Risk perception of mobile communication: a mental models approach

Some lay people confronted with a new base station project fear serious health consequences from the high‐frequency radiation, while experts consider exposure under the current international standards as unproblematic. These conflictive estimations may be attributed to the different mental models of lay people and experts. Less is known about lay people’s knowledge in regard to mobile communication and their intuitive understanding of the associated health risks. An adaptation of the ‘Mental Models Approach’ was used to reveal lay people’s beliefs about mobile communication and to learn more about lay people’s information requirements, potential knowledge gaps, and misconceptions. Through the means of open interviews with Swiss experts (N = 16), lay people (N = 16), and base station opponents (N = 15), different mental models were constructed and evaluated. Comparisons between the expert and the lay groups showed several qualitative differences in all identified knowledge domains. Knowledge gaps in regard to changing exposure magnitudes due to the interaction patterns of cell phones and base stations as well as misconceptions about regulation issues and scientific processes were found in both lay groups. In addition, lack of trust in responsible actors and disaffection with base station location processes were mentioned. The reported qualitative insights may be useful for the improvement of further risk communication tools.     

Public perception of dementia risk in the UK: a mental models approach

Abstract

Dementia is a leading cause of death in the developed world. A number of modifiable risk factors for dementia have been identified, yet lay knowledge on dementia risk is limited. A mental models approach was used to compare lay and expert knowledge of risk in order to identify target areas for lay education. This method assumes experts and laypeople use different cognitive representations to make sense of phenomena. Semi-structured interviews were conducted with 8 experts and 15 laypeople to construct mental models of dementia risk. Interviews were transcribed verbatim and entered into NVivo qualitative data analysis software for coding. Findings indicated that laypeople had some knowledge of modifiable risk factors but in contrast to the expert mental model, laypeople poorly understood the causal links between these factors and dementia risk. Lay participants were unsure of the interaction between modifiable and non-modifiable risk factors and primarily attributed dementia risk to ‘bad luck’. It is suggested that future dementia education could benefit by building upon a general appreciation of healthy behaviour with particular focus on explaining the causal pathways to dementia risk. Additionally, it may be productive to inform laypeople it is possible to ‘change one’s luck’ by engaging in protective behaviours.     

Improving lay understanding of exposure to electromagnetic fields; the effect of information on perception of and responses to risk

Studies show that, although many people are concerned about the potential health risks of being exposed to electromagnetic fields (EMF), lay understanding of exposure, an important determinant of risk perceptions and responses, is limited. In an online consumer panel (n = 245), we tested the effects of providing people with information about EMF on lay understanding of exposure, and on perceptions and responses to risks, using an experimental 2?×?2?×?2 design. Providing people with specific information explaining the distance–exposure relationship, clarifying EMF policy, or specifying personal exposure management options actions resulted in a better understanding of exposure. We demonstrated that information provision as such had no effects on concerns about EMF nor on perceived risk of personal sources, i.e. mobile phones, but lowered perception of risk of public sources, i.e. mobile phone base stations and high-voltage power lines. In addition, information explaining the distance–exposure relationship in combination with policy information resulted in reduced self-reported risk-aversive responses. Moreover, participants who understood more about exposure in relation to the distance to the source showed lower perceptions of risk, were less likely to restrict their own exposure, and more likely to accept new installations of public sources of EMF in their neighborhood. In contrast, awareness that exposure was mainly determined by personal use of EMF sources corresponded with higher perceptions of risk from personal sources and a higher likelihood to restrict one’s own exposure. Our findings provide focal points for improving communication on EMF. In particular, we suggest to include information clarifying the distance–exposure relationship to improve understanding of exposure.     

