Extending the Choice Architecture Toolbox: The Choice Context Exploration

Abstract

The importance of context in behavioral interventions is undeniable, yet few intervention studies begin with a systematic investigation of the contextual factors that influence the behavior in question. This is largely due to the lack of a reliable method for doing so. In recognition of this gap in the field, we have developed a procedure called the Choice Context Exploration that uses machine learning tools to examine the contextual factors that influence a targeted behavior. We demonstrate the steps of Choice Context Exploration using the example of the behavioral choice between using stairs or an elevator. Potential contextual factors were identified by laypeople and experts, and two surveys were created to measure both the behavior and choice, as well as the beliefs of participants. We estimated the effect of contextual factors on participants’ behavior and were able to identify the most influential ones in relation to the studied choice. We achieved an accurate prediction of whether participants would choose the stairs or the elevator based on contextual information in 91.43% of cases on previously unseen data. We also found that participants had different beliefs about what influenced their choice in this situation and that they could be divided into different groups based on these beliefs. Our results suggest that the Choice Context Exploration is a useful procedure for collecting and assessing contextual factors in a given choice setting, which can aid in the planning of behavioral interventions by significantly reducing the number of potential interventions that are likely to be effective.

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