Complexity Measures from Interaction Structures

Abstract

We evaluate new complexity measures on the symbolic dynamics of coupled tent maps and cellular automata. These measures quantify complexity in terms of $k$-th order statistical dependencies that cannot be reduced to interactions between $k-1$ units. We demonstrate that these measures are able to identify complex dynamical regimes.

11 pages, figures improved, minor changes to the text

References in corpus (2)

• Maximizing Multi-Information
• Symbolic Synchronization and the Detection of Global Properties of Coupled Dynamics from Local Information

Cited by in corpus (20)

• A Bivariate Measure of Redundant Information
• Towards a Synergy-based Approach to Measuring Information Modification
• Information geometric methods for complexity
• Quantifying genuine multipartite correlations and their pattern complexity
• Computing complexity measures for quantum states based on exponential families
• Neighborliness of Marginal Polytopes
• STDP-driven networks and the \emph{C. elegans} neuronal network
• Maximizing the divergence from a hierarchical model of quantum states
• Complexity measures, emergence, and multiparticle correlations
• Quantifying structure in networks
• Aging and Reliability of Quantum Networks
• Decomposing information into copying versus transformation
• Synergy, Redundancy and Common Information
• Information Geometric Complexity of Entropic Motion on Curved Statistical Manifolds under Different Metrizations of Probability Spaces
• Statistical physics of exchangeable sparse simple networks, multiplex networks and simplicial complexes
• Algebraic geometry of Poisson regression
• Weaving and neural complexity in symmetric quantum states
• Analysing multiparticle quantum states
• Cooperation and dependencies in multipartite systems
• Classical Hierarchical Correlation Quantification on Tripartite Qubit Mixed State Families