Expertise is a key determining factor in design and technical decision-making. Leading theories of decision-making, and especially decision under risk, assume that choices are based on precise quantitative processing of tradeoffs. In fact, decisions made by technical experts are frequently based on qualitative “gist” processing – a highly-informed, yet simplified, representation of complex decision scenarios that captures their essential meaning in context. Most leading computational theories of decision-making do not have mechanisms to account for the incorporation of expertise, cultural, or interpretive factors. Therefore, they are of limited utility to scholars and practitioners who wish to use the output of computational analysis products to inform design decisions in a manner that is consistent with gist-based expertise. We developed the first mathematical formalization of Fuzzy-Trace Theory – a leading account of decision under risk and decision by experts – to take this qualitative processing into account, enabling explanation of several classical and novel experimental results, and building towards the explicit incorporation of models of expert decision-making that may be used to inform the design of decision support systems for technical fields. Specifically, I conducted a systematic review of core phenomena of risky decision-making, and successfully predicted 82 of 88 (93%) studies in the literature. I have built upon this body of work to apply this theory to the critical problem of combating antibiotic resistance – using FTT to explain decision-making by patients and expert healthcare providers with direct implications for educational interventions to reduce antibiotic resistance. Ongoing work is focused on extending these theories to inform decision-making by the intelligence community and expert engineers.
Research on this project is supported in part by the National Institute of General Medical Sciences of the National Institutes of Health under award number R01GM114771, and by the Defense Advanced Research Projects Agency (DARPA) under the Data-Driven Discovery of Models (D3M) program. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or DARPA.