Through the experience of battling against the rampaging COVID-19 pandemic, we have established that there are at least three specific phenomena appearing whereby we can recognize risk.
First, because the COVID-19 pandemic has all the factors which increase our risk perception, we tend to think that the risk factor associated with COVID-19 is much higher than other conventional, familiar risks with high fatality rates. Of course, this tendency does not apply to countries with a lower population than Japan but who nevertheless are recording hundreds of COVID deaths every day.
Second is the style and tone of reporting risk information by the mass media in Japan.
Third, we can identify cognitive biases emerging among the population when risk information is spreading across society.
In this report, I will first explain the concept of risk, and then discuss these three phenomena.
1. Concept of risk
Do you know how risk analysts define risk? The general definition of risk is the "degree of damage" arising from the occurrence of a risk event. Although some analysts adopt this definition, risk professionals typically define risk as to the "probability of damage occurring" as well as the "degree of damage."
Let's examine this definition by quantifying the degree of damage from the virus. Assume that there are two types of virus: Virus X and Virus Y. Also assume the degree of damage from Virus X is 50 (with relatively minor symptoms), while the degree of damage from Virus Y is 100 (with more severe symptoms). For the probability of damage occurrence, Virus X is 100 percent (i.e., 100 percent of infected individuals show minor symptoms), and Virus Y is 40 percent (i.e., 40 percent of infected individuals show severe symptoms). For the purpose of calculation, we can set a 100 percent probability as "1" and a 40 percent probability as "0.4."
Then, we tentatively assess the risk of these viruses in two ways, by including the probability factor and excluding the probability factor. For the calculation of risk reflecting the probability factor, we multiply the degree of damage by the probability of damage occurrence; thus, the risk associated with Virus X will be 50 x 1 = 50, while the risk associated with Virus Y will be 100 x 0.4 = 40. In this case, the risk associated with Virus X is more extensive than Virus Y. In the case of risk without reflecting the probability factor (i.e., risk = the degree of damage), Virus X is 50 and Virus Y is 100: therefore, the risk of Virus Y is twice as extensive as that of Virus X. Please note that, for infected individuals, the probability of occurrence will be considered as 100 percent, regardless of whether the probability has been assessed as low. This indicates the complexity of risk assessment.
The concept of "probability" used here has multiple meanings, including those based on population, infection rates, incidence rates upon exposure to the virus, etc.
2. Factors that increase our risk perception
About 40 years ago, Dr. Paul Slovic and his co-researchers conducted research on factors that increase risk perception, by focusing on the two dimensions of "unknown" and "fear." The unknown factor literally means "unknown things." SARS-CoV-2 is considered to be a mutation of the existing coronavirus, which has numerous unknown features such as developing blood clots and evading the immune system, while the existing coronaviruses are common cold viruses.
The fear factor also literally means "a sense of dread or fear." For example, when news of the COVID-19 outbreak in Wuhan, China, surfaced in January 2020, TV and the internet reports showed numerous people lying stricken in the streets and hospitals, which gave rise to a strong sense of fear along with a sense of "facing unknown things."
In addition to a sense of facing unknown things and fear, uncontrollable threats, and selective access to risk information also increase risk perception. In the case of the current pandemic, uncontrollable threats include the absence of quick remedies and vaccines as well as the absence of reliable safety assurance measures, which, as a result, increased the perception of risk.
Selective access to risk information means, for example, a situation where we only hear about patients who are critically ill or have died from COVID-19 but rarely hear about patients with minor symptoms or without any symptoms. In this case, we do not access all information equally but only specific information. The current COVID-19 pandemic has various factors to increase the perception of risk, including the unknown factor (there are numerous unknown things about the virus), the fear factor (a large number of people are critically ill or have died), the uncontrollable-threat factor (there is no remedy to control the virus), and the factor of selective information access (the mass media intensively report the number of deaths due to COVID-19 every day but rarely talk about those with minor symptoms or no symptoms).