Developing communications about CCS: three lessons learned

To curb the risks of climate change, the Intergovernmental Panel on Climate Change posits that global CO₂ emissions from the energy supply sector must be reduced to 90% below 2010 levels between 2040 and 2070. Electricity generation is the largest contributor to emissions from the energy supply sector. Carbon capture and storage (CCS) holds the promise of helping to reduce CO₂ emissions from coal-fired power plants, as part of a low-carbon portfolio that could also include energy efficiency, natural gas, renewables and nuclear power. To inform people’s decisions about whether or not to support the implementation of CCS, our team created brochures about 10 low-carbon technologies as well as a computer tool that helped users to develop technically realistic low-carbon portfolios. Here, we highlight three main lessons we learned in developing these communications about CCS: (1) when learning about CCS people also want to know about other alternatives; (2) using simple wording improves understanding, even about complex technologies; and (3) the time to communicate about CCS is now.     

Climate-induced migration: using mental models to explore aggregate and individual decision-making

The US Dust Bowl of the 1930s (a prolong period of drought experienced in the United States accompanied by severe sand storms) is often described as an abnormal event. However, climate change is likely to increase the frequency and impact of similar occurrences. Because of this, a growing number of scholars have begun to examine multiple facets of climate-induced migration from various disciplinary traditions. Specifically, scholars have called for continued research into individual decision-making processes. Responding to this call, we construct a mental model from historical interviews of those who migrated to California during the US Dust Bowl. Our model provides insight into the migration process with a unique focus on individual decision-making processes of migrants.     

A new method for early detection of mass concern about public health issues

During a public health crisis, risk communication professionals and other risk managers need timely and reliable information about the public reaction as soon as possible in order to effectively carry out their responsibilities. Based on a data-set of microblog news posts, this article presents a method that can forecast to what extent news about a public health issue will be disseminated. The study makes advances in rapidly tracking citizens’ reaction towards public health issues by monitoring news media on microblog sites. The findings show that the proposed method can detect early on the intensity of social reaction on a public health issue as well as provide alert signals. The method can also complement existing risk detection systems and help in the design of other powerful risk analytics tools.     

Selecting credit rating models: a cross-validation-based comparison of discriminatory power

In commercial banking, various statistical models for corporate credit rating have been theoretically promoted and applied to bank-specific credit portfolios. In this paper, we empirically compare and test the performance of a wide range of parametric and nonparametric credit rating model approaches in a statistically coherent way, based on a ‘real-world’ data set. We repetitively (k times) split a large sample of industrial firms’ default data into disjoint training and validation subsamples. For all model types, we estimate k out-of-sample discriminatory power measures, allowing us to compare the models coherently. We observe that more complex and nonparametric approaches, such as random forest, neural networks, and generalized additive models, perform best in-sample. However, comparing k out-of-sample cross-validation results, these models overfit and lose some of their predictive power. Rather than improving discriminatory power, we perceive their major contribution to be their usefulness as diagnostic tools for the selection of rating factors and the development of simpler, parametric models.

College,mental health,and a violent student: employing numerical formats to communicate risk on campus

Abstract

Non-experts, including college administrators, professors, and students are often faced with risk assessment decisions on campus. Despite this fact, there are few accepted professional standards for presenting statistical risk and far fewer recommendations for the communication of violence risk. Existing systematic reviews are both incomplete and limited in scope and more troubling are the contradictory conclusions and competing recommendations for the communication of information that is essential to decision-making. In this article, three directions in the violence risk communication literature were given attention. First, non-experts were asked to make risk estimates using information from an expert source. Second, this project moves research attention to an important domain, campus violence. Finally, multiple outcome variables were assessed in an effort to extend the scope of research beyond Bayesian reasoning. Measured outcomes included: risk judgments, linguistic features, and affective processing as put forth by the affective processing theories. When the effect of estimating or communicating risk as a probability was compared to a frequency: frequency evidence was rated as less confusing and easier to understand.     

