Communicating Risk Information

What negative consequences may occur when communicating risk information in the “real-world” and how can insights from cognitive psychology help?

What is risk? Ahmed, Naik, Willoughby and Edwards (2012) offered a definition, simply by stating that risk is a probability that something dangerous will provoke harm in the future. Then, what is risk communication? Since the literature about this topic is so vast, a good starting point would be by citing Benjamin Franklin (1789). He wrote in a letter: ”Our Constitution is in actual operation; everything appears to promise that it will last; but in this world there is nothing certain but death and taxes”. Gerd Gigerenzer (2002) only highlighted the last part (”Nothing is certain but death and taxes”) and talked about the uncertainty which the public has to live with. Franklin acknowledged the risk but he offered no solution on how to deal with it. This paper will concentrate on how people communicate risk information in the ”real-world” and what negative consequences it might have on an individual. Also, could cognitive psychology improve this exchange of information?

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Ahmed et al. (2012) continued by explaining that risk communication is a tool which aims to make people understand a certain type of information (for example, a diagnosis of disease) in order to help the patient to make a better decision about his health. But in many cases, risk information is transmitted wrongly and the information will either be correct but too ambiguous explained or the individual will prefer to not ask further question. The negative consequences of these actions could lead to anxiety and depression when receiving such a delicate information. The communication of risk can be found anywhere, from a news reportage (”the terrorism threat level is orange”) to medicine (”risk of a heart attack is 15%”). But a question emerges. How could an individual understand the term ”orange” or the percentage of 15% without a background information. The risk might be intercepted as low because the orange colour is not red or that 15% is too far away from 100%. Therefore, the patient will have the basic perception. If it happened to other people, it does not mean that it will happen to me. He will choose to not ask further questions, but the anxiety could remain in one’s mind. What if could it happen to me? That’s the main purpose of this paper, to analyse how the risk information is transmitted around different areas of interest and how it is perceived among individuals. The public interpret the risk depending on their own understanding and background. The most important thing should be the awareness of how risk is welcomed in people’s minds.

Moreover, uncertainty is a terrible feeling that one will possess several times in a lifetime. Brashers (2001) approached this subject and highlighted the emotions one would feel when a decision has to be made when there is not enough information. In the previous example, when the risk information was communicated through percentages, the individual who received the news that ”the risk of having a heart attack is 15%” might be struggling with the diagnosis. The uncertainty was present. What 15% means? Is there a chance of a future heart attack? Or there is not, mainly because the percentage is just 15%? Both explications could seem equally correct or equally ambiguous, because the individual had not received enough information beforehand. He will convince himself of a certain result, which in most cases might not be correct. Still, there is research which shows that there is no proof on how uncertainty affects the population. (Johnson & Slovic, 1994)

In relation to the facts mentioned above, one concerning issue in how risk is communicated could be the probabilities of percentages. Gigerenzer and Hoffrage (1994) started this debate, by suggesting to replace this method of reporting delicate information with natural frequencies. An explication would be to imagine that individuals possessed a cognitive algorithm that could perform statistical inferences. And what if these algorithms would not be designed to understand probabilities of percentages? Once the humanity progressed, maybe a person would be able to comprehend a natural presentation of specific information, based on previous experiences. According to Gallistel (1990), even animals have a high tendency of recognizing certain changes in frequency distributions in their environment.

Consequently, this fact could also be applied to humans. A proper example for this argument was presented by Gigerenzer (2000) in his book. A story called ”Prozac’s side Effects” began with a psychiatrist friend of the author. When a patient received a prescription to take Prozac, the psychiatrist told him that he had ”a 30 to 50 percent chance of developing a sexual problem”, for example impotence or lack of sexual interest. The curious thing was that no patient asked for an explication of the side effects, even when the psychiatrist observed their increased level of anxiety. The issue to be resolved was the way in which the communication of risk information was perceived. He realised this effect after he read Gigerenzer’s book (2000) and changed his method of approaching the patients. But what was the problem? When he told patients that they had ”a 30 to 50 percent chance of developing a sexual problem”, most of them thought that in 30 out of 50 percent of their sexual encounters something serious might happen. Even if the numbers are correct, from a psychological point were seen in a different light. Once the psychiatrist changed the probability of percentages by expressing the side effects through natural frequencies (”3 to 5 patients will experience a sexual problem, out of 10”), the level of anxiety of individuals decreased considerably and the patients started to ask questions about their condition, such as what could happen if they are among the three to five people? The moral of the story is that even though an experienced doctor gives the correct information to his patient, the reference class was left out. The negative consequences of communicating risk in the real world were that the expression of percentages left the perception of individuals to wonder and invent a reference class. When someone uses natural frequencies, a person would not have to think if that involved his sexual encounters.

