Communication of health risk events is a complex and challenging task. The advent of information and communication technology along with the following popularisation and widespread uptake of social media are reshaping the field of risk communication. Guided by key tenets of the Social Amplification of Risk Framework, this study developed a causal loop diagram, capturing the perceptions of professionals in health organisations regarding the role of Twitter during risk events. The aim of this paper is to explore the use of the causal loop diagram and its role with rationalising the use of Twitter in risk communication strategies. A key finding of the model is the central role of trust and its interrelationship with other factors during a risk event. A contribution is made to operational research through the novel use of soft system dynamics in risk communication, to risk communication through the investigation of the new medium Twitter and also to research on the Social Amplification of Risk Framework by providing a means through which to operationalise the framework.
Mental models are a human's internal representation of the real world and have an important role in the way a human understands and reasons about uncertainties, explores potential options, and makes decisions. However, they are susceptible to biases. Issues associated with mental models have not yet received much attention in geosciences, yet systematic biases can affect the scientific process of any geological investigation; from the inception of how the problem is viewed, through selection of appropriate hypotheses and data collection/processing methods, to the conceptualisation and communication of results. This article draws on findings from cognitive science and system dynamics, with knowledge and experiences of field geology, to consider the limitations and biases presented by mental models in geoscience, and their effect on predictions of the physical properties of faults in particular. We highlight a number of biases specific to geological investigations and propose strategies for debiasing. Doing so will enhance how multiple data sources can be brought together, and minimise controllable geological uncertainty to develop more robust geological models. Critically, we argue that there is a need for standardised procedures that guard against biases, permitting data from multiple studies to be combined and communication of assumptions to be made. While we use faults to illustrate potential biases in mental models and the implications of these biases, our findings can be applied across the geoscience discipline. What do you think of when you think of a geologist? In a study of the representations of scientists in 222 Hollywood films, Weingart et al. (2003) found that scientists were predominantly white (96%), male (82%), American (49%), and middle aged (40% between 35 and 49 years old). They found interesting variations in the characters of scientists based on discipline. Medical researchers, physicists, chemists, and psychologists were most likely to be portrayed as 'mad scientists', whereas anthropologists, astronomers, zoologists, and geologists were more likely to be depicted as 'good' or 'benevolent'. Further, 'benevolent' scientists were often portrayed as naïve! The stereotypical portrayal of scientists in movies is an example of analogous thinking; that is, people's 'mental models', which are internal representations of something in the real world (Johnson-Laird 1983). Such analogous thinking helps people to interact with and make sense of a (complex) external reality, often by allowing them to solve problems or make judgments quickly and efficiently. However, mental models are developed from biased input information and are thus inherently limited. This can be both beneficial and problematic. Every model is a simplification of reality and therefore every model is wrong (Sterman 2002; Poeter 2007). Increasing the complexity of a mental model does not necessarily make the model more useful, however overly simplified models can omit key factors. For humans, models are the foundation of decision making, ...
<p>Mental models are a human&#8217;s internal representation of the real world and have an important role in the way a human understands and reasons about uncertainties, explores potential options, and makes decisions. However, they are susceptible to biases. Issues associated with mental models have not yet received much attention in geosciences, yet systematic biases can affect the scientific process of any geological investigation; from the inception of how the problem is viewed, through selection of appropriate hypotheses and data collection/processing methods, to the conceptualisation and communication of results. This presentation draws on findings from cognitive science and system dynamics, with knowledge and experiences of field geology, to consider the limitations and biases presented by mental models in geoscience, and their effect on predictions of the physical properties of faults in particular. We highlight a number of biases specific to geological investigations and propose strategies for debiasing. Doing so will enhance how multiple data sources can be brought together, and minimise controllable geological uncertainty to develop more robust geological models. Critically, we argue that there is a need for standardised procedures that guard against biases, permitting data from multiple studies to be combined and communication of assumptions to be made. While we use faults to illustrate potential biases in mental models and the implications of these biases, our findings can be applied across the geoscience discipline.</p>
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