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    <title>Pinboard (cshalizi)</title>
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  </channel><item rdf:about="https://journals.aps.org/pre/abstract/10.1103/PhysRevE.108.014304">
    <title>Phys. Rev. E 108, 014304 (2023) - Dynamical independence: Discovering emergent macroscopic processes in complex dynamical systems</title>
    <dc:date>2023-07-24T01:19:02+00:00</dc:date>
    <link>https://journals.aps.org/pre/abstract/10.1103/PhysRevE.108.014304</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We introduce a notion of emergence for macroscopic variables associated with highly multivariate microscopic dynamical processes. Dynamical independence instantiates the intuition of an emergent macroscopic process as one possessing the characteristics of a dynamical system “in its own right,” with its own dynamical laws distinct from those of the underlying microscopic dynamics. We quantify (departure from) dynamical independence by a transformation-invariant Shannon information-based measure of dynamical dependence. We emphasize the data-driven discovery of dynamically independent macroscopic variables, and introduce the idea of a multiscale “emergence portrait” for complex systems. We show how dynamical dependence may be computed explicitly for linear systems in both time and frequency domains, facilitating discovery of emergent phenomena across spatiotemporal scales, and outline application of the linear operationalization to inference of emergence portraits for neural systems from neurophysiological time-series data. We discuss dynamical independence for discrete- and continuous-time deterministic dynamics, with potential application to Hamiltonian mechanics and classical complex systems such as flocking and cellular automata."

--- As rvenkat says, the lack of reference to Crutchfield et al. is striking (even if I am among the alii: [https://arxiv.org/abs/cond-mat/0303625].)  On the one hand: sic transit gloria mundi, etc., etc.  On the other hand: oh come _on_.
--- The limiting case of their dynamical independence would be when the coarse-grained variable follows a deterministic process of its own.  (There are then very general reasons to expect an H theorem a la Boltzmann: [http://arxiv.org/abs/cond-mat/0508089].)  Otherwise, it would seem very hard for to avoid some leakage of information from the microscale to the macro.  For an extreme example, let X=the continuous logistic map, say with r=4 and Y=the binary sequence that's 0 if X is =< 1/2 and 1 otherwise.  (This is the "generating" partition.)  The latter, symbolic-dynamical sequence is in fact a perfect model of IID coin-tossing (a Bernoulli(0.5) stochastic process), so conditioning on the past of Y gives no information about its future, but conditioning on X gives perfect information about the future of Y.  If conditioning on X seems like cheating, say X'=the discrete symbol sequence we get by dividing [0,1] into pre-pre-pre-... pre-images of the cells of the Y partition.  X' is discrete, but depending on how much we refined the generating partition, it lets us look arbitrarily far into the future of Y.  (We'd still get a lot of information from X'' which just divides [0,1] into many equal-length intervals.)  Now to be quite fair there are places where they acknowledge that "dynamical independence" will generally be imperfect, etc.
--- As for treating everything as a linear-and-Gaussian process, I realize the authors have gotten away with publishing that advice for decades at this point, but it was always dumb, and I think if you pressed them they'd admit it.]]></description>
<dc:subject>complexity_measures information_theory via:rvenkat emergence macro_from_micro transfer_entropy in_NB have_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ebde8153c608/</dc:identifier>
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<item rdf:about="https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.2.023219">
    <title>Phys. Rev. Research 2, 023219 (2020) - General anesthesia reduces complexity and temporal asymmetry of the informational structures derived from neural recordings in Drosophila</title>
    <dc:date>2023-04-27T12:02:04+00:00</dc:date>
    <link>https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.2.023219</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We apply techniques from the field of computational mechanics to evaluate the statistical complexity of neural recording data from fruit flies. First, we connect statistical complexity to the flies' level of conscious arousal, which is manipulated by general anesthesia (isoflurane). We show that the complexity of even single channel time series data decreases under anesthesia. The observed difference in complexity between the two states of conscious arousal increases as higher orders of temporal correlations are taken into account. We then go on to show that, in addition to reducing complexity, anesthesia also modulates the informational structure between the forward- and reverse-time neural signals. Specifically, using three distinct notions of temporal asymmetry we show that anesthesia reduces temporal asymmetry on information-theoretic and information-geometric grounds. In contrast to prior work, our results show that: (1) Complexity differences can emerge at very short timescales and across broad regions of the fly brain, thus heralding the macroscopic state of anesthesia in a previously unforeseen manner, and (2) that general anesthesia also modulates the temporal asymmetry of neural signals. Together, our results demonstrate that anesthetized brains become both less structured and more reversible."

--- Why was I (pardon the phrase) completely unaware of this?]]></description>
<dc:subject>to:NB to_read neural_data_analysis complexity_measures</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8098d4215588/</dc:identifier>
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<item rdf:about="https://www.nature.com/articles/336306a0">
    <title>A simple measure of complexity | Nature</title>
    <dc:date>2023-04-24T22:11:00+00:00</dc:date>
    <link>https://www.nature.com/articles/336306a0</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>landauer.rolf complexity_measures non-equilibrium lloyd.seth pagels.heinz_r. have_read have_taught thermodynamic_depth re:dissertation cleaning_out_the_filing_cabinet_for_the_first_time_since_2005 in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4660baa0508b/</dc:identifier>
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<item rdf:about="https://www.sciencedirect.com/science/article/abs/pii/0003491688900942">
    <title>Complexity as thermodynamic depth - ScienceDirect</title>
    <dc:date>2023-04-24T22:09:13+00:00</dc:date>
    <link>https://www.sciencedirect.com/science/article/abs/pii/0003491688900942</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A measure of complexity for the macroscopic states of physical systems is defined. Called depth, the measure is universal: it applies to all physical systems. The form of the measure is uniquely fixed by the requirement that it be a continuous, additive function of the processes that can result in a state. Applied to a Hamiltonian system, the measure is equal to the difference between the system's coarse- and fine-grained entropy, a quantity that we call thermodynamic depth. The measure satisfies the intuitive requirements that wholly ordered and wholly random systems are not thermodynamically deep and that a complex object together with a copy is not much deeper than the object alone. Applied to systems capable of computation, the measure yields a conventional computational measure of complexity as a special case. The relation of depth and thermodynamic depth to previously proposed definitions of complexity is discussed, and applications to physical, chemical, and mathematical problems are proposed."

--- See [https://arxiv.org/abs/cond-mat/9808147]]]></description>
<dc:subject>complexity_measures non-equilibrium lloyd.seth pagels.heinz_r. have_read have_taught thermodynamic_depth re:dissertation cleaning_out_the_filing_cabinet_for_the_first_time_since_2005 in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f4f91f073b2c/</dc:identifier>
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<item rdf:about="https://www.sciencedirect.com/science/article/abs/pii/S0020019097001105">
    <title>Finite automata-models for the investigation of dynamical systems - ScienceDirect</title>
    <dc:date>2023-04-24T22:07:58+00:00</dc:date>
    <link>https://www.sciencedirect.com/science/article/abs/pii/S0020019097001105</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We describe a method to measure the complexity of a dynamical system. By complexity we mean the intrinsic information processing abilities which we believe to be visible only on an infinitesimal scale. The complexity measure is based on concepts from information theory and from the theory of formal languages."]]></description>
<dc:subject>to:NB have_read automata_theory dynamical_systems complexity_measures re:dissertation cleaning_out_the_filing_cabinet_for_the_first_time_since_2005</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:7be4910aff26/</dc:identifier>
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<item rdf:about="https://journals.aps.org/pre/abstract/10.1103/PhysRevE.55.5050">
    <title>Phys. Rev. E 55, 5050 (1997) - Characterizing the dynamics of stochastic bistable systems by measures of complexity</title>
    <dc:date>2023-04-24T22:05:40+00:00</dc:date>
    <link>https://journals.aps.org/pre/abstract/10.1103/PhysRevE.55.5050</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The dynamics of noisy bistable systems is analyzed by means of Lyapunov exponents and measures of complexity. We consider both the classical Kramers problem with additive white noise and the case when the barrier fluctuates due to additional external colored noise. In the case of additive noise we calculate the Lyapunov exponents and all measures of complexity analytically as functions of the noise intensity or the mean escape time, respectively. For the problem of a fluctuating barrier the usual description of the dynamics with the mean escape time is not sufficient. The application of the concept of measures of complexity allows us to describe the structures of motion in more detail. Most complexity measures indicate the value of the correlation time at which the phenomenon of resonant activation occurs with an extremum."]]></description>
<dc:subject>have_read complexity_measures stochastic_processes kurths.jurgen cleaning_out_the_filing_cabinet_for_the_first_time_since_2005 re:dissertation metastability in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ee4a2bd3bebb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kurths.jurgen"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cleaning_out_the_filing_cabinet_for_the_first_time_since_2005"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:dissertation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:metastability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://guilfordjournals.com/doi/pdf/10.1521/siso.2023.87.2.234">
    <title>Inviting the Vampire in: Value and Thermodynamic Depth | Science &amp; Society</title>
    <dc:date>2023-04-11T01:34:51+00:00</dc:date>
    <link>https://guilfordjournals.com/doi/pdf/10.1521/siso.2023.87.2.234</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We refute the objection raised against Marx's theory of value by Philip Mirowski (that it is an internally inconsistent amalgam of the substance and social model of value) by identifying value insofar as it subsists between production and exchange with the property in statistical mechanics known as thermodynamic depth. In production, concrete labor time is converted into the thermodynamic depth embodied in commodities. In exchange, the thermodynamic depth of commodities is converted back into time again, albeit, at this point in the circuit, the time into which it is converted is socially necessary labor time, i.e., abstract labor. Consistently with the social model of value, value increases can (as Mirowski points out) arise where changes in the conversion factor between time and thermodynamic depth take place between production and exchange, but (consistently with the substance model) the thermodynamic depth of commodities can only be increased in production."

