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.- Anthology ID:
- 2022.findings-naacl.30
- Volume:
- Findings of the Association for Computational Linguistics: NAACL 2022
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 391–409
- Language:
- URL:
- https://aclanthology.org/2022.findings-naacl.30
- DOI:
- 10.18653/v1/2022.findings-naacl.30
- Cite (ACL):
- Jaron Mar and Jiamou Liu. 2022. From Cognitive to Computational Modeling: Text-based Risky Decision-Making Guided by Fuzzy Trace Theory. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 391–409, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- From Cognitive to Computational Modeling: Text-based Risky Decision-Making Guided by Fuzzy Trace Theory (Mar & Liu, Findings 2022)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2022.findings-naacl.30.pdf