Analytic Abduction: Causal Decomposition and Governed Commitment for Human--AI Coordination

2026-07-16

AI Summary

This work addresses the challenges of premature convergence and insufficient interpretability in human-AI collaboration under complex observations by proposing a non-greedy, risk-sensitive abductive reasoning framework. The approach leverages causal cluster structures and a dual-level κ-architecture (κ* and κ**) to enable accurate causal decomposition while avoiding misattribution. A novel κ–τ mechanism is introduced, wherein κ models cognitive interactions among competing hypotheses and τ dynamically adjusts commitment thresholds based on decision risk. Undetermined decompositions serve as shareable coordination artifacts to enhance transparency. Empirical validation in epidemic crisis and adversarial cyber threat scenarios demonstrates that the method generates multiple coexisting, evidence-supported explanatory pathways, thereby facilitating robust decision-making under ambiguity.

Abstract

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.

Problem

• analytic abduction
• causal decomposition
• human-AI coordination
• commitment
• causal misattribution

Innovation

• analytic abduction
• causal cluster
• kappa-tau apparatus
• risk-sensitive commitment
• human-AI coordination