--- This seems absolutely mad, but I have a special interest in thermodynamic depth (that's how I found this!), so I'd appreciate anyone with access sharing a copy.
--- ETA: that worked (fast!).   Thanks to M.R.]]></description>
<dc:subject>to:NB marxism complexity_measures thermodynamic_depth</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:cf03cab74e8e/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/2206.02279">
    <title>[2206.02279] Assembly Theory Explains and Quantifies the Emergence of Selection and Evolution</title>
    <dc:date>2022-06-09T08:39:12+00:00</dc:date>
    <link>https://arxiv.org/abs/2206.02279</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Since the time of Darwin, scientists have struggled to reconcile the evolution of biological forms in a universe determined by fixed laws. These laws underpin the origin of life, evolution, human culture and technology, as set by the boundary conditions of the universe, however these laws cannot predict the emergence of these things. By contrast evolutionary theory works in the opposite direction, indicating how selection can explain why some things exist and not others. To understand how open-ended forms can emerge in a forward-process from physics that does not include their design, a new approach to understand the non-biological to biological transition is necessary. Herein, we present a new theory, Assembly Theory (AT), which explains and quantifies the emergence of selection and evolution. In AT, the complexity of an individual observable object is measured by its Assembly Index (a), defined as the minimal number of steps needed to construct the object from basic building blocks. Combining a with the copy number defines a new quantity called Assembly which quantifies the amount of selection required to produce a given ensemble of objects. We investigate the internal structure and properties of assembly space and quantify the dynamics of undirected exploratory processes as compared to the directed processes that emerge from selection. The implementation of assembly theory allows the emergence of selection in physical systems to be quantified at any scale as the transition from undirected-discovery dynamics to a selected process within the assembly space. This yields a mechanism for the onset of selection and evolution and a formal approach to defining life. Because the assembly of an object is easily calculatable and measurable it is possible to quantify a lower limit on the amount of selection and memory required to produce complexity uniquely linked to biology in the universe."]]></description>
<dc:subject>evolutionary_biology complexity complexity_measures lachmann.michael kith_and_kin in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ce0d1ead4858/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evolutionary_biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:lachmann.michael"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://aip.scitation.org/doi/10.1063/5.0063384">
    <title>Integrated information as a common signature of dynamical and information-processing complexity: Chaos: An Interdisciplinary Journal of Nonlinear Science: Vol 32, No 1</title>
    <dc:date>2022-04-13T02:24:37+00:00</dc:date>
    <link>https://aip.scitation.org/doi/10.1063/5.0063384</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The apparent dichotomy between information-processing and dynamical approaches to complexity science forces researchers to choose between two diverging sets of tools and explanations, creating conflict and often hindering scientific progress. Nonetheless, given the shared theoretical goals between both approaches, it is reasonable to conjecture the existence of underlying common signatures that capture interesting behavior in both dynamical and information-processing systems. Here, we argue that a pragmatic use of integrated information theory (IIT), originally conceived in theoretical neuroscience, can provide a potential unifying framework to study complexity in general multivariate systems. By leveraging metrics put forward by the integrated information decomposition framework, our results reveal that integrated information can effectively capture surprisingly heterogeneous signatures of complexity—including metastability and criticality in networks of coupled oscillators as well as distributed computation and emergent stable particles in cellular automata—without relying on idiosyncratic, ad hoc criteria. These results show how an agnostic use of IIT can provide important steps toward bridging the gap between informational and dynamical approaches to complex systems.
"Originally conceived within theoretical neuroscience, integrated information theory (IIT) has been rarely used in other fields—such as complex systems or non-linear dynamics—despite the great value it has to offer. In this article, we inspect the basics of IIT, dissociating it from its contentious claims about the nature of consciousness. Relieved of this philosophical burden, IIT presents itself as an appealing formal framework to study complexity in biological or artificial systems, applicable in a wide range of domains. To illustrate this, we present an exploration of integrated information in complex systems and relate it to other notions of complexity commonly used in systems such as coupled oscillators and cellular automata. Through these applications, we advocate for IIT as a valuable framework capable of revealing common threads between diverging branches of complexity science."

--- On a quick skim, a lower & distorted form of what (e.g.) JPC and co have been doing since the 1990s.  Last tag applies.
]]></description>
<dc:subject>to:NB information_theory complexity_measures color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e3b4a49d658c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.aeaweb.org/articles?id=10.1257/aer.20191717">
    <title>What Makes a Rule Complex? - American Economic Association</title>
    <dc:date>2020-11-30T16:06:01+00:00</dc:date>
    <link>https://www.aeaweb.org/articles?id=10.1257/aer.20191717</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We study the complexity of rules by paying experimental subjects to implement a series of algorithms and then eliciting their willingness-to-pay to avoid implementing them again in the future. The design allows us to examine hypotheses from the theoretical "automata" literature about the characteristics of rules that generate complexity costs. We find substantial aversion to complexity and a number of regularities in the characteristics of rules that make them complex and costly for subjects. Experience with a rule, the way a rule is represented, and the context in which a rule is implemented (mentally versus physically) also influence complexity."]]></description>
<dc:subject>to:NB complexity_measures learning_in_games experimental_economics decision-making cognitive_science</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b93c403fcb3b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:experimental_economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.nature.com/articles/s41598-019-53167-5">
    <title>A detailed characterization of complex networks using Information Theory | Scientific Reports</title>
    <dc:date>2020-11-19T20:12:08+00:00</dc:date>
    <link>https://www.nature.com/articles/s41598-019-53167-5</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Understanding the structure and the dynamics of networks is of paramount importance for many scientific fields that rely on network science. Complex network theory provides a variety of features that help in the evaluation of network behavior. However, such analysis can be confusing and misleading as there are many intrinsic properties for each network metric. Alternatively, Information Theory methods have gained the spotlight because of their ability to create a quantitative and robust characterization of such networks. In this work, we use two Information Theory quantifiers, namely Network Entropy and Network Fisher Information Measure, to analyzing those networks. Our approach detects non-trivial characteristics of complex networks such as the transition present in the Watts-Strogatz model from k-ring to random graphs; the phase transition from a disconnected to an almost surely connected network when we increase the linking probability of Erdős-Rényi model; distinct phases of scale-free networks when considering a non-linear preferential attachment, fitness, and aging features alongside the configuration model with a pure power-law degree distribution. Finally, we analyze the numerical results for real networks, contrasting our findings with traditional complex network methods. In conclusion, we present an efficient method that ignites the debate on network characterization."]]></description>
<dc:subject>to:NB information_theory complexity_measures network_data_analysis to:blog have_read my_initial_skeptical_coloration_became_on_examination_a_permanent_stain</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e4d21bc5d723/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:blog"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:my_initial_skeptical_coloration_became_on_examination_a_permanent_stain"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.mdpi.com/1099-4300/11/3/385">
    <title>Entropy | Free Full-Text | Properties of the Statistical Complexity Functional and Partially Deterministic HMMs</title>
    <dc:date>2020-05-16T17:46:39+00:00</dc:date>
    <link>https://www.mdpi.com/1099-4300/11/3/385</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Statistical complexity is a measure of complexity of discrete-time stationary stochastic processes, which has many applications. We investigate its more abstract properties as a non-linear function of the space of processes and show its close relation to the Knight’s prediction process. We prove lower semi-continuity, concavity, and a formula for the ergodic decomposition of statistical complexity. On the way, we show that the discrete version of the prediction process has a continuous Markov transition. We also prove that, given the past output of a partially deterministic hidden Markov model (HMM), the uncertainty of the internal state is constant over time and knowledge of the internal state gives no additional information on the future output. Using this fact, we show that the causal state distribution is the unique stationary representation on prediction space that may have finite entropy."]]></description>
