Randomness, Structure and Causality - Agenda

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Effective Complexity of Stationary Process Realizations

Ay, Nihat (
SFI & Max Planck Institute

Links: [[1]]

Learning Out of Equilibrium
Bell, Tony (
UC Berkeley


The Transmission of Sense Information

Bergstrom, Carl (
SFI & University of Washington

Links: [[2]]

Optimizing Information Flow in Small Genetic Networks

Bialek, William (wbialek@Princeton.EDU)
Princeton University

Links: [[3]]

To a Mathematical Theory of Evolution and Biological Creativity

Chaitin, Gregory (
IBM Watson Research Center

We present an information-theoretic analysis of Darwin’s theory of evolution, modeled as a hill-climbing algorithm on a fitness landscape. Our space of possible organisms consists of computer programs, which are subjected to random mutations. We study the random walk of increasing fitness made by a single mutating organism. In two different models we are able to show that evolution will occur and to characterize the rate of evolutionary progress, i.e., the rate of biological creativity.

Links: File:Darwin.pdf

Framing Complexity

Crutchfield, James (
SFI & UC Davis

Is there a theory of complex systems? And who should care, anyway?

Links: [[4]]

The Vocabulary of Grammar-Based Codes and the Logical Consistency of Texts

Debowski, Lukasz (
Polish Academy of Sciences

We will present a new explanation for the distribution of words in natural language which is grounded in information theory and inspired by recent research in excess entropy. Namely, we will demonstrate a theorem with the following informal statement: If a text of length describes independent facts in a repetitive way then the text contains at least different words.  In the formal statement, two modeling postulates are adopted. Firstly, the words are understood as nonterminal symbols of the shortest grammar-based encoding of the text. Secondly, the text is assumed to be emitted by a finite-energy strongly nonergodic source whereas the facts are binary IID variables predictable in a shift-invariant way. Besides the theorem, we will exhibit a few stochastic processes to which this and similar statements can be related.

Links: [[5]] and [[6]]

Prediction, Retrodiction, and the Amount of Information Stored in the Present

Ellison, Christopher (
Complexity Sciences Center, UC Davis

We introduce an ambidextrous view of stochastic dynamical systems, comparing their forward-time and reverse-time representations and then integrating them into a single time-symmetric representation. The perspective is useful theoretically, computationally, and conceptually. Mathematically, we prove that the excess entropy--a familiar measure of organization in complex systems--is the mutual information not only between the past and future, but also between the predictive and retrodictive causal states. Practically, we exploit the connection between prediction and retrodiction to directly calculate the excess entropy. Conceptually, these lead one to discover new system invariants for stochastic dynamical systems: crypticity (information accessibility) and causal irreversibility. Ultimately, we introduce a time-symmetric representation that unifies all these quantities, compressing the two directional representations into one. The resulting compression offers a new conception of the amount of information stored in the present.

Links: [[7]]

Spatial Information Theory

Feldman, David (
College of the Atlantic

Links: File:Afm.tri.5.pdf and [[File:CHAOEH184043106_1.pdf

Crypticity and Information Accessibility

Mahoney, John (
UC Merced

Links: [[8]]

Complexity, Parallel Computation and Statistical Physics

Machta, Jon (
SFI & University of Massachusetts

Links: [[9]]

Automatic Identification of Information-Processing Structures in Cellular Automata

Mitchell, Melanie (
SFI & Portland State University

Cellular automata have been widely used as idealized models of natural spatially-extended dynamical systems.  An open question is how to best understand such systems in terms of their information-processing capabilities.   In this talk we address this question by describing several approaches to automatically identifying the structures underlying information processing in cellular automata. In particular, we review the computational mechanics methods of Crutchfield et al.,  the local sensitivity and local statistical complexity filters proposed by Shalizi et al., and the information theoretic filters proposed by Lizier et al.   We illustrate these methods by applying them to several one- and two-dimensional cellular automata that have been designed to perform the so-called density (or majority) classification task.

Statistical Mechanics of Interactive Learning

Still, Suzanne (
University of Hawaii at Manoa


Measuring the Complexity of Psychological States

Tononi, Guilio (
University of Michigan


Ergodic Parameters and Dynamical Complexity

Vilela-Mendes, Rui (
University of Lisbon

Using a cocycle formulation, old and new ergodic parameters beyond the Lyapunov exponent are rigorously characterized. Dynamical Renyi entropies and fluctuations of the local expansion rate are related by a generalization of the Pesin formula. How the ergodic parameters may be used to characterize the complexity of dynamical systems is illustrated by some examples: Clustering and synchronization, self-organized criticality and the topological structure of networks.

Links: [[10]]

Hidden Quantum Markov Models and Non-adaptive Read-out of Many-body States

Wiesner, Karoline (
University of Bristol

Links: [[11]]