Cosma Rohilla Shalizi

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Aaron Clauset, 2 Cosma Rohilla Shalizi, and M. E. J. Newman Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA Department of Computer Science, University of New Mexico, Albuquerque, NM 87131, USA Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213, USA Department of Physics and Center for the Study of Complex Systems,(More)
The authors consider processes on social networks that can potentially involve three factors: homophily, or the formation of social ties due to matching individual traits; social contagion, also known as social influence; and the causal effect of an individual's covariates on his or her behavior or other measurable responses. The authors show that(More)
IV Computational Mechanics 9 A Causal States . . . . . . . . . . . . . . 9 Causal States of a Process Defined . . . 9 1 Morphs . . . . . . . . . . . . . . . . 10 Independence of Past and Future Conditional on a Causal State . . . 10 2 Homogeneity . . . . . . . . . . . . . 11 Strict Homogeneity . . . . . . . . . 11 Weak Homogeneity . . . . . . . . . 11(More)
We present a new method for nonlinear prediction of discrete random sequences under minimal structural assumptions. We give a mathematical construction for optimal predictors of such processes, in the form of hidden Markov models. We then describe an algorithm, CSSR (Causal-State Splitting Reconstruction), which approximates the ideal predictor from data.(More)
Power-law distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and man-made phenomena. Unfortunately, the detection and characterization of power laws is complicated by the large fluctuations that occur in the tail of the distribution—the part of the distribution representing large(More)
Most current methods for identifying coherent structures in spatially extended systems rely on prior information about the form which those structures take. Here we present two approaches to automatically filter the changing configurations of spatial dynamical systems and extract coherent structures. One, local sensitivity filtering, is a modification of(More)
We present a new algorithm for discovering patterns in time series and other sequential data. We exhibit a reliable procedure for building the minimal set of hidden, Markovian states that is statistically capable of producing the behavior exhibited in the data | the underlying process’s causal states. Unlike conventional methods for tting hidden Markov(More)
Despite broad interest in self-organizing systems, there are few quantitative, experimentally applicable criteria for self-organization. The existing criteria all give counter-intuitive results for important cases. In this Letter, we propose a new criterion, namely, an internally generated increase in the statistical complexity, the amount of information(More)
Particle-like objects are observed to propagate and interact in many spatially extended dynamical systems. For one of the simplest classes of such systems, one-dimensional cellular automata, we establish a rigorous upper bound on the number of distinct products that these interactions can generate. The upper bound is controlled by the structural complexity(More)