Information Anatomy of Stochastic Equilibria

A stochastic nonlinear dynamical system generates information, as measured by its entropy rate. Some—the ephemeral information—is dissipated and some—the bound information—is actively stored and so affects future behavior. We derive analytic expressions for the ephemeral and bound information in the...

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Main Authors: Sarah Marzen, James P. Crutchfield
Format: Article
Language:English
Published: MDPI AG 2014-08-01
Series:Entropy
Subjects:
Online Access:http://www.mdpi.com/1099-4300/16/9/4713
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spelling doaj-131d22e9d8ea402da9ec938d1d1e6e182020-11-25T01:02:53ZengMDPI AGEntropy1099-43002014-08-011694713474810.3390/e16094713e16094713Information Anatomy of Stochastic EquilibriaSarah Marzen0James P. Crutchfield1Department of Physics, University of California at Berkeley, Berkeley, CA 94720, USAComplexity Sciences Center, Department of Physics, University of California at Davis, One Shields Avenue, Davis, CA 95616, USAA stochastic nonlinear dynamical system generates information, as measured by its entropy rate. Some—the ephemeral information—is dissipated and some—the bound information—is actively stored and so affects future behavior. We derive analytic expressions for the ephemeral and bound information in the limit of infinitesimal time discretization for two classical systems that exhibit dynamical equilibria: first-order Langevin equations (i) where the drift is the gradient of an analytic potential function and the diffusion matrix is invertible and (ii) with a linear drift term (Ornstein–Uhlenbeck), but a noninvertible diffusion matrix. In both cases, the bound information is sensitive to the drift and diffusion, while the ephemeral information is sensitive only to the diffusion matrix and not to the drift. Notably, this information anatomy changes discontinuously as any of the diffusion coefficients vanishes, indicating that it is very sensitive to the noise structure. We then calculate the information anatomy of the stochastic cusp catastrophe and of particles diffusing in a heat bath in the overdamped limit, both examples of stochastic gradient descent on a potential landscape. Finally, we use our methods to calculate and compare approximations for the time-local predictive information for adaptive agents.http://www.mdpi.com/1099-4300/16/9/4713Langevin equationentropy rateephemeral informationbound informationtime-local predictive information
collection DOAJ
language English
format Article
sources DOAJ
author Sarah Marzen
James P. Crutchfield
spellingShingle Sarah Marzen
James P. Crutchfield
Information Anatomy of Stochastic Equilibria
Entropy
Langevin equation
entropy rate
ephemeral information
bound information
time-local predictive information
author_facet Sarah Marzen
James P. Crutchfield
author_sort Sarah Marzen
title Information Anatomy of Stochastic Equilibria
title_short Information Anatomy of Stochastic Equilibria
title_full Information Anatomy of Stochastic Equilibria
title_fullStr Information Anatomy of Stochastic Equilibria
title_full_unstemmed Information Anatomy of Stochastic Equilibria
title_sort information anatomy of stochastic equilibria
publisher MDPI AG
series Entropy
issn 1099-4300
publishDate 2014-08-01
description A stochastic nonlinear dynamical system generates information, as measured by its entropy rate. Some—the ephemeral information—is dissipated and some—the bound information—is actively stored and so affects future behavior. We derive analytic expressions for the ephemeral and bound information in the limit of infinitesimal time discretization for two classical systems that exhibit dynamical equilibria: first-order Langevin equations (i) where the drift is the gradient of an analytic potential function and the diffusion matrix is invertible and (ii) with a linear drift term (Ornstein–Uhlenbeck), but a noninvertible diffusion matrix. In both cases, the bound information is sensitive to the drift and diffusion, while the ephemeral information is sensitive only to the diffusion matrix and not to the drift. Notably, this information anatomy changes discontinuously as any of the diffusion coefficients vanishes, indicating that it is very sensitive to the noise structure. We then calculate the information anatomy of the stochastic cusp catastrophe and of particles diffusing in a heat bath in the overdamped limit, both examples of stochastic gradient descent on a potential landscape. Finally, we use our methods to calculate and compare approximations for the time-local predictive information for adaptive agents.
topic Langevin equation
entropy rate
ephemeral information
bound information
time-local predictive information
url http://www.mdpi.com/1099-4300/16/9/4713
work_keys_str_mv AT sarahmarzen informationanatomyofstochasticequilibria
AT jamespcrutchfield informationanatomyofstochasticequilibria
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