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Understanding, modelling and predicting human risky decision-making ischallenging due to intrinsic individual differences and irrationality. Fuzzytrace theory (FTT) is a powerful paradigm that explains human decision-makingby incorporating gists, i.e., fuzzy representations of information whichcapture only its quintessential meaning. Inspired by Broniatowski and Reyna'sFTT cognitive model, we propose a computational framework which combines theeffects of the underlying semantics and sentiments on text-baseddecision-making. In particular, we introduce Category-2-Vector to learncategorical gists and categorical sentiments, and demonstrate how ourcomputational model can be optimised to predict risky decision-making in groupsand individuals.

From Cognitive to Computational Modeling: Text-based Risky Decision-Making Guided by Fuzzy Trace Theory