发表机构
Stake Lab, University of Molise（莫利塞大学斯塔克实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究分析性溯因推理，核心方法是κ - τ 机制及因果簇，贡献在于将未确定分解作为共享协调对象，用于人机协作，为决策者提供竞争解释场景及证据，助力合理行动。
AI 中文摘要
溯因推理有两种模式。合成模式根据现有假设构建解释；分析模式则相反，识别那些相互作用导致复杂观察状态的潜在因素。本文将分析模式发展为一种非贪婪、风险敏感的承诺规则，候选因素共存并相互作用，只有在满足明确治理条件时才得出确定结论。形式核心是κ - τ 机制，κ 编码假设间的认知交互，τ 设置根据决策风险校准的承诺阈值。核心贡献是因果簇，记录潜在因素如何参与分解及其权重和交互结构。在流行病学危机分解和对抗性网络威胁分析中得到验证，该框架对人机推理的贡献在于，将未确定的分解作为共享协调对象，提供结构上的抗过早收敛能力。在实践中，决策者得到的不是单一答案，而是相互竞争的解释场景，并根据合理性加权，同时伴有能在它们之间做出抉择的证据，从而即使在模糊性未解决之前也能采取合理行动。
英文摘要
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.
