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

2024-12-31

AI Summary

In large-scale causal modeling, missing “phantom nodes”—unobserved or latent causal variables—lead to inaccurate system behavior representation. Method: This paper proposes a multi-expert fuzzy cognitive map (FCM) hybrid framework that, for the first time, formalizes causal hallucination as a controllable estimation mechanism and introduces a phantom-node adaptive learning paradigm supervised by equilibrium-bias regularization. The approach integrates multi-expert convex combination, feedback-driven dynamical system modeling, and limit-cycle equilibrium estimation to enable scalable causal inference under large knowledge bases. Contributions/Results: Experiments demonstrate significant improvements in phantom-node estimation accuracy and convergence speed; achieve 92.7% equilibrium trajectory approximation accuracy on multivariate chaotic systems; and reduce computational overhead by approximately 40%. This work establishes a novel, robust paradigm for modeling missing causal structures in complex systems.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Complex Systems
• Knowledge Integration
• Predictive Modeling

Innovation

• Multi-expert Feedback Causal Model
• Fuzzy Cognitive Maps
• Prediction Accuracy Enhancement