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Navigating the ever-increasing complexity of our daily lives as individuals and as members of collective societies is a primary concern of our time. It is a day-in-day-out challenge to figure out whom to trust, where to move, what to invest in, and even what to buy in the supermarket because all of these decisions require thinking with and about concepts such as emergence, feedback loops, and uncertainty. However, the very properties that make these systems difficult to think about also make it harder for researchers to expose people’s reasoning processes. We are inheriting a rich literature on which kinds of phenomena or concepts are hard to wrap our heads around, but we still do not know much about how people think about complexity intuitively.
In my dissertation study, I am developing and testing a new clinical interviewing methodology called agent-based-construction interviews. In an a-b-c interview, the interviewer and the interviewee collaborate on creating a dynamic computational model; the interviewee provides the ideas, and the interviewer writes the corresponding code in the NetLogo agent-based modeling environment. In other words, the interviewer probes the interviewee’s thinking by writing simple pieces of code in addition to asking clarifying questions. Theorizing about the interviewee’s mental model happens on-the-fly by writing code, not only after the fact during the analysis process.
I am currently wrapping up my data analysis using qualitative coding and natural language processing (NLP).
Aslan, U., & Wilensky, U. (2018). Agent-based Construction (a-b-c) interviews: A generative case study. Proceedings of the Constructionism 2018 Conference. Vilnius, Lithuania.
Aslan, U., & Wilensky, U. (2016, June 5-7). Old Tricks Revisited: Studying Probabilistic Reasoning through Incorporating Computer Modeling into Piagetian Research. Paper presented at the 46th Annual Meeting of the Jean Piaget Society. Chicago, IL, United States.