通过可控因果幻觉，用多专家模型估算大规模因果系统中的缺失节点。
Controlled Causal Hallucinations Can Estimate Phantom Nodes in Multiexpert Mixtures of Fuzzy Cognitive Maps
- 用模糊认知图混合建模，通过专家协作补全缺失因果变量。
- 在动力系统中成功逼近多个极限环平衡点，提升对系统轨迹的预测能力。
- 适合研究复杂系统建模与缺失信息推断的学者使用。
一种自适应的多专家反馈因果模型可近似大规模因果模型中的缺失或幻象节点，形成可扩展的‘大知识’形式。该混合模型通过近似采样动力系统的主极限环平衡点来实现。每个专家先生成一个至少缺失一个因果节点的模糊认知图（FCM），FCM为有向带符号的部分因果循环图。这些FCM通过凸组合自然融合，生成新的因果反馈FCM。监督学习使各专家FCM能通过对比其部分平衡态与完整多节点平衡态，估计出幻象节点。这种节点估计实现了对因果幻觉的部分控制，并有助于近似动态系统的未来轨迹。尽管近似过程计算量较大，但调优后的专家FCM混合可有效发现多个幻象节点，从而更准确地逼近反馈系统的实际平衡行为。
原文摘要 · Abstract (English)
An adaptive multiexpert mixture of feedback causal models can approximate missing or phantom nodes in large-scale causal models. The result gives a scalable form of \emph{big knowledge}. The mixed model approximates a sampled dynamical system by approximating its main limit-cycle equilibria. Each expert first draws a fuzzy cognitive map (FCM) with at least one missing causal node or variable. FCMs are directed signed partial-causality cyclic graphs. They mix naturally through convex combination to produce a new causal feedback FCM. Supervised learning helps each expert FCM estimate its phantom node by comparing the FCM's partial equilibrium with the complete multi-node equilibrium. Such phantom-node estimation allows partial control over these causal hallucinations and helps approximate the future trajectory of the dynamical system. But the approximation can be computationally heavy. Mixing the tuned expert FCMs gives a practical way to find several phantom nodes and thereby better approximate the feedback system's true equilibrium behavior.
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