# JIDT: An information-theoretic toolkit for studying the dynamics of complex systems

arXiv: https://arxiv.org/abs/1408.3270 · HTML: https://mlsanity.com/paper/1408.3270/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2014.

## Built on by (most cited first, 11 of 11)

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- [Emotional EEG Classification using Connectivity Features and Convolutional Neural Networks](https://mlsanity.com/paper/2101.07069/) (2021)
- [Understanding Concept Identification as Consistent Data Clustering Across Multiple Feature Spaces](https://mlsanity.com/paper/2301.05525/) (2023)
- [Inferring the time-varying coupling of dynamical systems with temporal convolutional autoencoders](https://mlsanity.com/paper/2406.03212/) (2024)
- [Information Theoretic Measures of Causal Influences during Transient Neural Events](https://mlsanity.com/paper/2209.07508/) (2022)
- [An AI Theory of Mind Will Enhance Our Collective Intelligence](https://mlsanity.com/paper/2411.09168/) (2024)
- [The Human Brain as a Combinatorial Complex](https://mlsanity.com/paper/2511.20692/) (2025)
- [Non-Stationarity Breaks Permutation Surrogates in Multi-Agent Reinforcement Learning: Diagnosis and Remedies](https://mlsanity.com/paper/2604.23716/) (2026)

## Abstract (arXiv)

Complex systems are increasingly being viewed as distributed information processing systems, particularly in the domains of computational neuroscience, bioinformatics and Artificial Life. This trend has resulted in a strong uptake in the use of (Shannon) information-theoretic measures to analyse the dynamics of complex systems in these fields. We introduce the Java Information Dynamics Toolkit (JIDT): a Google code project which provides a standalone, (GNU GPL v3 licensed) open-source code implementation for empirical estimation of information-theoretic measures from time-series data. While the toolkit provides classic information-theoretic measures (e.g. entropy, mutual information, conditional mutual information), it ultimately focusses on implementing higher-level measures for information dynamics. That is, JIDT focusses on quantifying information storage, transfer and modification, and the dynamics of these operations in space and time. For this purpose, it includes implementations of the transfer entropy and active information storage, their multivariate extensions and local or pointwise variants. JIDT provides implementations for both discrete and continuous-valued data for each measure, including various types of estimator for continuous data (e.g. Gaussian, box-kernel and Kraskov-Stoegbauer-Grassberger) which can be swapped at run-time due to Java's object-oriented polymorphism. Furthermore, while written in Java, the toolkit can be used directly in MATLAB, GNU Octave, Python and other environments. We present the principles behind the code design, and provide several examples to guide users.