The first essay I wrote for On Risk and Reason.
General overview: At first glance, information about risk may seem straightforward and have obvious implications for behavior: Using sunscreen reduces the risk of skin cancer, sleeping 8 hours a night is good for mental and physical health, washing your hands regularly helps reduce the risk of getting the flu. Are those choices to engage in these preventative behaviors as obvious as they may appear to be? Apparently not: one-third of Americans don’t use sunscreen (Washington Post, 2009), lack of sleep is implicated close to 100,000 car accidents per year (National Highway and Traffic Safety Administration, n.d.), and about one-third of people don’t wash their hands after using a public restroom (American Society for Microbiology, 2003). So what is going on? Risk is ultimately about cause and effect, but causal relationships are actually quite complex. Reasoning about causality turns out to be fairly complicated. In this essay, you will compare the causal structure of two risks in order to see how causal complexity can vary and consider how that causal structure may affect people’s reasoning about the two risks.
What you will do: For this essay, you will consider two health risks—you can choose from the risk of developing skin cancer, coming down with the flu, or the consequences of sleep deprivation. Using the taxonomy on causal complexity in the reading by Grotzer, Miller, & Lincoln (2011), you will explain what types of complex causality exist in these health risks and how the comparison of the causal structure of the two risks helps us better explain why reasoning about risk is difficult.
How to get started: In an essay in which you apply a framework (or lens, as it is sometimes called), it is first helpful to define the terms of the framework. For our purposes here, that means you first have to start out by explaining what the taxonomy is and what it is meant to do. Once you have done that, you can then move on to showing how it helps you explain the causal structure of two of the health risks. What does the taxonomy help you understand better about the health risk? How does comparing the causal structure of the two risks help you explain the relative risk perception of these two health risks? Is one risk easier to perceive than another? Alternatively, when taken together does the complex causality of the two risks underscore why risk is a generally difficult concept to reason about?
Big Picture: This essay is designed to give you practice in applying a framework and constructing comparative writing. Grotzer et al. (2011) provides a framework in that they identify the dimensions of causality and the ways they can vary according to a scale that ranges from simple to complex. You will use the categories they provide to compare the type of causality inherent in the two risks. The writing assignment gives you the opportunity to think through how to compose an essay that involves both of these analytical strategies.
Despite their best efforts, health officials still encounter substantial difficulties in convincing the public to take precautionary, preventative measures. Whether it is getting enough sleep or washing one’s hands, these actions garner less attention than health proponents would prefer. To this end, advocates have urged for increased health education to bridge gaps in knowledge between the public and the scientific community. But after researching scientific education, Grotzer, Miller and Lincoln (Grotzer, Miller & Lincoln, 2011) propose that some of the difficulties faced by health advocates lie in the mismatch between humans’ emotionally driven processes and the complexity of scientific information. In addition, they propose a risk specific taxonomy that identifies nine axes of causal complexity that affect the perception of risks based on a risk’s cause and effect. Grotzer et al. (2011) suggest that a risk with a multitude of causes is more causally complex than one without, using global warming as an example. This increase in complexity reduces the amount of salience attached to the risk. However, absent in their analysis is the exploration of the impact a multitude of effects generated by one cause has on causal complexity and risk perception. Both influenza and sleep deprivation are examples of risks of this nature, where one cause can have multiple effects. Using Grotzer et al.’s risk taxonomy to analyze influenza and sleep deprivation reveals that risks with multiple effects may best be understood as several independent risks that share a common cause and the same alias. Furthermore, when risks stemming from a cause differ in causal complexity, risk perception will alter human behavior to focus on the risk with most salience, even if the solution generated ignores the underlying cause.
In determining that human perception developed in a more primitive environment with different risks, Grotzer et al. (2011) identify nine causal features of risks that affect perception such as the time period between cause and effect and the reliability or obviousness of cause and effect. A risk can be categorized as being causally complex or causally simple along these nine spectrums. Grotzer et al. (2011) illustrate these dimensions with two examples from Picher, Oklahoma, a Superfund site. The first scenario examined is getting stung by a bee and the second is getting lead poisoning. In the case of a bee sting, the time period between cause and effect is immediate, the effects are deterministic, and both the cause and effect are obvious, both to the victim and to any observers. In Grotzer et al.’s taxonomy, this risk is causally simple. However, in the case of lead poisoning, the time period between effect and cause is delayed, the effects are probabilistic, and the cause, intake of dangerous metals while playing on piles of chat, is non-obvious. These factors make the risk more complex and less perceivable and as a result, less salience is attached to this risk than to a bee sting. As Grotzer et al. (2011) describes, parents in Picher were more likely to act to prevent their child from being stung than from playing on the chat. Simple risks garner more salience and are more likely to spur action.