<dc:subject>to:NB complexity_measures markov_models prediction stochastic_processes to_read re:AoS_project</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:7bbb0912fe69/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:markov_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:AoS_project"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.100.208702">
    <title>Phys. Rev. Lett. 100, 208702 (2008) - Using the Memories of Multiscale Machines to Characterize Complex Systems</title>
    <dc:date>2020-05-12T23:50:33+00:00</dc:date>
    <link>https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.100.208702</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A scheme is presented to extract detailed dynamical signatures from successive measurements of complex systems. Relative entropy based time series tools are used to quantify the gain in predictive power of increasing past knowledge. By lossy compression, data is represented by increasingly coarsened symbolic strings. Each compression resolution is modeled by a machine: a finite memory transition matrix. Applying the relative entropy tools to each machine’s memory exposes correlations within many time scales. Examples are given for cardiac arrhythmias and different heart conditions are distinguished."]]></description>
<dc:subject>to:NB complexity_measures re:AoS_project to_read color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3ea37e3fcd80/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:AoS_project"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2004.07857">
    <title>[2004.07857] Ultimate limit on time signal generation</title>
    <dc:date>2020-04-24T13:06:18+00:00</dc:date>
    <link>https://arxiv.org/abs/2004.07857</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The generation of time signals is a fundamental task in science. Here we study the relation between the quality of a time signal and the physics of the system that generates it. According to quantum theory, any time signal can be decomposed into individual quanta that lead to single detection events. Our main result is a bound on how sharply peaked in time these events can be, which depends on the dimension of the signal generator. This result promises applications in various directions, including information theory, quantum clocks, and process simulation."]]></description>
<dc:subject>to:NB quantum_mechanics complexity_measures color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ae91b74e77d2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:quantum_mechanics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://link.springer.com/article/10.1007/s10955-017-1776-0">
    <title>Leveraging Environmental Correlations: The Thermodynamics of Requisite Variety | SpringerLink</title>
    <dc:date>2019-11-10T22:23:21+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/s10955-017-1776-0</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Key to biological success, the requisite variety that confronts an adaptive organism is the set of detectable, accessible, and controllable states in its environment. We analyze its role in the thermodynamic functioning of information ratchets—a form of autonomous Maxwellian Demon capable of exploiting fluctuations in an external information reservoir to harvest useful work from a thermal bath. This establishes a quantitative paradigm for understanding how adaptive agents leverage structured thermal environments for their own thermodynamic benefit. General ratchets behave as memoryful communication channels, interacting with their environment sequentially and storing results to an output. The bulk of thermal ratchets analyzed to date, however, assume memoryless environments that generate input signals without temporal correlations. Employing computational mechanics and a new information-processing Second Law of Thermodynamics (IPSL) we remove these restrictions, analyzing general finite-state ratchets interacting with structured environments that generate correlated input signals. On the one hand, we demonstrate that a ratchet need not have memory to exploit an uncorrelated environment. On the other, and more appropriate to biological adaptation, we show that a ratchet must have memory to most effectively leverage structure and correlation in its environment. The lesson is that to optimally harvest work a ratchet’s memory must reflect the input generator’s memory. Finally, we investigate achieving the IPSL bounds on the amount of work a ratchet can extract from its environment, discovering that finite-state, optimal ratchets are unable to reach these bounds. In contrast, we show that infinite-state ratchets can go well beyond these bounds by utilizing their own infinite “negentropy”. We conclude with an outline of the collective thermodynamics of information-ratchet swarms."]]></description>
<dc:subject>to:NB control_theory_and_control_engineering maxwells_demon information_theory complexity_measures crutchfield.james_p. kith_and_kin ashby.w._ross law_of_requisite_variety to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:503af7cd0f0e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:control_theory_and_control_engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:maxwells_demon"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:crutchfield.james_p."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ashby.w._ross"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:law_of_requisite_variety"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://royalsocietypublishing.org/doi/full/10.1098/rsfs.2019.0058">
    <title>Probing complexity: thermodynamics and computational mechanics approaches to origins studies | Interface Focus</title>
    <dc:date>2019-10-23T19:19:38+00:00</dc:date>
    <link>https://royalsocietypublishing.org/doi/full/10.1098/rsfs.2019.0058</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper proposes new avenues for origins research that apply modern concepts from stochastic thermodynamics, information thermodynamics and complexity science. Most approaches to the emergence of life prioritize certain compounds, reaction pathways, environments or phenomena. What they all have in common is the objective of reaching a state that is recognizably alive, usually positing the need for an evolutionary process. As with life itself, this correlates with a growth in the complexity of the system over time. Complexity often takes the form of an intuition or a proxy for a phenomenon that defies complete understanding. However, recent progress in several theoretical fields allows the rigorous computation of complexity. We thus propose that measurement and control of the complexity and information content of origins-relevant systems can provide novel insights that are absent in other approaches. Since we have no guarantee that the earliest forms of life (or alien life) used the same materials and processes as extant life, an appeal to complexity and information processing provides a more objective and agnostic approach to the search for life's beginnings. This paper gives an accessible overview of the three relevant branches of modern thermodynamics. These frameworks are not commonly applied in origins studies, but are ideally suited to the analysis of such non-equilibrium systems. We present proposals for the application of these concepts in both theoretical and experimental origins settings."]]></description>
<dc:subject>to:NB to_read non-equilibrium statistical_mechanics origin_of_life complexity complexity_measures self-organization flashbacks_to_my_dissertation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:cee3d4db6f45/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:non-equilibrium"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_mechanics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:origin_of_life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:self-organization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:flashbacks_to_my_dissertation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1909.13243">
    <title>[1909.13243] Measuring complexity</title>
    <dc:date>2019-10-01T15:23:44+00:00</dc:date>
    <link>https://arxiv.org/abs/1909.13243</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Complexity is heterogenous, involving nonlinearity, self-organisation, diversity, adaptive behaviour, among other things. It is therefore obviously worth asking whether purported measures of complexity measure aggregate phenomena, or individual aspects of complexity and if so which. This paper uses a recently developed rigorous framework for understanding complexity to answer this question about measurement. The approach is two-fold: find measures of individual aspects of complexity on the one hand, and explain measures of complexity on the other. We illustrate the conceptual framework of complexity science and how it links the foundations to the practised science with examples from different scientific fields and of various aspects of complexity. Furthermore, we analyse a selection of purported measures of complexity that have found wide application and explain why and how they measure aspects of complexity. This work gives the reader a tool to take any existing measure of complexity and analyse it, and to take any feature of complexity and find the right measure for it."]]></description>
<dc:subject>to:NB wiesner.karoline complexity_measures to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:fe3738a40063/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:wiesner.karoline"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1909.02900">
    <title>[1909.02900] On the Estimation of Network Complexity: Dimension of Graphons</title>
    <dc:date>2019-09-09T03:45:40+00:00</dc:date>
