用模糊数学建模智慧决策，让AI学会在不确定中保持谦逊判断。
Modeling Wise Decision Making: A Z-Number Fuzzy Framework Inspired by Phronesis
- 基于Z数构建多维度智慧评分系统，融合判断力与信心度。
- 实验显示模型得分与传统量表有显著关联，且不与无关特质混淆。
- 适合心理学测量改进与可解释性人工智能研究者参考。
智慧是一种包含视角采纳、反思性、利他倾向、共情行动及认知谦逊的高层次心理特质。与依赖二元思维的传统推理模型不同，智慧体现于模糊性中的动态平衡，需兼顾分级评估与自我反思的谦逊。现有测量多依赖自评，难以反映智慧判断中的不确定性与谦逊特质。本文提出一种基于Z数的模糊推理框架，将每个决策表示为智慧分（限制）和信心分（确定性）。研究招募100名参与者，通过文化中立的图像道德困境任务生成思考语言，映射至五个理论上的智慧维度。各维度分数通过21条规则组合，隶属函数采用高斯核密度估计调优。结果显示，该系统生成的双重属性智慧表征与已有量表呈适度但显著正相关，且与无关特质无关，支持收敛效度与区分效度。本工作将智慧形式化为多维且具不确定性意识的构造，以Z数实现，不仅推进心理学测量发展，更使AI具备可解释、信心敏感的推理能力，在严谨计算与人类判断间建立安全中间地带。
原文摘要 · Abstract (English)
Background: Wisdom is a superordinate construct that embraces perspective taking, reflectiveness, prosocial orientation, reflective empathetic action, and intellectual humility. Unlike conventional models of reasoning that are rigidly bound by binary thinking, wisdom unfolds in shades of ambiguity, requiring both graded evaluation and self-reflective humility. Current measures depend on self-reports and seldom reflect the humility and uncertainty inherent in wise reasoning. A computational framework that takes into account both multidimensionality and confidence has the potential to improve psychological science and allow humane AI. Method: We present a fuzzy inference system with Z numbers, each of the decisions being expressed in terms of a wisdom score (restriction) and confidence score (certainty). As part of this study, participants (N = 100) were exposed to culturally neutral pictorial moral dilemma tasks to which they generated think-aloud linguistic responses, which were mapped into five theoretically based components of wisdom. The scores of each individual component were combined using a base of 21 rules, with membership functions tuned via Gaussian kernel density estimation. Results: In a proof of concept study, the system produced dual attribute wisdom representations that correlated modestly but significantly with established scales while showing negligible relations with unrelated traits, supporting convergent and divergent validity. Contribution: The contribution is to formalize wisdom as a multidimensional, uncertainty-conscious construct, operationalized in the form of Z-numbers. In addition to progressing measurement in psychology, it calculates how fuzzy Z numbers can provide AI systems with interpretable, confidence-sensitive reasoning that affords a safe, middle ground between rigorous computation and human-like judgment.
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