Nudge & Boost Agents: Designing Ambient Intelligent Systems to Effectively Influence Human Decision Making

Behavioral economics has revealed that human decision making is not rational and logical as previously thought. It is strongly dependent on the environment and context, and is influenced by various biases. In this paper, we focus on the type of information technology and findings that can be used to construct such environments and contexts to improve decision making. An additional objective is to help humans change their behavior to adapt to the context. For this purpose, the design of ambient intelligent systems is proposed in this study. Specifically, by integrating the concepts of "nudge" and "boost" in behavioral economics, we propose a method to design "nudge & boost agents" that encourage users to change their behavior to adapt to a situation (nudge function) and foster individual decision-making skills (boost function). The nudge and boost agents are implemented on the basis of ambient intelligent systems based on previous human-agent interaction (HAI) research. In this paper, we describe the concept of these agents and the implementation of the system. Furthermore, based on the evaluation results, the effectiveness of the system is clarified.