Controlled Causal Hallucinations Can Estimate Phantom Nodes in Multiexpert Mixtures of Fuzzy Cognitive Maps

TL;DR

This paper introduces a scalable multiexpert approach using fuzzy cognitive maps to estimate missing causal nodes in large systems, improving the approximation of system equilibria and future trajectories.

Contribution

It presents a novel method for estimating phantom nodes in causal models by mixing expert FCMs through convex combinations and supervised learning.

Findings

Effective estimation of phantom nodes in large-scale causal models.

Improved approximation of system equilibria and trajectories.

Practical approach for handling missing causal information.

Abstract

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.