Communicating with the public during health crises: experts' experiences and opinions

Governments and health agencies worldwide are planning for a potential influenza pandemic. Their plans acknowledge the importance of public communication during an outbreak and include related guidelines and strategies. Emerging infectious disease (EID) communication is a new addition to the literature, drawing on health promotion communication, crisis communication and environmental/technological risk communication. This paper adds to the literature, exploring the notion of ‘effective communication during health crises' by reporting on interviews with 22 public health officials, scientists and communications professionals responsible for communicating with the public. When analyzed in the context of the risk communication literature, the interviews reveal several considerations for health risk communicators. First, given the important role that mass media will play in an EID outbreak, there is an urgent need for public health to build partnerships with journalists based on an understanding of the two parties' unique societal roles. Second, seemingly practical communications considerations – such as how certain to be about information before sharing it and whether to engage in two‐way communication with the public – have ethical dimensions that deserve attention. Third, there are unique challenges associated with communicating uncertainty, which would benefit from an exploration of the role of trust in health crisis communication.     

Risk association: towards a linguistically informed framework for analysing risk in discourse

This article addresses a fundamental feature of risk discourse, namely, risk association, defined as the process whereby an agent establishes a connection between something, x, and the notion of risk. In addition, risk association can be defined as the result of such a process, i.e. an established connection between x and risk. A special case of risk association is when x is linked to harmful properties and thus is represented as a risk. Although fundamental to any analysis of socio-cognitive attention to risks, the process of risk association is often taken for granted in risk research. A layered model of risk association is presented taking linguistic practices, i.e. the use of words, as the point of departure. Accordingly, there are both central and more peripheral means of risk association. The central means include the morpheme ‘risk’. More peripheral means of risk association are close synonyms and antonyms of risk (e.g. ‘hazard’, ‘danger’, ‘safety’ and ‘security’) and other related words (e.g. ‘crisis’, ‘protection’ and ‘threat’). For an illustration, the model is applied to an empirical example: the instructions for Swedish government agencies. The example illustrates how the exact vocabulary considered for operationalization in analysis has important consequences for the conclusions that follow with respect to the extent to which government agencies are associated with risk.     

Adding and subtracting Black-Scholes: A new approach to approximating derivative prices in continuous-time models

We develop a new approach to approximating asset prices in the context of continuous-time models. For any pricing model that lacks a closed-form solution, we provide a closed-form approximate solution, which relies on the expansion of the intractable model around an “auxiliary” one. We derive an expression for the difference between the true (but unknown) price and the auxiliary one, which we approximate in closed-form, and use to create increasingly improved refinements to the initial mispricing induced by the auxiliary model. The approach is intuitive, simple to implement, and leads to fast and extremely accurate approximations. We illustrate this method in a variety of contexts including option pricing with stochastic volatility, computation of Greeks, and the term structure of interest rates.     

Helping behavior in a virtual crisis situation: effects of safety awareness and crisis communication

Incident evaluations show that bystanders tend to help: they do not wait for professionals to arrive, but act as required by the situation at hand. In the present study, we investigated how safety awareness (induced before an accident happened) and providing a course of action by emergency services affect helping behavior after witnessing a virtual accident with two victims. The main task of the participants was to arrive at a job interview in time. Safety awareness was manipulated by the specific organization they went to: either promoting safe traffic or healthy living. The results show that all participants were inclined to help. Participants who were primed towards safe traffic more often called the emergency number, but talked to the victim less often. Participants who had received specific courses of action moved the victim less often. In all, the results clearly indicate the value of effective risk communication (before an event occurs) and crisis communication (after an event has occurred), as both types of information improve the quality of actual helping behavior at the scene.     

A new technique for calibrating stochastic volatility models: the Malliavin gradient method

We discuss the application of gradient methods to calibrate mean reverting stochastic volatility models. For this we use formulas based on Girsanov transformations as well as a modification of the Bismut–Elworthy formula to compute the derivatives of certain option prices with respect to the parameters of the model by applying Monte Carlo methods. The article presents an extension of the ideas to apply Malliavin calculus methods in the computation of Greek's.     

Implementing control mutuality using prediction markets: a new mechanism for risk communication

In this article prediction markets are presented as an innovative technology that can facilitate effective risk communication. The application of prediction markets in this context is an opportunity to reconcile the disparate approaches currently discussed in the risk communication literature. We demonstrate that control mutuality is uniquely achieved through the use of the prediction market mechanism and it is thus an innovative and in some cases optimal technology for use in risk communication in both public and private sector organisations.     

