用于风险敏感溯因推理的最小κ–τ逻辑
A Minimal $κ$--$τ$ Logic for Risk-Sensitive Abduction
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中文总结 AI 辅助
本文针对风险敏感溯因推理领域，提出含认知交互与承诺阈值的最小κ–τ逻辑框架，其作为神经符号架构的符号管控层，可实现透明可审计的溯因推理。
中文摘要 AI 辅助
标准溯因推理方法可保留多个候选解释，但通常未将明确的组合式跨假设交互与内部的、对竞争敏感的承诺判断相结合。本文指出，在风险敏感领域中，过早承诺会带来不对称的下行成本，承诺的时机本身就是推理装置应正式表征的受管控决策。我们提出一种基于两个原语的最小κ–τ逻辑框架：假设间的认知交互（κ）与规范性承诺阈值（τ）。假设可共存、相互强化或抑制，并形成涌现的复合解释，而向承诺结论的坍缩由管控约束调节，而非仅由推理强制。该逻辑以两种互补模式开发，共享交互关系与管控装置：合成模式中，原子假设向上组合为涌现解释；分析模式中，复杂的观测事态被分解为潜在因素的因果簇，承诺在簇和因素层面均受管控。该框架为“高度可能”与“值得承诺”的区分具有操作重要性的领域提供了形式化工具。κ–τ逻辑被定位为神经符号架构的符号管控层：其认知参数可由神经组件（语义嵌入与生成模型，如现有计算实现所示）自然估计，而其规范性参数仍受明确的人类管控，从而为高风险场景的部署提供透明且可审计的溯因推理。
英文摘要
Standard approaches to abductive reasoning can retain multiple candidate explanations, but they do not generally combine explicit compositional cross-hypothesis interaction with an internal, rival-sensitive commitment judgment. This paper argues that in risk-sensitive domains -- where premature commitment carries asymmetric downside costs -- the timing of commitment is itself a governed decision that the inferential apparatus should formally represent. We present a minimal $κ$--$τ$ logical framework built on two primitives: epistemic interaction among hypotheses ($κ$) and a normative commitment threshold ($τ$). Hypotheses may coexist, reinforce or inhibit one another, and form emergent composite explanations, while collapse into committed conclusions is regulated by governance constraints rather than forced by inference alone. The logic is developed in two complementary modes sharing the interaction relation and the governance apparatus: a synthetic mode, in which atomic hypotheses are composed upward into emergent explanations, and an analytic mode, in which complex observed states of affairs are decomposed into causal clusters of latent factors, with commitment governed at both the cluster and the factor level. The framework provides formal machinery for domains in which the distinction between highly likely and commit-worthy is operationally consequential. The $κ$--$τ$ logic is positioned as the symbolic governance layer of a neurosymbolic architecture: its epistemic parameters are naturally estimated by neural components -- semantic embeddings and generative models, as demonstrated in existing computational realizations -- while its normative parameters remain under explicit human governance, yielding transparent and auditable abductive reasoning for deployment in high-stakes settings.
