让AI与人协作时，能清晰展示推理过程中的各种可能解释。
Analytic Abduction: Causal Decomposition and Governed Commitment for Human--AI Coordination
- 用$κ$-$τ$机制动态管理假设间的互动与结论承诺条件。
- 通过因果簇结构记录因子参与程度和相互作用，避免误判因果关系。
- 适合需在不确定中做决策的场景，如疫情分析或网络安全应对。
归纳推理分为两种：合成式从已有假设构建解释，分析式则反向追溯复杂现象背后的潜在因素。本文发展了分析式推理作为非贪婪、风险敏感的承诺机制，其中候选因素并存互动，仅当满足明确治理条件时才形成确定结论。核心是$κ$-$τ$框架：$κ$编码假设间的认知交互，$τ$设置与决策风险匹配的承诺阈值。关键贡献是因果簇结构，它记录潜因在分解中的参与度、权重及交互模式，并采用双层架构（簇内$κ^*$，簇间$κ^{**}$）防止因果误判。在流行病危机分析与对抗性网络威胁检测中验证，该框架使人类与AI共享未定论的推理过程，提供结构性抗过早收敛的能力。实际应用中，决策者获得多个竞争性解释，按可信度加权，并配以可区分证据，即使在模糊状态也可做出合理行动。
原文摘要 · Abstract (English)
Abductive reasoning operates in two directions. The synthetic mode builds explanations from available hypotheses; the analytic mode, conversely, identifies the latent factors whose interaction accounts for a complex observed state. This paper develops the analytic mode as a non-greedy, risk-sensitive discipline of commitment, in which candidate factors coexist and interact, resolving into committed conclusions only when explicit governance conditions are met. The formal core is the $κ$-$τ$ apparatus: $κ$ encodes the epistemic interaction among hypotheses, and $τ$ sets a commitment threshold calibrated to the decision's stakes. The central contribution is the causal cluster, a structured object recording which latent factors participate in a decomposition, with what weights and interaction structure, together with a two-level architecture (intra-cluster $κ^*$, inter-cluster $κ^{**}$) that guards against causal misattribution. Demonstrated in epidemiological crisis decomposition and adversarial cyber threat analysis, the framework's contribution to human-AI reasoning is the legibility of suspended decomposition as a shared coordination object, providing structural resistance to premature convergence. In practice, the decision-maker is handed not a single imposed answer but the competing explanatory scenarios, weighted by plausibility and paired with the evidence that would resolve between them, so that sound action is possible even before the ambiguity is resolved.
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