# Seeing Through Risk: A Symbolic Approximation of Prospect Theory

arXiv: https://arxiv.org/abs/2504.14448 · HTML: https://mlsanity.com/paper/2504.14448/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 20 Apr 2025.

## Builds on (most cited first, 6 of 6)

- [Interpretable Scientific Discovery with Symbolic Regression: A Review](https://mlsanity.com/paper/2211.10873/) (2022)
- [Interpretable Neural-Symbolic Concept Reasoning](https://mlsanity.com/paper/2304.14068/) (2023)
- [Foundations of Symbolic Languages for Model Interpretability](https://mlsanity.com/paper/2110.02376/) (2021)
- [The Cognitive Revolution in Interpretability: From Explaining Behavior to Interpreting Representations and Algorithms](https://mlsanity.com/paper/2408.05859/) (2024)
- [Neuro-Symbolic AI: Explainability, Challenges, and Future Trends](https://mlsanity.com/paper/2411.04383/) (2024)
- [Graph Attention Convolutional U-NET: A Semantic Segmentation Model for Identifying Flooded Areas](https://mlsanity.com/paper/2502.15907/) (2025)

## Abstract (arXiv)

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.