However, health risks present situations that do not fit precisely into the model presented by Grotzer et al. While risks such as smoking, AIDS, and global warming are presented as possible risks to be analyzed, Grotzer et al. (2011) do not analyze them. Instead of being composed of a single cause and effect like the lead poisoning and bee sting cases, these risks often present several different effects that may differ in time scale or severity. The interaction between the effects and the interaction’s influence on perception is not directly addressed in Grotzer et al.’s analysis. Knowing the consequence multiple effects has on perception would help contribute to Grotzer et al.’s goal of improving science and risk education.
In beginning to accurately assess the influence a risk’s effects might have on perception, choosing a risk with clearly different effects is important. Sleep deprivation presents itself as one such risk. Sleep deprivation has effects that can be grouped into two distinct categories. The first group would contain the short-term effects of sleep deprivation such as decreased performance and alertness, memory impairment, and mood affects (Centers for Disease Control [CDC], 2014). The second group would contain the long-term effects of sleep deprivation: obesity, depression, heart attack, etc (CDC, 2014). The effects in the first group are deterministic and immediate whereas the effects in the second are probabilistic and delayed. Under Grotzer et al.’s framework, more salience would be attached to the effects in the first group. If there were to be differences caused in perception by the difference in salience garnered by both groups of risks, they should be observable in how people respond to sleep deprivation.
When asked to consider the risks of sleep deprivation, the vast majority of people agreed that sleep deprivation leads to difficulty concentrating and increased stress (Better Sleep Council [BSC], 2013). This is consistent under Grotzer et al.’s (2011) framework because the short-term effects of sleep deprivation are causally simple and therefore more perceivable. However, when asked whether they associate sleep deprivation with long-term effects such as heart disease and diabetes, a significantly fewer proportion of people agreed (BSC, 2013). This is also consistent with Grotzer et al.’s (2011) framework. These effects are temporally distant, probabilistic, and are linked with multiple other causes. When asked how they cope with the effects of sleep deprivation, nearly half of men reported believing that people can be trained to need less sleep (BSC, 2013). Similarly, nearly a third of adults regularly turn to coffee or caffeine to make up for lost sleep (BSC, 2013). Grotzer et al. are correct in assessing that the causal complexity of the long term effects like heart disease are preventing people from perceiving them, but some of the information encoded in this behavioral pattern is lost in a simple analysis with their taxonomy. The causal difference in effects means that it is possible for people to split their perception between the two despite both being consequences of the same cause. Because trouble concentrating and other short term effects capture much more salience than the long-term effects of sleep deprivation, human reaction may be directed towards solving or preventing individual risks rather than addressing the root cause as shown in the survey results.
People tend to perceive, or rather medicate, as if the effects of sleep deprivation were different. However, some risks present effects that are similar in causal complexity. Influenza presents itself as one possible case study. The group of influenza viruses causes influenza, meaning the cause is non-obvious as contact with the influenza viruses is imperceptible (Centers for Disease Control [CDC], 2011). Consequences of influenza can be split into two categories, the first containing effects associated with the flu, such as fever, sore throat, runny nose, etc., and another containing complications from the flu, such as pneumonia, bronchitis, and sinus and ear infections (CDC, 2011). In terms of causal complexity, the first group of consequences has a delayed onset, is probabilistic, but is obvious. The second group of effects is even more delayed than the first, probabilistic, affects only a subset of those who suffer the first group of consequences, and obvious. Each of these sets of consequences is causally complex. Influenza’s effects are causally complex, which may explain why less than half of Americans report getting a flu shot yearly (CDC, 2011).
It is unlikely, based on the responses and reactions to sleep deprivation, that people’s perceptions of influenza and its effects would elicit different actions. The two groups of effects that influenza presents are similar in causal complexity and in causality. It is most likely that the perceptions of both threats are mapped on top of each other, meaning that the existence of similar subsequent effects do not contribute to the salience garnered by the primary effect. In the case of influenza, consequences of the flu would likely not make more people get flu shots. Additionally, since the most relevant preventative measure of both effects is getting a flu shot, there is increased difficulty in unearthing a difference in actions.
Though there appears to be limited use in separating the effects of a risk such as influenza, doing so in cases such as sleep deprivation provides greater insight into human behavior and helps guide future risk education. In their admirable attempt to help improve science education Grotzer et al. was able to provide a risk taxonomy that helps identify areas of complexity that make science education difficult. In further dissecting risks into their various causes and effects, it appears prudent to analyze human behavior as the result of perceiving multiple different risks independently of one another, although they all might be grouped under one alias. Doing so may help reveal areas in health education where gaps between public and expert knowledge pools may better be bridged and help improve public health for everyone.
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