    <link>https://arxiv.org/abs/1909.02900</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Network complexity has been studied for over half a century and has found a wide range of applications. Many methods have been developed to characterize and estimate the complexity of networks. However, there has been little research with statistical guarantees. In this paper, we develop a statistical theory of graph complexity in a general model of random graphs, the so-called graphon model. Given a graphon, we endow the latent space of the nodes with the so-called neighborhood distance that measures the propensity of two nodes to be connected with similar nodes. Our complexity index is then based on the covering number and the Minkowski dimension of (a purified version of) this metric space. Although the latent space is not identifiable, these indices turn out to be identifiable. This notion of complexity has simple interpretations on popular examples of random graphs: it matches the number of communities in stochastic block models; the dimension of the Euclidean space in random geometric graphs; the regularity of the link function in Hölder graphon models. From a single observation of the graph, we construct an estimator of the neighborhood-distance and show universal non-asymptotic bounds for its risk, matching minimax lower bounds. Based on this estimated distance, we compute the corresponding covering number and Minkowski dimension and we provide optimal non-asymptotic error bounds for these two plug-in estimators."]]></description>
<dc:subject>to:NB to_read complexity_measures graph_limits network_data_analysis re:smoothing_adjacency_matrices</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:47ae48949e23/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graph_limits"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:smoothing_adjacency_matrices"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1905.13173">
    <title>[1905.13173] Distinguishing states of conscious arousal using statistical complexity</title>
    <dc:date>2019-08-05T12:45:27+00:00</dc:date>
    <link>https://arxiv.org/abs/1905.13173</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We apply techniques from the field of computational mechanics to evaluate the statistical complexity of neural recording data in fruit flies. We connect statistical complexity to the flies' level of conscious arousal, which is manipulated by general anaesthesia (isoflurane). We show that the complexity of even single channel time series data decreases under anaesthesia. The observed difference in complexity between the two states of conscious arousal increases as higher orders of temporal correlations are taken into account. In contrast to prior work, our results show that complexity differences can emerge at very short time scales and across broad regions of the fly brain without the need to saturate Markov order, thus heralding the macroscopic state of anaesthesia in a previously unforeseen manner. Furthering the links between physics, complexity science and neuroscience promotes the understanding of the physical basis that supports the level of conscious arousal in biological organisms."]]></description>
<dc:subject>to:NB complexity_measures neural_data_analysis consciousness CSSR i_dont_know_you_people color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:74c71b413fb3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neural_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:consciousness"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:CSSR"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:i_dont_know_you_people"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.pnas.org/content/115/2/E144.abstract.html">
    <title>Quantitative historical analysis uncovers a single dimension of complexity that structures global variation in human social organization</title>
    <dc:date>2018-01-09T20:46:25+00:00</dc:date>
    <link>http://www.pnas.org/content/115/2/E144.abstract.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Do human societies from around the world exhibit similarities in the way that they are structured, and show commonalities in the ways that they have evolved? These are long-standing questions that have proven difficult to answer. To test between competing hypotheses, we constructed a massive repository of historical and archaeological information known as “Seshat: Global History Databank.” We systematically coded data on 414 societies from 30 regions around the world spanning the last 10,000 years. We were able to capture information on 51 variables reflecting nine characteristics of human societies, such as social scale, economy, features of governance, and information systems. Our analyses revealed that these different characteristics show strong relationships with each other and that a single principal component captures around three-quarters of the observed variation. Furthermore, we found that different characteristics of social complexity are highly predictable across different world regions. These results suggest that key aspects of social organization are functionally related and do indeed coevolve in predictable ways. Our findings highlight the power of the sciences and humanities working together to rigorously test hypotheses about general rules that may have shaped human history."

--- Contributed, so the last tag applies very forcefully.]]></description>
<dc:subject>to:NB to_read comparative_history complexity_measures principal_components to_teach:undergrad-ADA color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:fc1b60107b6e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:comparative_history"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:principal_components"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.aps.org/pre/abstract/10.1103/PhysRevE.95.042140">
    <title>Phys. Rev. E 95, 042140 (2017) - Thermodynamics of complexity and pattern manipulation</title>
    <dc:date>2017-06-23T17:00:16+00:00</dc:date>
    <link>https://journals.aps.org/pre/abstract/10.1103/PhysRevE.95.042140</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Many organisms capitalize on their ability to predict the environment to maximize available free energy and reinvest this energy to create new complex structures. This functionality relies on the manipulation of patterns—temporally ordered sequences of data. Here, we propose a framework to describe pattern manipulators—devices that convert thermodynamic work to patterns or vice versa—and use them to build a “pattern engine” that facilitates a thermodynamic cycle of pattern creation and consumption. We show that the least heat dissipation is achieved by the provably simplest devices, the ones that exhibit desired operational behavior while maintaining the least internal memory. We derive the ultimate limits of this heat dissipation and show that it is generally nonzero and connected with the pattern's intrinsic crypticity—a complexity theoretic quantity that captures the puzzling difference between the amount of information the pattern's past behavior reveals about its future and the amount one needs to communicate about this past to optimally predict the future."]]></description>
<dc:subject>to:NB to_read complexity complexity_measures prediction thermodynamics maxwells_demon</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:461e0c65660b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:thermodynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:maxwells_demon"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.ncbi.nlm.nih.gov/pubmed/22008374">
    <title>Altered resting state complexity in schizophrenia. - PubMed - NCBI</title>
    <dc:date>2015-03-30T16:51:52+00:00</dc:date>
    <link>http://www.ncbi.nlm.nih.gov/pubmed/22008374</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The complexity of the human brain's activity and connectivity varies over temporal scales and is altered in disease states such as schizophrenia. Using a multi-level analysis of spontaneous low-frequency fMRI data stretching from the activity of individual brain regions to the coordinated connectivity pattern of the whole brain, we investigate the role of brain signal complexity in schizophrenia. Specifically, we quantitatively characterize the univariate wavelet entropy of regional activity, the bivariate pairwise functional connectivity between regions, and the multivariate network organization of connectivity patterns. Our results indicate that univariate measures of complexity are less sensitive to disease state than higher level bivariate and multivariate measures. While wavelet entropy is unaffected by disease state, the magnitude of pairwise functional connectivity is significantly decreased in schizophrenia and the variance is increased. Furthermore, by considering the network structure as a function of correlation strength, we find that network organization specifically of weak connections is strongly correlated with attention, memory, and negative symptom scores and displays potential as a clinical biomarker, providing up to 75% classification accuracy and 85% sensitivity. We also develop a general statistical framework for the testing of group differences in network properties, which is broadly applicable to studies where changes in network organization are crucial to the understanding of brain function."]]></description>
<dc:subject>to:NB complexity_measures functional_connectivity schizophrenia neuroscience network_data_analysis fmri re:network_differences bassett.danielle_s.</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:eca9200a964d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:functional_connectivity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:schizophrenia"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neuroscience"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:fmri"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:network_differences"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bassett.danielle_s."/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/cs/0508075">
    <title>[cs/0508075] Complexity of Networks</title>
    <dc:date>2014-09-03T18:44:31+00:00</dc:date>
    <link>http://arxiv.org/abs/cs/0508075</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Network or graph structures are ubiquitous in the study of complex systems. Often, we are interested in complexity trends of these system as it evolves under some dynamic. An example might be looking at the complexity of a food web as species enter an ecosystem via migration or speciation, and leave via extinction. 
"In this paper, a complexity measure of networks is proposed based on the {\em complexity is information content} paradigm. To apply this paradigm to any object, one must fix two things: a representation language, in which strings of symbols from some alphabet describe, or stand for the objects being considered; and a means of determining when two such descriptions refer to the same object. With these two things set, the information content of an object can be computed in principle from the number of equivalent descriptions describing a particular object. 
