App for decision support
What do experts check?
If there is a lack of reliable data on the occurrence of specific events or knowledge on the consequences of decisions, there is a problem of uncertainty. Better decisions can hardly be achieved through the trained use of statistics or their transparent communication. Instead, the central question is how individual consumers can reduce uncertainty in their decision-making situation. Two scenarios are central here:
How can uncertainty be reduced (quickly, practically) for everyday problems in which consumers are left to their own devices?
How can uncertainty be reduced (quickly, practically) for everyday problems for which an expert provides advice to the consumer?
Why is it difficult to provide decision support for problems of uncertainty?
Decision problems of uncertainty are characterised by a lack of reliable data. This effectively rules out the direct selection of the best decision option. The support consists of identifying key strategies to reduce uncertainty. What do I need to ask to reduce the choice of potential information or options? What do I need to look for? What do I need to consider to sort out inappropriate options that do not meet the minimum requirements?
In many cases, consumers who have entered professional life can no longer be reached through institutional education. In this respect, educational support measures cannot be directed at them via schools, vocational schools or universities. The support measures, which involve effort in terms of access and use and learning, should therefore be communicated in terms of the possible benefits for the consumer. At the same time, channels are needed to reach consumers, for example digital ones like the RisikoKompass app.
How can these challenges be addressed not only according to the latest state of research, but also in an appealing way, since it is necessary to attract and bind the consumer's attention to the decision support tool?
How can a technical solution benefit everyday life, i.e. how can a competence-promoting assistant help make consumers more competent in the long run?
In contrast to consumers, experts in a particular subject area are able to identify objective shortfalls in the standard of a decision problem on the basis of fewer heuristic features. With the help of an analysis of specific consumer decision situations, possible expert heuristics are distilled into decision trees. These summarize the experts' gut feeling based on their experiences and provide consumers with a robust expertise that enables them, similar to the expert, to separate the wheat from the chaff.
Fast-and-Frugal Trees (FFTs) are suitable decision trees that can be transparent, comprehensible to consumers and of high quality at the same time. They represent a sequence of features to be examined (Martignon et al., 2008), are an evidence-based tool for decision support, and are easy to implement. In the RisikoAtlas project it was developed and implemented for the first time for everyday consumer practice. The use of FFTs is also helpful because their application trains skills. The use of FFTs facilitates the internalisation of key characteristics for problems and stimulates critical thinking.
This is not only important for issues where consumers are left to their own devices. Potential decision heuristics can also be combined in decision trees for consulting situations: Here it is a matter of asking the consultant the most important questions in order to be able to assess this situation robustly.
The use of an app as a collection of decision trees (assistants) can be appealing if it provides immediate help, meets consumer expectations of a modern app, is straightforward and easy to understand, and operates error-free.
An app is beneficial for everyday life when its use trains certain skills. The use of decision assistants facilitates the internalisation of key features for consumer problems and might stimulate critical thinking. In this way, you learn through the app in an independent way.
- Aikman, D., Galesic, M., Gigerenzer, G., Kapadia, S., Katsikopoulos, K. V., Kothiyal, A., ... & Neumann, T. (2014). Taking uncertainty seriously: Simplicity versus complexity in financial regulation. Bank of England Financial Stability Paper, 28.
- Green, L., & Mehr, D. R. (1997). What alters physicians' decisions to admit to the coronary care unit?. Journal of Family Practice, 45(3), 219–226.
- Jablonskis, E., & Czienskowski, U. (2017). Decision trees online. http://www.adaptivetoolbox.net/Library/Trees/TreesHome#/
- Jenny, M. A., Pachur, T., Williams, S. L., Becker, E., & Margraf, J. (2013). Simple rules for detecting depression. Journal of Applied Research in Memory and Cognition, 2(3), 149–157.
- Luan, S., Schooler, L. J., & Gigerenzer, G. (2011). A signal-detection analysis of fast-and-frugal trees. Psychological Review, 118(2), 316.
- Martignon, L., Katsikopoulos, K. V., & Woike, J. K. (2008). Categorization with limited resources: A family of simple heuristics. Journal of Mathematical Psychology, 52(6), 352–361.
You can recommend the app and refer to the PlayStore (https://play.google.com/store/apps/details?id=de.mpg.hardingcenter.RisikoKompass).
You can also receive our contents (decision trees) as illustrations or PDF files.
How do you make good decisions when a lot of information and products are available? Reduce the options by separating the wheat from the chaff! But what is quality and demand-oriented?
This app provides you with the view of experts who make decisions - be it trustworthy health information or quality violations in investment advice. For this purpose, the most important test criteria were identified using machine learning methods at the Max Planck Institute for Human Development in Berlin, more precisely the Harding Center for Risk Literacy. You can test it for yourself and thus make better decisions!
The app offers support in the form of decision trees (assistants on various topics). They only suggest the most important questions to the consumer in order to reduce uncertainty in concrete problem situations.
In addition to assistants based on Fast-and-frugal Trees, natural frequency trees are also embedded. The latter model is specifically designed to help understand conditional probabilities (McDowell & Jacobs, 2017) and allows consumers to check test services, e.g. medical IGeL services or Direct2Consumer tests from the Internet, on their reliability.
You can find the "RisikoKompass" app at the Playstore (https://play.google.com/store/apps/details?id=de.mpg.hardingcenter.RisikoKompass).
Empirical evaluation with consumers
All research results on the fundamentals and on the effectiveness of the RiskoAtlas tools in terms of competence enhancement, information search and risk communication will be published together with the project research report on 30 June 2020. If you are interested beforehand, please contact us directly (Felix Rebitschek, email@example.com).
Last update: 27 November 2019.
The image shows decision trees (assistants) from the "Digital World" category.
The image shows decision trees (assistants) from the "finances" category.