3. Media reports that increase the perception of risk
As I have discussed above, the mass media's stories are often selective. They tend to focus on information about infected people rather than the uninfected or focus on news about patients who are critically ill or have died rather than those with minor symptoms or no symptoms. As a result, the viewers are more likely to have an impression that the virus is "unknown, dreadful, and uncontrollable."
For example, at the beginning of the pandemic, there were very few reports that compared the risk of COVID-19 with other conventional risks (such as cancer and heart diseases). Only recently have the mass media paid more attention to the comparison with other risks. There are numerous other risks such as normal influenza, traffic accidents, illnesses other than COVID-19, or death resulting from a criminal act that we should compare with the risk associated with COVID-19. In other words, the mass media influenced the public perception of risk by giving intense reports on COVID-19, which is an unknown virus, without providing the public with opportunities to compare it with other risks.
The mass media may have no choice but to deliver the news in such a way, considering the nature of the industry. Therefore, it is essential for the public to acquire "information literacy" in order to objectively evaluate the accuracy and reliability of each news story provided by the mass media. A lack of such information literacy at an organizational level, such as companies and schools, will cause more serious problems, which I will discuss in the next report.
Another characteristic of the mass media is that they only report data on "frequency (headcount)" instead of "probability." For example, "Today's Number of Infections," which is announced every day, only reports the number of newly infected people instead of the probability of infection. As a result, viewers tend to pay attention to the number of infected people and pass over the probability of infection.
For example, in the case of Tokyo, where about 140 million people live (the total population of Tokyo as of September 2020 was 13,981,782), if 400 people are infected, the probability of infection is one in 35,000. In contrast, if 100 people are infected in a provincial town with two million residents, the probability of infection is one in 20,000, which is higher than that of Tokyo. However, if we only focus on the number of infected people (400 vs. 100), Tokyo seems four times more dangerous than the provincial town.
As a side note, getting a positive result from the PCR testing for COVID-19 does not necessarily mean that the coronavirus is growing in the body, i.e., infectious.
The PCR testing detects the presence of viral RNA to determine the existence of the virus or gene fragments based on DNA samples extracted from the body.
Therefore, if samples are taken from a handrail or the surface of a smartphone where viral RNA remains, a positive PCR result is obtained. If the PCR testing has a high degree of sensitivity, a positive result will be more likely to be determined. In contrast, if samples are taken from areas in the body where no viral RNA is detected, the result will be negative, even though the virus is actually growing in the body.
Nevertheless, if a person who belongs to an organization such as a company or school gets a positive PCR result, the organization might be affected substantially, such as receiving on-site inspections by a public health center. The benefits of scientific technologies such as PCR testing enable more effective action to prevent the spread of the coronavirus infection, but at the same time, they motivate social discrimination against infected people. This indicates that we need to address the issue of scientific literacy as well. The advancement of scientific technologies should not promote bias and discrimination against the vulnerable in society. For example, enhanced accessibility to genetic information may raise the same issue of literacy. Leading-edge technologies developed with superior brainpower and considerable expense could negatively affect human well-being, but we should prevent that from happening.
Although I will not discuss it in detail here, it is an urgent requirement to solve the ongoing fundamental question: what is required to ensure the advancement of scientific technologies that can enhance human well-being?
4. Individual and interpersonal biases in processing risk information
We human beings tend to have various cognitive biases when we process risk information. These biases make us either overestimate or underestimate the true nature of the risk.
The factors that decrease the perception of risk are familiarity, confidence, and general access to information. They stand in contrast to those factors that increase the perception of risk, that is, unknown, fearful, or uncontrollable things, and selective access to information, as I mentioned above.
Another example of biases is "normalcy bias," which leads to a typical attitude towards familiar risks. To understand the concept of this bias, there is the metaphor of the boiling frog. If a frog is placed into a pot of tepid water, which is then brought to the boil slowly, it will think "the water is getting warm, but it should be OK" and will remain in the water until it boils to death. The mechanism of the normalcy bias is the same. When people observe warning signals or abnormal events, these signs may be regarded as being within the normal range until it is too late to escape from disaster. In other words, this is one of the cognitive biases to make us underestimate a risk until it is too late to avoid it.