Perception of technological risk: insights from research and lessons for risk communication and management

Technology has become a familiar companion in all areas of life. Production consumption, administration, education, communication, and leisure activities are all shaped by the use of technologies. However, people often associate with the term ‘technology’ attributes, such as catastrophic and potentially dangerous, and tend to take the benefits of technological products for granted. This asymmetry in risk–benefit perception is one of the main reasons why many consumers are concerned about ‘hidden’ risks of technologies and why they demand stringent regulatory actions when they feel unduly exposed to potential emissions or waste products associated with the lifecycle of technologies. This situation is often aggravated by social amplification processes, by which even small risks receive high media attention and are blown out of proportion in the public arena. At the same time, however, one needs to acknowledge that many technologies have the potential to harm the human health and the environment. The purpose of this article is to review our knowledge about risk perception with respect to technologies, in particular emerging technologies, and to suggest possible strategies to use this knowledge for improving our risk management practice. Technological risk perception is defined in this article as the processing of physical signals and/or information about a potentially harmful impact of using technology and the formation of a judgment about seriousness, likelihood, and acceptability of the respective technology. Based on the review of psychological, social, and cultural factors that shape individual and social risk perceptions, we have attempted to develop a structured framework that provides an integrative and systematic perspective on technological risk perception and that may assist risk management and regulation in taking perceptions into account.     

Optimal investments for risk- and ambiguity-averse preferences: a duality approach

Ambiguity, also called Knightian or model uncertainty, is a key feature in financial modeling. A recent paper by Maccheroni et al. (preprint, 2004) characterizes investor preferences under aversion against both risk and ambiguity. Their result shows that these preferences can be numerically represented in terms of convex risk measures. In this paper we study the corresponding problem of optimal investment over a given time horizon, using a duality approach and building upon the results by Kramkov and Schachermayer (Ann. Appl. Probab. 9, 904–950, 1999; Ann. Appl. Probab. 13, 1504–1516, 2003). Supported by Deutsche Forschungsgemeinschaft through the SFB 649 “Economic Risk”.     

Precautionary advice about mobile phones: public understandings and intended responses

There is a widespread academic and policy debate about public responses to precaution in public health campaigns. This paper explores these issues in relation to the precautionary stance adopted in the UK around the regulation of mobile telecommunications. The aim of the paper is to examine the nature of attitudes to precaution, and the way in which these, along with other relevant variables, relate to the intention to adopt relevant behaviours. The results from an experimental study (n = 173) indicate that people distinguish between two dimensions of precaution: firstly in relation to its value or necessity per se and secondly as anchored to notions of governance. The two variables differentially relate to other variables including trust and uncertainty, and are predictive of intended behaviour change indirectly, through worry about mobile phone risks. Precautionary advice was generally interpreted as causing concern rather than providing reassurance. The results suggest that precaution may be considered a valuable stance but this does not mean that it is seen as good governance or that it will reduce concern. Whilst the discourse of precaution is aimed at reducing concern, it appears that the uptake of relevant behaviours is largely triggered by worry.     

On long-term credit risk assessment and rating: towards a new set of models

Institutional investors are supposed to assess credit risk by using a combination of quantitative information such as option models and qualitative assessments. Although option models can be easily constructed, they are not so suitable for the assessment of long-term credit risk that is required by institutional investors. This is mainly because the probability of bankruptcy varies so widely depending on the timing of assessment. We propose a new set of assessment models for long-term credit risk which does not necessarily use stock prices and may incorporate business cycles. The new grand model consists of the two pillars: a long-term cash flow prediction model and a credit risk spread assessment model. The calculated values derived from these models are effectively usable for reasonable calculation of risk spreads. It is quite interesting to see that our investigation indicates that rating bias may exist in the credit risk assessment of the market.