Inferring Kernel $\epsilon$-Machines: Discovering Structure in Complex Systems

TL;DR

This paper extends a kernel-based method to infer causal structures in complex systems, demonstrating its effectiveness across diverse examples from simple physics to ecological data.

Contribution

It introduces causal diffusion components to the kernel causal-state method, enabling explicit extraction of predictive features from various types of observational data.

Findings

Successfully applied to simple pendulum, n-butane dynamics, sunspot data, and crop field observations.

Robustly discovers predictive structures across systems with different dimensions and stochasticity.

Demonstrates broad applicability of the kernel causal-states algorithm.

Abstract

Previously, we showed that computational mechanic's causal states -- predictively-equivalent trajectory classes for a stochastic dynamical system -- can be cast into a reproducing kernel Hilbert space. The result is a widely-applicable method that infers causal structure directly from very different kinds of observations and systems. Here, we expand this method to explicitly introduce the causal diffusion components it produces. These encode the kernel causal-state estimates as a set of coordinates in a reduced dimension space. We show how each component extracts predictive features from data and demonstrate their application on four examples: first, a simple pendulum -- an exactly solvable system; second, a molecular-dynamic trajectory of $n$-butane -- a high-dimensional system with a well-studied energy landscape; third, the monthly sunspot sequence -- the longest-running available time series of direct observations; and fourth, multi-year observations of an active crop field -- a set of heterogeneous observations of the same ecosystem taken for over a decade. In this way, we demonstrate that the empirical kernel causal-states algorithm robustly discovers predictive structures for systems with widely varying dimensionality and stochasticity.