Failure of LMC statistical complexity in identifying structural order in the XY model

TL;DR

This paper critically evaluates the LMC statistical complexity measure in the 2D XY model, revealing its failure to accurately identify structurally complex states during system relaxation.

Contribution

The study demonstrates the limitations of LMC complexity in physical systems and suggests incorporating dynamical sensitivity for better complexity quantification.

Findings

LMC complexity often peaks at near-equilibrium states

It underestimates vortex-rich intermediate configurations

The time derivative of LMC provides more dynamical insight

Abstract

Quantifying complexity in physical systems remains a fundamental challenge, and many proposed measures fail to capture the structural features that intuitive or theoretical considerations would demand. Among them, the Lopez-Ruiz-Mancini-Calbet (LMC) statistical complexity has been widely cited due to its simplicity and analytic tractability. Here, we examine the performance and limitations of the LMC measure in a controlled physical setting: a two-dimensional XY model studied through Monte Carlo simulations. By computing LMC complexity at each step of the system's relaxation dynamics, and directly comparing these values with the evolving dipole configurations, we show that LMC complexity systematically fails to identify states of high structural complexity. In particular, the measure often assigns maximal complexity to nearly equilibrated configurations while underestimating vortex-rich intermediate states. We further show that the time derivative of LMC complexity contains more meaningful dynamical information. We propose that future measures incorporate directionality and dynamical sensitivity to better reflect the emergence and decay of organization in nonequilibrium systems.