We should immediately take action, such as evacuation, to avoid risk when we recognize any abnormality.
However, if we evacuate whenever we recognize any sign of abnormality, 99 percent of our evacuation activities may have no outcome and will be regarded as wasting of time and energy. This is why people tend to think "it should still be OK" when observing warning signs.
There is another metaphor in the fable of "The Boy Who Cried Wolf." There was once a boy who looked after some sheep. When he got bored, he cried, "Wolf!" to get attention from the local villagers. The villagers came to his help several times and finally realized he was deliberately giving a false alarm. Later, when there was real danger, the boy cried "Wolf!" again, but nobody came to help him, and the wolf made a good meal off the boy's flock. This metaphor also describes the essence of normalcy bias effectively.
There is another bias that increases the perception of risk among a group of people, for which I coined the term "distance bias." I strongly recognized this bias when the issue of radiation contamination from the Fukushima plant became controversial. I was in Tokyo at that time. Many people living in Tokyo tended to think that the entire Fukushima area was dangerous, while people living in areas more distant from Fukushima than Tokyo probably thought that even Tokyo was dangerous. For people living in Fukushima, they must have examined safety zones closely by checking the wind direction and other factors.
At that time, I went to New York for one week. At the hotel there was a TV monitor in the elevator. I was casually watching the screen and saw a wave of radiation shown spreading across Japan and flowing to the American continents. I was surprised at such an absurd image to the eyes of Japanese people. Then, I realized that "when risk perception towards a specific area increases, the greater the geographical distance is, the more people tend to generalize the risk of the entire area."
Another experience was the situation of Thailand when I visited about eleven years ago (2009). The corruption issue of the previous Prime Minister Thaksin Shinawatra (2001-2006) was a major point of contention at that time, and massive anti-Thaksin demonstrations were reported as taking place in Bangkok every day. The mass media in Japan constantly reported, "Bangkok is dangerous!" All educational travel, such as school trips to Bangkok, was summarily canceled. At that time, I had to visit Bangkok but never saw any demonstrations on the street during the ten days of my stay. Again, I had the strong realization that the greater the geographical distance, the more mass media tend to report as if a whole area is hazardous.
The greater the distance from a high-risk area, the more people living in distant areas tend to assume that the high-risk area is large and entirely including the surrounding areas. Furthermore, people living in low-risk areas tend to generalize their overestimated assumption that it is dangerous everywhere in the high-risk area. In contrast, people living in the high-risk tend to grasp the risk more narrowly and conclude that the risk is limited; only specific zones are dangerous, which prevents the generalization of the risk across the area.
For example, people living in Tokyo, where the number of infections is high, tend to consider that "the downtown areas of Tokyo are dangerous, but the local community I live in should be OK." In contrast, people living in provincial areas with relatively few infections tend to think that "All people from Tokyo are dangerous." In other words, the greater the distance from the high-risk area is, the more people living in the low-risk areas tend to think that all residents and all zones of the high-risk area are dangerous.
This bias might have prompted the negative attitude of people living in provincial areas, rejecting travelers from Tokyo under the Go-To Travel campaign.
Moreover, this attitude might have resulted from the motive to avoid risk, that is, "I don't want to take any more risk." After all, this motive to avoid risk is a root cause of biases and discriminations relating to various types of risk, including COVID-19.
It should be noted that if we avoid risk all the time and no damage is incurred, we tend to think, "We are unaffected and safe because we avoided the risk." We can never identify whether we are safe because we avoided the risk, or we are still safe even if we did not avoid the risk. In this way, we overlook the opportunity to reduce our bias towards risk; rather it will continue to be maintained or increase, and consequently will let discrimination be preserved.
There are numerous other ideas about the perception of risk I would like to discuss, but I will finish here. I will talk about the COVID-19 pandemic and school management in the next report.
- Slovic, P. "Perception of risk", Science, 236, pp.280-285, 1987
Press release by the Bureau of General Affairs on September 30, 2020, regarding the population of Tokyo as of September 1, 2020: