Seeing Through Risk: A Symbolic Approximation of Prospect Theory
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910915209723904 |
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| author | Yousaf, Ali Arslan Rehman, Umair Danish, Muhammad Umair |
| author_facet | Yousaf, Ali Arslan Rehman, Umair Danish, Muhammad Umair |
| contents | We propose a novel symbolic modeling framework for decision-making under risk that merges interpretability with the core insights of Prospect Theory. Our approach replaces opaque utility curves and probability weighting functions with transparent, effect-size-guided features. We mathematically formalize the method, demonstrate its ability to replicate well-known framing and loss-aversion phenomena, and provide an end-to-end empirical validation on synthetic datasets. The resulting model achieves competitive predictive performance while yielding clear coefficients mapped onto psychological constructs, making it suitable for applications ranging from AI safety to economic policy analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_14448 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Seeing Through Risk: A Symbolic Approximation of Prospect Theory Yousaf, Ali Arslan Rehman, Umair Danish, Muhammad Umair Artificial Intelligence Machine Learning Optimization and Control Methodology We propose a novel symbolic modeling framework for decision-making under risk that merges interpretability with the core insights of Prospect Theory. Our approach replaces opaque utility curves and probability weighting functions with transparent, effect-size-guided features. We mathematically formalize the method, demonstrate its ability to replicate well-known framing and loss-aversion phenomena, and provide an end-to-end empirical validation on synthetic datasets. The resulting model achieves competitive predictive performance while yielding clear coefficients mapped onto psychological constructs, making it suitable for applications ranging from AI safety to economic policy analysis. |
| title | Seeing Through Risk: A Symbolic Approximation of Prospect Theory |
| topic | Artificial Intelligence Machine Learning Optimization and Control Methodology |
| url | https://arxiv.org/abs/2504.14448 |