用可解释符号模型模拟风险决策，还原损失厌恶等心理现象
Seeing Through Risk: A Symbolic Approximation of Prospect Theory
- 用效应量指导的符号特征替代黑箱函数
- 在合成数据上复现框架效应和损失厌恶
- 系数对应心理概念，适合安全与政策分析
我们提出一种新的风险决策符号建模框架，融合可解释性与前景理论核心洞见。该方法用透明、效应量引导的特征取代模糊的效用曲线和概率加权函数。数学形式化了该方法，验证其能复现典型的框架效应与损失厌恶现象，并在合成数据上完成端到端实证。模型在保持竞争预测性能的同时，输出可映射至心理构念的清晰系数，适用于人工智能安全与经济政策分析等场景。
原文摘要 · Abstract (English)
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.
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