The Artificial Facilitator: Guiding Participants in Developing Causal Maps Using Voice-Activated Technologies

Abstract

Complex problems often require coordinated actions from stakeholders. Agreeing on a course of action can be challenging as stakeholders have different views or ‘mental models’ of how a problem is shaped by many interacting causes. Participatory modeling allows to externalize mental models in forms such as causal maps. Participants can be guided by a trained facilitator (with limitations of costs and availability) or use a free software (with limited guidance). Neither solution easily copes with large causal maps, for instance by preventing redundant concepts. In this paper, we leveraged voice-activated virtual assistants to create causal models at any time, without costs, and by avoiding redundant concepts. Our three case studies demonstrated that our artificial facilitator could create causal maps similar to previous studies. However, it is limited by current technologies to identify concepts when the user speaks (i.e. entities), and its design had to follow pre-specified rules in the absence of sufficient data to generate rules by discriminative machine-learned methods.