160 citations · 817 across the 25 of their papers we have counts for
26 papers
Modes of Information Flow in Collective Cohesion
Sulimon Sattari, Udoy S. Basak, Ryan G. James +2
Pairwise interactions between individuals are taken as fundamental drivers of collective behavior responsible for group cohesion and decision-making. While an individual directly i…
Correlated structural evolution within multiplex networks
Haochen Wu, Ryan G. James, James P. Crutchfield +1
Many natural, engineered, and social systems can be represented using the framework of a layered network, where each layer captures a different type of interaction between the same…
Unique Information and Secret Key Agreement
Ryan G. James, Jeffrey Emenheiser, James P. Crutchfield
The partial information decomposition (PID) is a promising framework for decomposing a joint random variable into the amount of influence each source variable Xi has on a target va…
A Perspective on Unique Information: Directionality, Intuitions, and Secret Key Agreement
Ryan G. James, Jeffrey Emenheiser, James P. Crutchfield
Recently, the partial information decomposition emerged as a promising framework for identifying the meaningful components of the information contained in a joint distribution. Its…
Modes of Information Flow
Ryan G. James, Blanca Daniella Mansante Ayala, Bahti Zakirov +1
Information flow between components of a system takes many forms and is key to understanding the organization and functioning of large-scale, complex systems. We demonstrate three…
Causal Scale Shift Associated with Phase Transition to Human Atrial Fibrillation
Hiroshi Ashikaga, Konstantinos N. Aronis, Susumu Tao +1
An example of phase transition in natural complex systems is the qualitative and sudden change in the heart rhythm between sinus rhythm and atrial fibrillation (AF), the most commo…
Inter-Scale Information Flow as a Surrogate for Downward Causation That Maintains Spiral Waves
Hiroshi Ashikaga, Ryan G. James
The mechanism that maintains atrial fibrillation (AF) remains elusive. One approach to understanding and controlling the mechanism ("AF driver") is to quantify inter-scale informat…
Unique Information via Dependency Constraints
Ryan G. James, Jeffrey Emenheiser, James P. Crutchfield
The partial information decomposition (PID) is perhaps the leading proposal for resolving information shared between a set of sources and a target into redundant, synergistic, and…
Prediction and Generation of Binary Markov Processes: Can a Finite-State Fox Catch a Markov Mouse?
J. Ruebeck, R. G. James, J. R. Mahoney +1
Understanding the generative mechanism of a natural system is a vital component of the scientific method. Here, we investigate one of the fundamental steps toward this goal by pres…
Trimming the Independent Fat: Sufficient Statistics, Mutual Information, and Predictability from Effective Channel States
Ryan G. James, John R. Mahoney, James P. Crutchfield
One of the most fundamental questions one can ask about a pair of random variables X and Y is the value of their mutual information. Unfortunately, this task is often stymied by th…
Multivariate Dependence Beyond Shannon Information
Ryan G. James, James P. Crutchfield
Accurately determining dependency structure is critical to discovering a system's causal organization. We recently showed that the transfer entropy fails in a key aspect of this---…
Hidden Structures of Information Transport Underlying Spiral Wave Dynamics
Hiroshi Ashikaga, Ryan G. James
A spiral wave is a macroscopic dynamic of excitable media that plays an important role in several distinct systems, including the Belousov-Zhabotinsky reaction, seizures in the bra…
Information Flows? A Critique of Transfer Entropies
Ryan G. James, Nix Barnett, James P. Crutchfield
A central task in analyzing complex dynamics is to determine the loci of information storage and the communication topology of information flows within a system. Over the last deca…
A new method for choosing parameters in delay reconstruction-based forecast strategies
Joshua Garland, Ryan G. James, Elizabeth Bradley
Delay-coordinate reconstruction is a proven modeling strategy for building effective forecasts of nonlinear time series. The first step in this process is the estimation of good va…
Anatomy of a Spin: The Information-Theoretic Structure of Classical Spin Systems
V. S. Vijayaraghavan, R. G. James, J. P. Crutchfield
Collective organization in matter plays a significant role in its expressed physical properties. Typically, it is detected via an order parameter, appropriately defined for each gi…
The Elusive Present: Hidden Past and Future Dependency and Why We Build Models
Pooneh M. Ara, Ryan G. James, James P. Crutchfield
Modeling a temporal process as if it is Markovian assumes the present encodes all of the process's history. When this occurs, the present captures all of the dependency between pas…
Understanding and Designing Complex Systems: Response to "A framework for optimal high-level descriptions in science and engineering---preliminary report"
James P. Crutchfield, Ryan G. James, Sarah Marzen +1
We recount recent history behind building compact models of nonlinear, complex processes and identifying their relevant macroscopic patterns or "macrostates". We give a synopsis of…
Model-free quantification of time-series predictability
Joshua Garland, Ryan James, Elizabeth Bradley
This paper provides insight into when, why, and how forecast strategies fail when they are applied to complicated time series. We conjecture that the inherent complexity of real-wo…
Intersection Information based on Common Randomness
Virgil Griffith, Edwin K. P. Chong, Ryan G. James +2
The introduction of the partial information decomposition generated a flurry of proposals for defining an intersection information that quantifies how much of "the same information…
Chaos Forgets and Remembers: Measuring Information Creation, Destruction, and Storage
Ryan G. James, Korana Burke, James P. Crutchfield
The hallmark of deterministic chaos is that it creates information---the rate being given by the Kolmogorov-Sinai metric entropy. Since its introduction half a century ago, the met…
Determinism, Complexity, and Predictability in Computer Performance
Joshua Garland, Ryan James, Elizabeth Bradley
Computers are deterministic dynamical systems (CHAOS 19:033124, 2009). Among other things, that implies that one should be able to use deterministic forecast rules to predict their…
How Hidden are Hidden Processes? A Primer on Crypticity and Entropy Convergence
John R. Mahoney, Christopher J. Ellison, Ryan G. James +1
We investigate a stationary process's crypticity---a measure of the difference between its hidden state information and its observed information---using the causal states of comput…
Information Symmetries in Irreversible Processes
Christopher J. Ellison, John R. Mahoney, Ryan G. James +2
We study dynamical reversibility in stationary stochastic processes from an information theoretic perspective. Extending earlier work on the reversibility of Markov chains, we focu…
Anatomy of a Bit: Information in a Time Series Observation
Ryan G. James, Christopher J. Ellison, James P. Crutchfield
Appealing to several multivariate information measures---some familiar, some new here---we analyze the information embedded in discrete-valued stochastic time series. We dissect th…