Causal architecture, complexity and self-organization in time series and cellular automata

By Cosma Rohilla Shalizi, M. Olsson. 2001

266 citations recorded by the source corpus.

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Citations in the corpus

Multi-Level Cause-Effect Systems (2015, methodology): “Our approach to the automated construction of causal macro-variables is rooted in the theory of computational mechanics (Shalizi and Crutchfield, 2001; Shalizi, 2001; Shalizi and Shalizi, 2004).”
Visual Causal Feature Learning (2014, methodology): “Our approach derives its theoretical underpinnings from computational mechanics (Shalizi and Crutchfield, 2001; Shalizi, 2001), but supports a more explicitly causal interpretation by incorporating the possibility of confounding and interventions.”
An information-theoretic primer on complexity, self-organization, and emergence (2009, background): “We are proposing here to clarify the applicability and exemplify how diﬀerent information channels can be identiﬁed and used.”
A Network Diffusion Model of Disease Progression in Dementia (2012, unknown): “Indeed, the emergence of predictable and regular behavior from chaotic ensembles is considered a hallmark of complexity (Shalizi, 2001).”
JIDT: An Information-Theoretic Toolkit for Studying the Dynamics of Complex Systems (2014, background): “This contrasts with the statistical complexity [79, 80], an upper bound to the excess entropy, which measures all information which is relevant to the prediction of the future of the process states; i.e. the stored information which may be used in the future [23].”
Emergence Versus Self-Organisation: Different Concepts but Promising When Combined (2004, methodology): “In [25], a more formal approach is used to define self-organisation.”
Causal feature learning: an overview (2017, unknown): “The framework draws heavily on ideas developed in computational mechanics (Shalizi and Crutchfield 2001; Shalizi 2001; Shalizi and Moore 2003) and connects them with the framework of causal graphical models (Spirtes et al.”
Supply-chain networks: a complex adaptive systems perspective (2005, background): “Notions of complexity , emergence and s elf-organization have also been formalized and quantiﬁed in terms of various information measures (Shalizi 2005).”
Complexity and Adaptivity in Supply Networks: Building Supply Network Theory Using a Complex Adaptive Systems Perspective (2007, methodology): “Analysis techniques used within these articles include chaos theory (Strogatz, 1994), computational and statistical mechanics (Shalizi, 2001), and nonlinear time series methods (Williams, 1997).”