"I propose a simple representation language for undirected graphs that can be encoded as a bitstring, and equivalence is a topological equivalence. I also present an algorithm for computing the complexity of an arbitrary undirected network."]]></description>
<dc:subject>to:NB complexity_measures network_data_analysis</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a8265cf69088/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://link.springer.com/article/10.1007/s11009-012-9311-x">
    <title>Max-Plus Objects to Study the Complexity of Graphs - Springer</title>
    <dc:date>2014-08-06T11:13:21+00:00</dc:date>
    <link>http://link.springer.com/article/10.1007/s11009-012-9311-x</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Given an undirected graph G, we define a new object H G , called the mp-chart of G, in the max-plus algebra. We use it, together with the max-plus permanent, to describe the complexity of graphs. We show how to compute the mean and the variance of H G in terms of the adjacency matrix of G and we give a central limit theorem for H G . Finally, we show that the mp-chart is easily tractable also for the complement graph."]]></description>
<dc:subject>to:NB graph_theory complexity_measures network_data_analysis</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1ddf520584aa/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graph_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1405.6903">
    <title>[1405.6903] Quantifying the Rise and Fall of Complexity in Closed Systems: The Coffee Automaton</title>
    <dc:date>2014-06-04T12:37:20+00:00</dc:date>
    <link>http://arxiv.org/abs/1405.6903</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[I don't think they get our complexity measure exactly right, but I need to read carefully.  (I _definitely_ don't see how you can use gzip as a reliable approximation to Kolmogorov complexity, cf. http://bactra.org/notebooks/cep-gzip.html.)]]></description>
<dc:subject>to:NB to_read statistical_mechanics non-equilibrium arrow_of_time complexity_measures aaronson.scott carroll.sean to:blog</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4663e7aecc54/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_mechanics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:non-equilibrium"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:arrow_of_time"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:aaronson.scott"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:carroll.sean"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:blog"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.pnas.org/content/early/2013/11/12/1314922110.abstract">
    <title>Quantifying causal emergence shows that macro can beat micro</title>
    <dc:date>2014-05-02T15:24:17+00:00</dc:date>
    <link>http://www.pnas.org/content/early/2013/11/12/1314922110.abstract</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Causal interactions within complex systems can be analyzed at multiple spatial and temporal scales. For example, the brain can be analyzed at the level of neurons, neuronal groups, and areas, over tens, hundreds, or thousands of milliseconds. It is widely assumed that, once a micro level is fixed, macro levels are fixed too, a relation called supervenience. It is also assumed that, although macro descriptions may be convenient, only the micro level is causally complete, because it includes every detail, thus leaving no room for causation at the macro level. However, this assumption can only be evaluated under a proper measure of causation. Here, we use a measure [effective information (EI)] that depends on both the effectiveness of a system’s mechanisms and the size of its state space: EI is higher the more the mechanisms constrain the system’s possible past and future states. By measuring EI at micro and macro levels in simple systems whose micro mechanisms are fixed, we show that for certain causal architectures EI can peak at a macro level in space and/or time. This happens when coarse-grained macro mechanisms are more effective (more deterministic and/or less degenerate) than the underlying micro mechanisms, to an extent that overcomes the smaller state space. Thus, although the macro level supervenes upon the micro, it can supersede it causally, leading to genuine causal emergence—the gain in EI when moving from a micro to a macro level of analysis."

--- Cf. http://arxiv.org/abs/cond-mat/0303625]]></description>
<dc:subject>to:NB to_read emergence information_theory complexity_measures re:what_is_a_macrostate</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a9216045af4b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:emergence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:what_is_a_macrostate"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://link.springer.com/article/10.1007/s10618-013-0312-3">
    <title>CID: an efficient complexity-invariant distance for time series - Springer</title>
    <dc:date>2014-02-19T02:47:38+00:00</dc:date>
    <link>http://link.springer.com/article/10.1007/s10618-013-0312-3</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The ubiquity of time series data across almost all human endeavors has produced a great interest in time series data mining in the last decade. While dozens of classification algorithms have been applied to time series, recent empirical evidence strongly suggests that simple nearest neighbor classification is exceptionally difficult to beat. The choice of distance measure used by the nearest neighbor algorithm is important, and depends on the invariances required by the domain. For example, motion capture data typically requires invariance to warping, and cardiology data requires invariance to the baseline (the mean value). Similarly, recent work suggests that for time series clustering, the choice of clustering algorithm is much less important than the choice of distance measure used.In this work we make a somewhat surprising claim. There is an invariance that the community seems to have missed, complexity invariance. Intuitively, the problem is that in many domains the different classes may have different complexities, and pairs of complex objects, even those which subjectively may seem very similar to the human eye, tend to be further apart under current distance measures than pairs of simple objects. This fact introduces errors in nearest neighbor classification, where some complex objects may be incorrectly assigned to a simpler class. Similarly, for clustering this effect can introduce errors by “suggesting” to the clustering algorithm that subjectively similar, but complex objects belong in a sparser and larger diameter cluster than is truly warranted.We introduce the first complexity-invariant distance measure for time series, and show that it generally produces significant improvements in classification and clustering accuracy. We further show that this improvement does not compromise efficiency, since we can lower bound the measure and use a modification of triangular inequality, thus making use of most existing indexing and data mining algorithms. We evaluate our ideas with the largest and most comprehensive set of time series mining experiments ever attempted in a single work, and show that complexity-invariant distance measures can produce improvements in classification and clustering in the vast majority of cases."

- And what does "complexity" mean here, exactly?]]></description>
<dc:subject>to:NB time_series clustering classifiers data_mining complexity_measures statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b818e22f6c68/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:clustering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:classifiers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1309.3792">
    <title>[1309.3792] Exact Complexity: The Spectral Decomposition of Intrinsic Computation</title>
    <dc:date>2013-09-17T20:32:04+00:00</dc:date>
    <link>http://arxiv.org/abs/1309.3792</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We give exact formulae for a wide family of complexity measures that capture the organization of hidden nonlinear processes. The spectral decomposition of operator-valued functions leads to closed-form expressions involving the full eigenvalue spectrum of the mixed-state presentation of a process's epsilon-machine causal-state dynamic. Measures include correlation functions, power spectra, past-future mutual information, transient and synchronization informations, and many others. As a result, a direct and complete analysis of intrinsic computation is now available for the temporal organization of finitary hidden Markov models and nonlinear dynamical systems with generating partitions and for the spatial organization in one-dimensional systems, including spin systems, cellular automata, and complex materials via chaotic crystallography."]]></description>
<dc:subject>to:NB spectral_methods complexity_measures information_theory markov_models kith_and_kin crutchfield.james_p. stochastic_processes re:AoS_project</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:834717f3080a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:spectral_methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:markov_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:crutchfield.james_p."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:AoS_project"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1305.3801">
    <title>[1305.3801] Symbolic Complexity for Nucleotide Sequences: A Sign of the Genome Structure</title>
    <dc:date>2013-05-17T12:07:48+00:00</dc:date>
    <link>http://arxiv.org/abs/1305.3801</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We introduce a method to estimate the complexity function of symbolic dynamical systems from a finite sequence of symbols. We test such complexity estimator on several symbolic dynamical systems whose complexity functions are known exactly. We use this technique to estimate the complexity function for genomes of several organisms under the assumption that a genome is a sequence produced by a (unknown) dynamical system. We show that the genome of several organisms share the property that their complexity functions behaves exponentially for words of small length $\ell$ ($0\leq \ell \leq 10$) and linearly for word lengths in the range $11 \leq \ell \leq 50$. It is also found that the species which are phylogenetically close each other have similar complexity functions calculated from a sample of their corresponding coding regions."

