Seeing Through Risk: A Symbolic Approximation of Prospect Theory
This work addresses the need for interpretable models in AI safety and economic policy analysis, though it is incremental as it builds on existing Prospect Theory.
The paper tackled decision-making under risk by proposing a symbolic modeling framework that merges interpretability with Prospect Theory insights, achieving competitive predictive performance with clear coefficients mapped onto psychological constructs.
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.