Method comparing risks
Over-indebtedness, damaged luggage or accidents at work - What is more likely to happen?
Improving consumers' risk assessments.
Why is it relevant to improve consumers' risk assessments?
The aim is for the consumer to be able to make better decisions. Particularly when these decisions affect certain life risks, it is important not only to have contextual knowledge about the risk, but also to understand how likely the risk is going to occur.
However, many people tend to overestimate certain risks, such as catastrophic events like plane crashes. False conclusions can be drawn from this, which in turn increase risk: If you substitute the flight with driving the distance by car, the risk of a fatal accident increases.
In the case of risks that develop very slowly but occur more frequently, their occurrence is often underestimated, e.g. type 2 diabetes. In this case, the correct conclusions are often not drawn in order to reduce risk factors, e.g. to exercise more.
Why is it problematic to improve consumers' risk assessments?
If you want to improve consumers' risk assessments, you face a number of challenges:
- How can probabilities of occurrence be conveyed in a way that leads to a more realistic perception of risks, and thus an assessment of their occurrence?
- How can overestimated and underestimated events be conveyed in a way that leads to a more realistic assessment of their occurrence?
- How can interest in engaging with risk statistics be sparked?
The interest in dealing with statistics can be sparked by the personal involvement of learners through letting them test their own risk assessments. Through paired comparison tasks, learners can rely on their gut feelings without having to estimate a specific number. The feedback they receive helps them to learn when they are right. Paired comparisons of facts can also stimulate conclusions that can be transferred to other situations (Alfieri et al., 2013): e.g. when learners think about what two risks have in common and what distinguishes them. The meaning of explicitly stated characteristics of two decision options is better comprehended when presented as pairs than individually (Pachur & Olsson, 2012). Whether the mechanism shown for comparing characteristics works just as well if the characteristics are only to be imagined (for example, thinking that one risk is man-made, but the other one is natural) remains unclear.
Interactivity can be used to nurture interest. It should be used in an economical, targeted and coherent way with the necessary controls and content. Since this is a playful transfer of knowledge from the adult world (Barth, 2018), this interest can be reinforced among certain consumers by addressing the motive of power (competition, status) (Sailer et al., 2014) by comparing their own risk assessment abilities with those of others. The reduced design refrains from presenting further details that motivate interest. These could contradict the actual goal, particularly if they do not fit harmoniously into the content, or even distract or simply lead to confusion (Eitel & Kühl, 2019).
For risk comparisons, probabilities of occurrence are communicated using simple frequencies with the reference group of 100,000. Particularly probabilities of occurrence of rare events can be communicated more understandably than percentages in this way. Visual approaches such as icon arrays can further facilitate these comparisons, but the probability of occurrence for many risks is too low to be recognizable on a small display.
Proof of effectiveness
Comparisons of uncertain events with regard to their occurrence can improve understanding. Our studies show that this is also true for the playful approach of personal assessment with social feedback ("How often did others give a correct answer?") compared to tables.
- Alfieri, L., Nokes-Malach, T. J., & Schunn, C. D. (2013). Learning through case comparisons: A meta-analytic review. Educational Psychologist, 48(2), 87–113.
- Barth, R. (2018). Möglichkeiten der Nutzung von Game Design Prinzipien in der Erwachsenenbildung. digital. innovativ|# digiPH, 109–118.
- Eitel, A., & Kühl, T. (2019). Harmful or helpful to learning? The impact of seductive details on learning and instruction. Applied Cognitive Psychology, 33(1), 3–8.
- Pachur, T., & Olsson, H. (2012). Type of learning task impacts performance and strategy selection in decision making. Cognitive Psychology, 65(2), 207–240.
- Sailer, M., Hense, J., Mandl, J., & Klevers, M. (2014). Psychological perspectives on motivation through gamification. Interaction Design and Architecture Journal, (19), 28–37.
Option 1: You can embed the given visualisation
<iframe frameborder="0" height="650px" src="https://static.risikoatlas.de/modules/comparing-risks/index.html" width="1024px"></iframe>
Over-indebtedness, damaged luggage or accidents at work - how likely are these things going to happen to you during the next year?
When do I need this visualisation?
The risk comparison influences your perception of risks and is supposed to improve your risk assessment. Improved risk assessment enables you to make future decisions on a more realistic basis. It is not intended to support concrete decisions.
What does the visualisation show?
The visualisation compares randomly selected risks with regard to the probability of their occurrence within one year. It guides you from comparison to comparison. Two risks are presented at the same time. You then decide what is more likely. The risk that is more likely to occur is coloured green. In addition, you receive ongoing feedback on how many of all comparisons you and others have made correctly. The frequency format with 100,000 each as reference group and the absolute numbers are to avoid the use of smaller percentages, since percentages of probabilities of occurrence of rare events (<1%) are estimated incorrectly by the majority of people.
- Bach et al, 2014. DIW.
- BKA Bundeslagebild 2017.
- https://www.schlichtungsstelle-energie.de/presse/presseartikel/taetigkeitsbericht-der-schlichtungsstelle-energie-39.html; Bericht der Bundesnetzagentur 2017
- Schufa-Report 2018.
- Schufa-Report 2018.
- Statistisches Jahrbuch der Versicherungswirtschaft.
What is the quality of the data?
The numbers and data included in the risk comparison come from various sources, e.g. frequency observations of public registers, surveys, population studies or models. Most of these risk estimates are characterised by a relatively low quality of evidence. This means that future assessments are likely to lead to different results.
There are several reasons for this:
1) There is a lack of randomised controlled trials that compare consumer actions and inactions, and thus demonstrate that certain events are caused by these actions.
2) The registration of the occurrence of consumer events in Germany is based too little on qualitative systematic population studies, but rather primarily on the number of people affected.
3) The registration of consumer risks is also always subject to conflicts of interest or self-interests, which can weaken the informative value of data.