--- "Complexity" here == number of allowed words of a given length.  Time to send a reprint?]]></description>
<dc:subject>to:NB complexity_measures bioinformatics symbolic_dynamics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c106154c11ea/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bioinformatics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:symbolic_dynamics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1304.8046">
    <title>[1304.8046] Sophistication vs Logical Depth</title>
    <dc:date>2013-05-01T16:31:26+00:00</dc:date>
    <link>http://arxiv.org/abs/1304.8046</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Sophistication and logical depth are two measures that express how complicated the structure in a string is. Sophistication is defined as the minimal complexity of a computable function that defines a two-part description for the string that is shortest within some precision; the second can be defined as the minimal computation time of a program that is shortest within some precision. We show that the Busy Beaver function of the sophistication of a string exceeds its logical depth with logarithmically bigger precision, and that logical depth exceeds the Busy Beaver function of sophistication with logarithmically bigger precision. We also show that this is not true if the precision is only increased by a constant (when the notions are defined with plain Kolmogorov complexity). Finally we show that sophistication is unstable in its precision: constant variations can change its value by a linear term in the length of the string."]]></description>
<dc:subject>complexity_measures algorithmic_information_theory in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:70e58f3eaa99/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:algorithmic_information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.pnas.org/content/110/11/E1006.abstract">
    <title>Statistical method for comparing the level of intracellular organization between cells</title>
    <dc:date>2013-03-13T04:22:14+00:00</dc:date>
    <link>http://www.pnas.org/content/110/11/E1006.abstract</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Systems level approaches to analyzing complex emergent behavior require quantitative characterization of alterations of behavior on both the microscale and macroscale. Here we consider the problem of cellular organization and describe a statistical methodology for quantitative comparison of the internal organization between different populations of similar physical objects, such as cells. This comparison is achieved with several steps of analysis. Starting with three-dimensional or two-dimensional images of cells, images are segmented to identify individual cells. Locations of internal points of interest, such as organelles or proteins, are recorded. To define the configuration of internal points in each cell, the individual cells are subjected to bounded Voronoi tessellation: subdividing the bounded volume or area of the cell into subvolumes determined by the locations of the internal points of interest. A statistical methodology is applied to yield a metric for similarity in degree of organization between populations. We applied this methodology to test whether centrioles play a role in global cellular organization, using mutants of the green alga Chlamydomonas reinhardtii with known alterations in centriole number, structure, and position as a model system. Comparing mutant populations and wild-type populations revealed a dramatic difference in the degree of organization in the mutant strains. These computational and experimental results provide statistical support for prior observational studies and support the idea that centrioles play a role in generating or maintaining global cellular organization. Our results confirm that this method can be used to sensitively compare the extent and type of organization within cells."]]></description>
<dc:subject>to:NB biological_organization biology complexity_measures</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3b30624226cd/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biological_organization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1301.4211">
    <title>[1301.4211] Information-related complexity: a problem-oriented approach</title>
    <dc:date>2013-02-18T19:13:17+00:00</dc:date>
    <link>http://arxiv.org/abs/1301.4211</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A general notion of information-related complexity applicable to both natural and man-made systems is proposed. The overall approach is to explicitly consider a rational agent performing a certain task with a quantifiable degree of success. The complexity is defined as the minimum (quasi-)quantity of information that's necessary to complete the task to the given extent -- measured by the corresponding loss. The complexity so defined is shown to generalize the existing notion of statistical complexity when the system in question can be described by a discrete-time stochastic process. The proposed definition also applies, in particular, to optimization and decision making problems under uncertainty in which case it gives the agent a useful measure of the problem's "susceptibility" to additional information and allows for an estimation of the potential value of the latter."]]></description>
<dc:subject>to:NB complexity_measures information_theory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2319c6abb7a4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1212.3896">
    <title>[1212.3896] Predictive information in a nonequilibrium critical model</title>
    <dc:date>2013-01-13T02:33:35+00:00</dc:date>
    <link>http://arxiv.org/abs/1212.3896</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We propose predictive information, that is information between a long past of duration T and the entire infinitely long future of a time series, as a universal order parameter to study phase transitions in physical systems. It can be used, in particular, to study nonequlibrium transitions and other exotic transitions, where a simpler order parameter cannot be identifies using traditional symmetry arguments. As an example, we calculate predictive information for a stochastic nonequilibrium dynamics problem that forms an absorbing state under a continuous change of a parameter. The information at the transition point diverges as log(T), and a smooth crossover to constant away from the transition is observed."]]></description>
<dc:subject>to:NB statistical_mechanics complexity_measures information_theory phase_transitions</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5065a43d3423/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_mechanics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:phase_transitions"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1212.5843">
    <title>[1212.5843] A geometric method for spatiotemporal coherent structure analysis</title>
    <dc:date>2013-01-03T03:41:33+00:00</dc:date>
    <link>http://arxiv.org/abs/1212.5843</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We describe a geometric method to quantify wave patterns observed in the nervous system, which are non-stationary and with a mixture of spiral, target, plane and irregular waves. The method analyzes fluctuations of the energy angular distribution in two-dimensional Fourier spectrum of wave patterns, which reflects changes of the orientation distribution of wavefronts. We show that the number of the genuine peaks in generalized phase spectrum is close to the number of the coherent space-time clusters arising in wave patterns, and propose to use the number as a complexity measure."]]></description>
<dc:subject>pattern_formation complexity_measures spatio-temporal_statistics re:automatic_pattern_discovery via:vaguery in_NB fourier_analysis</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6e56ed05a23a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:pattern_formation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:spatio-temporal_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:automatic_pattern_discovery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:vaguery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:fourier_analysis"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1212.5841">
    <title>[1212.5841] Data complexity measured by principal graphs</title>
    <dc:date>2012-12-27T18:52:02+00:00</dc:date>
    <link>http://arxiv.org/abs/1212.5841</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["How to measure the complexity of a finite set of vectors embedded in a multidimensional space? This is a non-trivial question which can be approached in many different ways. Here we suggest a set of data complexity measures using universal approximators, principal cubic complexes. Principal cubic complexes generalise the notion of principal manifolds for datasets with non-trivial topologies. The type of the principal cubic complex is determined by its dimension and a grammar of elementary graph transformations. The simplest grammar produces principal trees. 
"We introduce three natural types of data complexity: 1) geometric (deviation of the data's approximator from some "idealized" configuration, such as deviation from harmonicity); 2) structural (how many elements of a principal graph are needed to approximate the data), and 3) construction complexity (how many applications of elementary graph transformations are needed to construct the principal object starting from the simplest one). 
"We compute these measures for several simulated and real-life data distributions and show them in the "accuracy-complexity" plots, helping to optimize the accuracy/complexity ratio. We discuss various issues connected with measuring data complexity. Software for computing data complexity measures from principal cubic complexes is provided as well."]]></description>
<dc:subject>to:NB to_read complexity_measures data_analysis dimension_reduction graph_theory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d210468e5073/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dimension_reduction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graph_theory"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1208.3459">
    <title>[1208.3459] Randomness, Information, and Complexity</title>
    <dc:date>2012-08-24T11:24:02+00:00</dc:date>
    <link>http://arxiv.org/abs/1208.3459</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We review possible measures of complexity which might in particular be applicable to situations where the complexity seems to arise spontaneously. We point out that not all of them correspond to the intuitive (or "naive") notion, and that one should not expect a unique observable of complexity. One of the main problems is to distinguish complex from disordered systems. This and the fact that complexity is closely related to information requires that we also give a review of information measures. We finally concentrate on quantities which measure in some way or other the difficulty of classifying and forecasting sequences of discrete symbols, and study them in simple examples."

- 1989 paper.]]></description>
<dc:subject>have_read complexity_measures grassberger.peter in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:003df7fa07f9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:grassberger.peter"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://pre.aps.org/abstract/PRE/v85/i3/e036206">
    <title>Phys. Rev. E 85, 036206 (2012): Heat diffusion: Thermodynamic depth complexity of networks</title>
    <dc:date>2012-03-18T21:02:02+00:00</dc:date>
    <link>http://pre.aps.org/abstract/PRE/v85/i3/e036206</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this paper we use the Birkhoff–von Neumann decomposition of the diffusion kernel to compute a polytopal measure of graph complexity. We decompose the diffusion kernel into a series of weighted Birkhoff combinations and compute the entropy associated with the weighting proportions (polytopal complexity). The maximum entropy Birkhoff combination can be expressed in terms of matrix permanents. This allows us to introduce a phase-transition principle that links our definition of polytopal complexity to the heat flowing through the network at a given diffusion time. The result is an efficiently computed complexity measure, which we refer to as flow complexity. Moreover, the flow complexity measure allows us to analyze graphs and networks in terms of the thermodynamic depth. We compare our method with three alternative methods described in the literature (Estrada's heterogeneity index, the Laplacian energy, and the von Neumann entropy). Our study is based on 217 protein-protein interaction (PPI) networks including histidine kinases from several species of bacteria. We find a correlation between structural complexity and phylogeny (more evolved species have statistically more complex PPIs). Although our methods outperform the alternatives, we find similarities with Estrada's heterogeneity index in terms of network size independence and predictive power."  ]]></description>
<dc:subject>complexity_measures networks in_NB color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:bb693589a132/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1203.3271">
    <title>[1203.3271] The thermodynamics of prediction</title>
    <dc:date>2012-03-18T16:07:50+00:00</dc:date>
    <link>http://arxiv.org/abs/1203.3271</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A system responding to a stochastic driving signal can be interpreted as computing, by means of its dynamics, an (implicit) model of the environmental variables. The system's state retains information about past environmental fluctuations, and a fraction of this information is predictive of future ones. The remaining nonpredictive information reflects model complexity that does not improve predictive power, and represents the ineffectiveness of the model. We expose the fundamental equivalence between this model inefficiency and thermodynamic inefficiency, measured by the energy dissipated during the interaction between system and environment. Our results hold arbitrarily far from thermodynamic equilibrium and are applicable to a wide range of systems, including biomolecular machines. They highlight a profound connection between the effective use of information and efficient thermodynamic operation: any system constructed to keep memory about its environment and to operate energetically efficiently has to be predictive."

