Appealing to several multivariate information measures—some familiar, some new here—
we analyze the information embedded in discrete-valued stochastic time series. We dissect …

The reign of digital computing is being challenged, not only by fundamental physical limits
but also by alternative information processing paradigms. The Focus Issue on Intrinsic and …

Intelligent agents can cope with sensory-rich environments by learning task-agnostic state
abstractions. In this paper, we propose an algorithm to approximate causal states, which are …

Computational mechanics quantifies structure in a stochastic process via its causal states,
leading to the process's minimal, optimal predictor—the ϵ-machine ϵ-machine. We extend …

The principle goal of computational mechanics is to define pattern and structure so that the
organization of complex systems can be detected and quantified. Computational mechanics …

We introduce a family of Maxwellian Demons for which correlations among information
bearing degrees of freedom can be calculated exactly and in compact analytical form. This …

Causal asymmetry is one of the great surprises in predictive modeling: The memory required
to predict the future differs from the memory required to retrodict the past. There is a …

In stochastic modeling, the excess entropy--the mutual information shared between a
processes past and future--represents the fundamental lower bound of the memory needed …

Simulating quantum contextuality with classical systems requires memory. A fundamental
yet open question is what is the minimum memory needed and, therefore, the precise sense …

The predictive information required for proper trajectory sampling of a stochastic process
can be more efficiently transmitted via a quantum channel than a classical one. This recent …