: An Underexamined Risk Concept

Some of the terms used in risk assessment and management are poorly and even contradictorily defined. One such term is “event,” which arguably describes the most basic of all risk‐related concepts. The author cites two contemporary textbook interpretations of “event” that he contends are incorrect and misleading. He then examines the concept of an event in A. N. Kolmogorov's probability axioms and in several more‐current textbooks. Those concepts are found to be too narrow for risk assessments and inconsistent with the actual usage of “event” by risk analysts. The author goes on to define and advocate linguistic definitions of events (as opposed to mathematical definitions)—definitions constructed from natural language. He argues that they should be recognized for what they are: the de facto primary method of defining events.