--- Hrmph, time to send some copies of old papers.]]></description>
<dc:subject>to:NB thermodynamics complexity_measures information_theory grumble</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:dac47de416ee/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:thermodynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:grumble"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0810.5663">
    <title>[0810.5663] Effective Complexity and its Relation to Logical Depth</title>
    <dc:date>2012-02-24T16:46:39+00:00</dc:date>
    <link>http://arxiv.org/abs/0810.5663</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Effective complexity measures the information content of the regularities of an object. It has been introduced by M. Gell-Mann and S. Lloyd to avoid some of the disadvantages of Kolmogorov complexity, also known as algorithmic information content. In this paper, we give a precise formal definition of effective complexity and rigorous proofs of its basic properties. In particular, we show that incompressible binary strings are effectively simple, and we prove the existence of strings that have effective complexity close to their lengths. Furthermore, we show that effective complexity is related to Bennett's logical depth: If the effective complexity of a string $x$ exceeds a certain explicit threshold then that string must have astronomically large depth; otherwise, the depth can be arbitrarily small."]]></description>
<dc:subject>kith_and_kin complexity_measures ay.nihat algorithmic_information_theory in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8d54d52943aa/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ay.nihat"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:algorithmic_information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1202.1540">
    <title>[1202.1540] Quantifying the complexity of random Boolean networks</title>
    <dc:date>2012-02-10T18:34:08+00:00</dc:date>
    <link>http://arxiv.org/abs/1202.1540</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We study two measures of the complexity of heterogeneous extended systems, taking random Boolean networks as prototypical cases. A measure defined by Shalizi et al. for cellular automata, based on a criterion for optimal statistical prediction [1], does not distinguish between the spatial inhomogeneity of the ordered phase and the dynamical inhomogeneity of the disordered phase. A modification in which complexities of individual nodes are calculated yields vanishing complexity values for networks in the ordered and critical regimes and for highly disordered networks, peaking somewhere in the disordered regime. Individual nodes with high complexity are the ones that pass the most information from the past to the future, a quantity that depends in a nontrivial way on both the Boolean function of a given node and its location within the network."]]></description>
<dc:subject>complexity_measures random_boolean_networks to_read in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1d1b7d3642e8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:random_boolean_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1111.2845">
    <title>[1111.2845] Natural Complexity, Computational Complexity and Depth</title>
    <dc:date>2011-11-14T17:27:18+00:00</dc:date>
    <link>http://arxiv.org/abs/1111.2845</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>complexity_measures computational_complexity machta.jon in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:575f40f48c91/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:computational_complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:machta.jon"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://chaos.aip.org/resource/1/chaoeh/v21/i3/p037103_s1">
    <title>A geometric approach to complexity | Browse - Chaos</title>
    <dc:date>2011-10-05T18:31:27+00:00</dc:date>
    <link>http://chaos.aip.org/resource/1/chaoeh/v21/i3/p037103_s1</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>complexity_measures information_geometry ay.nihat jost.jurgen kith_and_kin to_read in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:694d42bc6833/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_geometry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ay.nihat"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:jost.jurgen"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://chaos.aip.org/resource/1/chaoeh/v21/i3?&amp;section=focus-issue-randomness-structure-and-causality-measures-of-complexity-from-theory-to-applications&amp;page=1">
    <title>Focus Issue: Randomness, Structure, and Causality: Measures of Complexity from Theory to Applications</title>
    <dc:date>2011-10-01T23:07:46+00:00</dc:date>
    <link>http://chaos.aip.org/resource/1/chaoeh/v21/i3?&amp;section=focus-issue-randomness-structure-and-causality-measures-of-complexity-from-theory-to-applications&amp;page=1</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>complexity_measures to_read machta.jon ay.nihat jost.jurgen kith_and_kin krakauer.david crutchfield.james_p. in_NB feldman.david_p.</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b30f7400b2b0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:machta.jon"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ay.nihat"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:jost.jurgen"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:krakauer.david"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:crutchfield.james_p."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:feldman.david_p."/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1108.3984">
    <title>[1108.3984] Process Dimension of Classical and Non-Commutative Processes</title>
    <dc:date>2011-08-22T14:08:23+00:00</dc:date>
    <link>http://arxiv.org/abs/1108.3984</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We treat observable operator models (OOM) and their non-commutative generalisation, which we call NC-OOMs. A natural characteristic of a stochastic process in the context of classical OOM theory is the process dimension. We investigate its properties within the more general formulation, which allows to consider process dimension as a measure of complexity of non-commutative processes: We prove lower semi-continuity, and derive an ergodic decomposition formula. Further, we obtain results on the close relationship between the canonical OOM and the concept of causal states which underlies the definition of statistical complexity. In particular, the topological statistical complexity, i.e. the logarithm of the number of causal states, turns out to be an upper bound to the logarithm of process dimension."
]]></description>
<dc:subject>complexity_measures observable_operator_models ay.nihat kith_and_kin to_read re:AoS_project lohr.wolfgang predictive_states in_NB</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:bfb7dc2cdd00/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:observable_operator_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ay.nihat"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:AoS_project"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:lohr.wolfgang"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:predictive_states"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://pre.aps.org/abstract/PRE/v83/i4/e041906">
    <title>Phys. Rev. E 83, 041906 (2011): Neural complexity: A graph theoretic interpretation</title>
    <dc:date>2011-04-18T01:34:22+00:00</dc:date>
    <link>http://pre.aps.org/abstract/PRE/v83/i4/e041906</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>complexity_measures networks in_NB</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a274ea9009d8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://philsci-archive.pitt.edu/8496/">
    <title>What is a complex system? - PhilSci-Archive</title>
    <dc:date>2011-03-03T01:52:15+00:00</dc:date>
    <link>http://philsci-archive.pitt.edu/8496/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Unless this has changed drastically from the version Karoline showed me in Bristol in October.
]]></description>
<dc:subject>complexity_measures complexity kith_and_kin have_read wiesner.karoline</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:aa2d09919e23/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:wiesner.karoline"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1011.5334">
    <title>[1011.5334] A Graph Theoretic Interpretation of Neural Complexity</title>
    <dc:date>2010-12-13T22:14:56+00:00</dc:date>
    <link>http://arxiv.org/abs/1011.5334</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>complexity_measures information_theory to_read color_me_skeptical</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a76066b2350c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1012.1890">
    <title>[1012.1890] A measure of statistical complexity based on predictive information</title>
    <dc:date>2010-12-13T22:13:55+00:00</dc:date>
    <link>http://arxiv.org/abs/1012.1890</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We introduce an information theoretic measure of statistical structure, called 'binding information', for sets of random variables, and compare it with several previously proposed measures including excess entropy, Bialek et al.'s predictive information, and the multi-information. We derive some of the properties of the binding information, particularly in relation to the multi-information, and show that, for finite sets of binary random variables, the processes which maximises binding information are the 'parity' processes. Finally we discuss some of the implications this has for the use of the binding information as a measure of complexity."
]]></description>
<dc:subject>complexity_measures to_read color_me_skeptical</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b521fdcd47c9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0806.2552">
    <title>[0806.2552] Complexity Measures from Interaction Structures</title>
    <dc:date>2010-11-28T19:02:10+00:00</dc:date>
    <link>http://arxiv.org/abs/0806.2552</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We evaluate new complexity measures on the symbolic dynamics of coupled tent maps and cellular automata. These measures quantify complexity in terms of $k$-th order statistical dependencies that cannot be reduced to interactions between $k-1$ units. We demonstrate that these measures are able to identify complex dynamical regimes."
]]></description>
<dc:subject>complexity_measures information_theory information_geometry ay.nihat jost.jurgen</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:482c3097e866/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_geometry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ay.nihat"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:jost.jurgen"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.cs.utk.edu/~huangj/vis09/janicke_tutorial.pdf">
    <title>Information-Theoretic Methods for the Visual Analysis of Climate and Flow Data (Tutorial Slides)</title>
    <dc:date>2010-10-06T19:05:29+00:00</dc:date>
    <link>http://www.cs.utk.edu/~huangj/vis09/janicke_tutorial.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[My reaction to this must, I imagine, be a little bit like how a proud parent feels when they hear from someone else about their child doing something worthwhile.