When taking about uncertainty, another major flag could be the illusion of certainty. Gigerenzer (2000) concentrated around this tendency of living with an answer that might be wrong in reality. Our subconscious mind, when encountering a certain issue, switches the uncertainty with certainty. This could be included in the communication of risk and its negative consequences, when one struggle with a diagnosis which is perceived at a cognitive level as true. In the real world, when an individual is obligated to deal with a sensitive and ambiguous term, the mind steps in. The perception system will try to understand the information and send it to our subconscious, which it will acknowledge it as a truthful fact.

Another case presented by Gigerenzer (2000) is ”Susan’s nightmare”. The woman received a positive HIV diagnostic, after she used drugs. The disease made her lose her job, her house and she even distanced from her child to protect him. Several months passed and she went to the doctor with bronchitis. Then, she was asked to retest her blood for HIV. No one can imagine her surprise when the test came back negative. But how could this be possible? Apparently, Susan’s blood sample was switched with another patient’s sample. This produced an illusion of certainty. The certainty that Susan was infected and the certainty that the other patient could live. This is called a false-positive, and the problem was that no doctors told her that there were two tests (the Elisa and Western blot) or that mistakes might happen. The outcome was considered as 100% correct and that once a test came positive, even if the other one gave a false-positive, it meant that she had the disease. The consequences of the doctors’ action changed Susan’s life for nine months and almost destroyed her.  Of course, many factors could be considered, such as both tests showing a negative result when in fact she was infected. But whatever the risk might be, it was the doctors’ duty to address these issues and explain the whole situation to Susan.

To avoid the consequences of risk communicant, some cognitive psychologists tried to deal with people’s uncertainty by inventing ‘the cognitive theory of choice’. (Noll & Krier, 1990). It was created with the solely purpose of making people understand the risks that they have to confront every day. In this theory, it was presented that people choose shortcuts every day that causes most of the time their inability to understand probability when assessing the risk that they have to face. This theory tried to analyse how an individual can manage risk regulation, even though the source of mistakes could not be found. Every person is different and they have numerous perception on the world. Still, this theory might help to improve how the public understand risk communication and how the risks should be minimised.

To conclude, risk information is presented in many ways. From an illusion of certainty to the difficult task of making individuals understand percentages, the communication of risk information poses as a major challenge for institutions. Harman (2009) brought up the reality in which people live in, by stating that in order for people to understand the risks that surround them, it would be better to embrace the wrong perception of the individuals and explore it as an opportunity for improvement. Therefore, the ‘cognitive theory of choice’ (Noll & Krier, 1990) steps in, with the hope of reducing the uncertainty which an individual has to live with in the ‘real-world’. However, these methods of dealing with risk will be hard to erase from everyone’s minds.


Ahmed, H., Naik, G., Willoughby, H., & Edwards, A. G. (2012). Communicating risk. Bmj, 344, e3996.

Brashers, D. E. (2001). Communication and uncertainty management. Journal of communication, 51(3), 477-497.

Gigerenzer, G. (2002). Reckoning with risk. London: Penguin.

Gigerenzer, G., & Hoffrage, U. (1995). How to improve Bayesian reasoning without instruction: frequency formats. Psychological Review, 102, 684-704.

Hardman, D. (2009). Judgment and decision making. Malden, MA: Blackwell.

Johnson, B. B., & Slovic, P. (1994). “Improving” Risk Communication and Risk Management: Legislated Solutions or Legislated Disasters?. Risk Analysis, 14(6), 905-906.

Johnson, B. B., & Slovic, P. (1995). Presenting uncertainty in health risk assessment: initial studies of its effects on risk perception and trust. Risk analysis, 15(4), 485-494.

Noll, R. G., & Krier, J. E. (1990). Some implications of cognitive psychology for risk regulation. The Journal of Legal Studies, 19(S2), 747-779.



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