By Barbara Fasolo – London School of Economics & Political Science (LSE)
Ellen Peters’ article gives us good news: Decision making can be improved. How? By means of decision aids that provide the right information (e.g. tools for smoking cessation), and training (e.g. numeracy training programmes)
I agree with her main conclusion that improving the quality of decision making is not only possible, but necessary, and that decision aids along with instruction are critical. Interventions that increase numeracy are particularly important as they have a positive impact on individual and societal welfare. In one of our studies in collaboration with the King’s Fund in the UK, we found for example that greater numeracy directly correlated with choice of higher quality hospitals (Boyce, Dixon, Fasolo and Reutskaja, 2010). I also agree that decision aids are necessary to provide the right information. As “information technology”, or repositories of smart information, decision aids achieve the goal of informing decision making.
But I think the news is even brighter: in this digital age, decision aids can be more than information technology and serve their primary purpose: to be effective decision technology. The distinction I introduce is between an aid as “information technology” and an aid as “decision technology” (Edwards and Fasolo, 2001). Information technology is basically a one-way channel of information, for instance about risks and consequences. Most apps and tools available are information technology. In contrast, decision technology is a two-way channel that requires input from the decision maker, and provokes the decision maker to find a solution that best fits one’s goal. There are very few of these around, at least which operate according to sound principles of decision science.
This distinction might not matter much in the health domain which Peters considers in her article. The endpoint of a health intervention is clear (to improve one’s health, one’s lung capacity, etc.) and has a single solution (to give up smoking). In cases such as these, a one-way tool that provides information about the consequences of continuing smoking or quitting might be sufficient.
The distinction between information and decision technology is more consequential when the endpoint of an intervention depends heavily on the goals of the decision maker, there are conflicting objectives and it is hard to visualise the consequences of different courses of action – for instance, the choice of a more or less aggressive financial investment or choice of monthly mortgage payment amount. In these contexts, a one-way aid which simply provides information misses the chance to help decision makers experience the implications of different options, and ‘feel’ the best way forward. Instead, a two-way tool that makes people think and interact engages the decision maker and expands decision capability. Take Neil Stewart’s online decision tool. This simple online tool led people to choose what they really wanted: a smaller total repayment amount and shorter debt. What triggered this decision was not the information provided about APR and amount of debt, but the ability to move the slider of the online tool. Interaction unlocked the meaning of the complex inter-relationship between APR and debt, and what it meant for the decision maker at different points in time and for different amounts of money.
How can a decision tool provoke decision capability?
Behavioural science suggests at least two ways.
The first is interactivity. As for the choice of mortgage amount mentioned above, a decision tool must elicit a reaction from the user, for instance in the form of sliding a ruler or clicking an option. The added benefit of this interactivity is that it is likely to improve the motivation of a decision maker to engage throughout a task which might be cognitively demanding. Even physically tiring activities like jumping up to touch a wall become easier to complete when – upon jumping – a touchpad on the wall emits a sound (Hsee, Yang, & Ruan, 2015). This ‘mere reaction’ effect is capitalized by interactive decision aids.
The second is its adaptivity to the numerical or fluency skills of the decision. Low numeracy can challenge understanding and use of numerical information, as Peters’ programme of research has been showing. Our research found that a way around the challenges of aiding decision makers who are low in numeracy is to capitalize on another strength they might have – fluency with words. A tool that asks about preferences in a verbal form is liked more and found more helpful than one that asks about preferences in a numerical form (Fasolo and Bana Costa 2014).
Achieving the right mix of interactivity, adaptivity and simple information is not trivial, and requires careful experimentation because any feature present on a screen can unintendedly influence decision making (Shen and Hsee, 2017). But this research is well worth it. Decision-capability-provoking apps and aids could well be our only resort for reaching and helping many decision makers we care about, such as people living in disadvantaged areas where no education programme can be offered to all households, but access to a smart device can. Decision-capability-provoking tools can also offer opportunities to grow decision literacy among the digital natives – for instance the children and teenagers who have a natural way with screens and digital platforms, and are fortunate to have access to them, at school or home.
Rethinking the purpose of a decision aid in our digital age gives us the chance to rethink also the concept of “decision capability”. Decision capability, as we see it, is the ability to make effective decisions appropriate to the context, when relevant structures, processes and tools are employed by the decision makers (Ni & Fasolo 2017). Fortunately, what distinguishes us from non-human animals is the innate ability to utilize tools – and this has accelerated in the digital age.
Let’s not miss the chance that this digital age, more than any previous time, gives us: to design, to use and to disseminate tools that provoke decision capability.
References
Boyce, T., Dixon, A., Fasolo, B. and Reutskaja, E. (2010). Choosing a high-quality hospital: the role of nudges, scorecard design and information. The King’s Fund, London, UK. ISBN 9781857176032.
Edwards, W and Fasolo, B (2001). Decision technology. Annual Review of Psychology, 52. 581-606.
Fasolo, B and Bana-Costa, C (2014). Tailoring value elicitation to decision makers’ numeracy and fluency: expressing value judgments in numbers or words. Omega: The International Journal of Management Science, 44. pp. 83-90.
Hsee, C. K., Yang, Y., & Ruan, B. (2015). The mere reaction effect: Even non-positive and non-informative reactions can reinforce actions. Journal of Consumer Research, 42(3), 420-43.
Shen, L. & Hsee, C. K. (2017). Numerical nudging with an accelerating nonsense number. Psychological Science.
Ni, Z. and Fasolo, B. (2017). Decision Capability. LSE Working Paper 13.155.
Stewart, N. http://www.stewart.psych.warwick.ac.uk/decisiontool/