]]></description>
<dc:subject>visual_display_of_quantitative_information complexity_measures computational_mechanics janicke.heiki to:blog via:georg</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4cb8ff268d67/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:computational_mechanics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:janicke.heiki"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:blog"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:georg"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://pre.aps.org/abstract/PRE/v79/i2/e026201">
    <title>Phys. Rev. E 79, 026201 (2009): Complexity measures from interaction structures</title>
    <dc:date>2010-09-10T12:22:57+00:00</dc:date>
    <link>http://pre.aps.org/abstract/PRE/v79/i2/e026201</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We evaluate information-theoretic quantities that quantify complexity in terms of kth-order statistical dependences that cannot be reduced to interactions among k−1 random variables. Using symbolic dynamics of coupled maps and cellular automata as model systems, we demonstrate that these measures are able to identify complex dynamical regimes."
]]></description>
<dc:subject>complexity_measures information_theory kith_and_kin ay.nihat jost.jurgen to_read re:stacs</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f04e5087c8ba/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ay.nihat"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:jost.jurgen"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:stacs"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://cs.swan.ac.uk/~csheike/Data/dag07.pdf">
    <title>Towards Automatic Feature-based Visualization</title>
    <dc:date>2010-08-29T20:23:34+00:00</dc:date>
    <link>http://cs.swan.ac.uk/~csheike/Data/dag07.pdf</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>visual_display_of_quantitative_information fluid_mechanics markov_models information_theory complexity_measures have_read</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6b3484ff1852/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:fluid_mechanics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:markov_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.sciencedirect.com/science?_ob=ArticleURL&amp;_udi=B6TYG-4WS2HX2-1&amp;_user=10&amp;_coverDate=10%2F31%2F2009&amp;_rdoc=1&amp;_fmt=high&amp;_orig=search&amp;_origin=search&amp;_sort=d&amp;_docanchor=&amp;view=c&amp;_rerunOrigin=scholar.google&amp;_acct=C000050221&amp;_version=1&amp;_urlVersion=0&amp;_userid=10&amp;md5=25a55951d410834ebc2d488c9bca496b&amp;searchtype=a">
    <title>ScienceDirect - Computers &amp; Graphics : Steady visualization of the dynamics in fluids using ε-machines</title>
    <dc:date>2010-08-29T20:20:54+00:00</dc:date>
    <link>http://www.sciencedirect.com/science?_ob=ArticleURL&amp;_udi=B6TYG-4WS2HX2-1&amp;_user=10&amp;_coverDate=10%2F31%2F2009&amp;_rdoc=1&amp;_fmt=high&amp;_orig=search&amp;_origin=search&amp;_sort=d&amp;_docanchor=&amp;view=c&amp;_rerunOrigin=scholar.google&amp;_acct=C000050221&amp;_version=1&amp;_urlVersion=0&amp;_userid=10&amp;md5=25a55951d410834ebc2d488c9bca496b&amp;searchtype=a</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>fluid_mechanics visual_display_of_quantitative_information markov_models re:stacs information_theory complexity_measures have_read</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:413f8cc06d62/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:fluid_mechanics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:markov_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:stacs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0712.4216">
    <title>[0712.4216] Offdiagonal complexity: A computationally quick network complexity measure. Application to protein networks and cell division</title>
    <dc:date>2010-08-29T20:12:07+00:00</dc:date>
    <link>http://arxiv.org/abs/0712.4216</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>complexity_measures networks</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:9404dbef3a42/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://pre.aps.org/abstract/PRE/v82/i2/e026117">
    <title>Phys. Rev. E 82, 026117 (2010): Self-assembly, modularity, and physical complexity</title>
    <dc:date>2010-08-27T20:32:44+00:00</dc:date>
    <link>http://pre.aps.org/abstract/PRE/v82/i2/e026117</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>complexity_measures to_read self-organization</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1fa70bb2b369/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:self-organization"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0911.3482">
    <title>[0911.3482] Complexity of Networks (reprise)</title>
    <dc:date>2010-08-26T16:50:30+00:00</dc:date>
    <link>http://arxiv.org/abs/0911.3482</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>complexity_measures networks</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:539ac4ec28a6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1007.1829">
    <title>[1007.1829] Topological reversibility and causality in feed-forward networks</title>
    <dc:date>2010-07-13T18:21:31+00:00</dc:date>
    <link>http://arxiv.org/abs/1007.1829</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[The abstract makes this sound closely akin to good old-fashioned Lloyd-Pagels thermodynamic depth.
]]></description>
<dc:subject>complexity_measures irreversibility causality sole.ricard color_me_skeptical</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:50b9201c06d7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:irreversibility"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sole.ricard"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0902.1209">
    <title>[0902.1209] Time's Barbed Arrow: Irreversibility, Crypticity, and Stored Information</title>
    <dc:date>2010-05-31T00:50:57+00:00</dc:date>
    <link>http://arxiv.org/abs/0902.1209</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>complexity_measures to_read crutchfield.james_p.</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3bbc76af1660/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:crutchfield.james_p."/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1003.3028">
    <title>[1003.3028] Quantifying Emergence in term of Persistent Mutual Information</title>
    <dc:date>2010-03-17T13:53:36+00:00</dc:date>
    <link>http://arxiv.org/abs/1003.3028</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>emergence complexity_measures to_read</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0bbd1a83c8d8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:emergence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1001.2686">
    <title>[1001.2686] Effective complexity of stationary process realizations</title>
    <dc:date>2010-01-18T13:44:40+00:00</dc:date>
    <link>http://arxiv.org/abs/1001.2686</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>algorithmic_information_theory complexity_measures kith_and_kin ay.nihat stochastic_processes in_NB</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:972274e7c9ea/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:algorithmic_information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ay.nihat"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0908.2248">
    <title>[0908.2248] Mapping from Architecture to Dynamics: A Unified View of Dynamical Processes on Networks</title>
    <dc:date>2009-08-26T20:45:36+00:00</dc:date>
    <link>http://arxiv.org/abs/0908.2248</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>networks dynamical_systems complexity_measures color_me_skeptical</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:63ea05ddd422/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0908.3447">
    <title>[0908.3447] Recurrence networks - A novel paradigm for nonlinear time series analysis</title>
    <dc:date>2009-08-25T14:02:44+00:00</dc:date>
    <link>http://arxiv.org/abs/0908.3447</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>dynamical_systems recurrence_times networks complexity_measures time_series color_me_skeptical</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d825903acb0a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:recurrence_times"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0905.2918">
    <title>[0905.2918] Information erasure lurking behind measures of complexity</title>
    <dc:date>2009-05-19T16:13:25+00:00</dc:date>
    <link>http://arxiv.org/abs/0905.2918</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[*dramatic sigh* Shalizi and Moore (2003) *dramatic sigh*
]]></description>
<dc:subject>complexity_measures prediction information_theory</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f8f8c527cc63/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.pnas.org/content/105/48/18970.abstract">
    <title>Memory traces in dynamical systems — PNAS</title>
    <dc:date>2008-12-16T18:20:39+00:00</dc:date>
    <link>http://www.pnas.org/content/105/48/18970.abstract</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[How much information (in the Fisher sense) does the present state of a recurrent dynamical network retain about the history of its inputs?  All, or almost all, done for linear-Gaussian systems, but numerical results for nonlinear, non-Gaussian systems would be straightforward in principle.
]]></description>
<dc:subject>memory dynamical_systems information_theory complexity_measures fisher_information to_teach:complexity-and-inference re:stacs in_NB</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3d4cfe141027/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:memory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:fisher_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:complexity-and-inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:stacs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0812.1949">
    <title>[0812.1949] Prediction with Restricted Resources and Finite Automata</title>
    <dc:date>2008-12-12T02:46:34+00:00</dc:date>
    <link>http://arxiv.org/abs/0812.1949</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We obtain an index of the complexity of a random sequence by allowing the role of the measure in classical probability theory to be played by a function we call the generating mechanism. Typically, this generating mechanism will be a finite automata. We generate a set of biased sequences by applying a finite state automata with a specified number, $m$, of states to the set of all binary sequences. Thus we can index the complexity of our random sequence by the number of states of the automata. We detail optimal algorithms to predict sequences generated in this way."
]]></description>
<dc:subject>prediction automata_theory complexity_measures to_read color_me_skeptical</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:92a34f0ee35a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:automata_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>