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

TL;DR

This paper introduces a computational framework based on Fuzzy Trace Theory that models and predicts human risky decision-making from text by capturing essential meanings and sentiments, accounting for individual differences.

Contribution

It presents a novel computational model that integrates semantic and sentiment analysis using Category-2-Vector to improve prediction of risky decisions from text.

Findings

Model effectively predicts risky decisions in groups and individuals.

Incorporates fuzzy gist representations to capture decision-making nuances.

Demonstrates the importance of semantics and sentiments in text-based risk prediction.

Abstract

Understanding, modelling and predicting human risky decision-making is challenging due to intrinsic individual differences and irrationality. Fuzzy trace theory (FTT) is a powerful paradigm that explains human decision-making by incorporating gists, i.e., fuzzy representations of information which capture only its quintessential meaning. Inspired by Broniatowski and Reyna's FTT cognitive model, we propose a computational framework which combines the effects of the underlying semantics and sentiments on text-based decision-making. In particular, we introduce Category-2-Vector to learn categorical gists and categorical sentiments, and demonstrate how our computational model can be optimised to predict risky decision-making in groups and individuals.