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  </channel><item rdf:about="https://direct.mit.edu/books/oa-edited-volume/6112/Dennett-s-Real-Patterns-in-Science-and-Nature">
    <title>Dennett's Real Patterns in Science and Nature | Books Gateway | MIT Press</title>
    <dc:date>2026-04-08T17:36:05+00:00</dc:date>
    <link>https://direct.mit.edu/books/oa-edited-volume/6112/Dennett-s-Real-Patterns-in-Science-and-Nature</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["How the concept of a pattern, as understood in information science and applied in contemporary AI, can address deep questions in science and philosophy.
"The explosive growth of AI and machine learning in recent decades is predicated on the recognition and exploitation of patterns in data. Of course, scientists have engaged in their own—less automated—processes of pattern recognition since the birth of science itself, and biological organisms evolved their own neural networks for pattern recognition long before people and their technology came along.
"In his seminal work, “Real Patterns,” philosopher and cognitive scientist Daniel Dennett laid out a road map for connecting the idea of “patterns” as understood by information theory to the practices of scientists and to our own cognitive capacity to model and predict the world around us. In this book—the first dedicated to the topic of real patterns—Tyler Millhouse, Steve Petersen, and Don Ross follow this road map. They explore the relevance of patterns to important aspects of both science and nature, including the emergence of high-level structure in physics, the nature of biological species, the measurement of welfare in economics, the evaluation of causal models, and the possibility of understanding in large neural networks."]]></description>
<dc:subject>books:noted philosophy_of_science emergence dennett.daniel_c. to_read downloaded in_NB</dc:subject>
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<item rdf:about="https://arxiv.org/abs/2506.11135">
    <title>[2506.11135] Large Language Models and Emergence: A Complex Systems Perspective</title>
    <dc:date>2025-07-01T14:46:18+00:00</dc:date>
    <link>https://arxiv.org/abs/2506.11135</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Emergence is a concept in complexity science that describes how many-body systems manifest novel higher-level properties, properties that can be described by replacing high-dimensional mechanisms with lower-dimensional effective variables and theories. This is captured by the idea "more is different". Intelligence is a consummate emergent property manifesting increasingly efficient -- cheaper and faster -- uses of emergent capabilities to solve problems. This is captured by the idea "less is more". In this paper, we first examine claims that Large Language Models exhibit emergent capabilities, reviewing several approaches to quantifying emergence, and secondly ask whether LLMs possess emergent intelligence."]]></description>
<dc:subject>have_read artificial_intelligence emergence large_language_models_(so_called) kith_and_kin mitchell.melanie krakauer.david in_NB to_teach:statistics_and_generative_ai</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ec7043fdf39c/</dc:identifier>
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<item rdf:about="https://duncanlaw.wordpress.com/2022/08/25/marxs-negative-catallactics/">
    <title>Marx’s negative catallactics |</title>
    <dc:date>2024-03-23T17:52:43+00:00</dc:date>
    <link>https://duncanlaw.wordpress.com/2022/08/25/marxs-negative-catallactics/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>have_read marx.karl emergence socialist_calculation_debate via:henry_farrell</dc:subject>
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<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>
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<dc:identifier>https://pinboard.in/u:cshalizi/b:ebde8153c608/</dc:identifier>
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<item rdf:about="https://www.nature.com/articles/s41598-017-00810-8">
    <title>Formal Definitions of Unbounded Evolution and Innovation Reveal Universal Mechanisms for Open-Ended Evolution in Dynamical Systems | Scientific Reports</title>
    <dc:date>2023-01-23T03:35:12+00:00</dc:date>
    <link>https://www.nature.com/articles/s41598-017-00810-8</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Open-ended evolution (OEE) is relevant to a variety of biological, artificial and technological systems, but has been challenging to reproduce in silico. Most theoretical efforts focus on key aspects of open-ended evolution as it appears in biology. We recast the problem as a more general one in dynamical systems theory, providing simple criteria for open-ended evolution based on two hallmark features: unbounded evolution and innovation. We define unbounded evolution as patterns that are non-repeating within the expected Poincare recurrence time of an isolated system, and innovation as trajectories not observed in isolated systems. As a case study, we implement novel variants of cellular automata (CA) where the update rules are allowed to vary with time in three alternative ways. Each is capable of generating conditions for open-ended evolution, but vary in their ability to do so. We find that state-dependent dynamics, regarded as a hallmark of life, statistically out-performs other candidate mechanisms, and is the only mechanism to produce open-ended evolution in a scalable manner, essential to the notion of ongoing evolution. This analysis suggests a new framework for unifying mechanisms for generating OEE with features distinctive to life and its artifacts, with broad applicability to biological and artificial systems."]]></description>
<dc:subject>to:NB artificial_intelligence dynamical_systems emergence color_me_skeptical via:rvenkat</dc:subject>
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<dc:identifier>https://pinboard.in/u:cshalizi/b:55f81a741912/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/2106.06511">
    <title>[2106.06511] Dynamical independence: discovering emergent macroscopic processes in complex dynamical systems</title>
    <dc:date>2021-06-18T16:55:43+00:00</dc:date>
    <link>https://arxiv.org/abs/2106.06511</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We introduce a notion of emergence for coarse-grained macroscopic variables associated with highly-multivariate microscopic dynamical processes, in the context of a coupled dynamical environment. 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 emphasise 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 via state-space modelling, in both time and frequency domains, facilitating discovery of emergent phenomena at all spatiotemporal scales. We discuss application of the state-space operationalisation to inference of the emergence portrait for neural systems from neurophysiological time-series data. We also examine 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."

--- *ahem* https://arxiv.org/abs/cond-mat/0303625 *ahem*]]></description>
<dc:subject>to:NB to_read emergence macro_from_micro information_theory via:cris_moore abstraction</dc:subject>
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<dc:identifier>https://pinboard.in/u:cshalizi/b:4229df5da19b/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/2105.03470">
    <title>[2105.03470] Microscopic Origins of Macroscopic Behavior</title>
    <dc:date>2021-05-12T18:34:05+00:00</dc:date>
    <link>https://arxiv.org/abs/2105.03470</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This article is mostly based on a talk I gave at the March 2021 meeting (virtual) of the American Physical Society on the occasion of receiving the Dannie Heineman prize for Mathematical Physics from the American Institute of Physics and the American Physical Society."]]></description>
<dc:subject>to:NB lebowitz.joel statistical_mechanics emergence macro_from_micro arrow_of_time</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:661989fcf13d/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/2104.01242">
    <title>[2104.01242] Major Cooperative Transitions and Management Theory in the Game of Life</title>
    <dc:date>2021-04-08T14:24:34+00:00</dc:date>
    <link>https://arxiv.org/abs/2104.01242</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Biological and cultural evolution show a trend towards increasing hierarchical organization, in which entities at one level combine cooperatively to form a new entity at a higher level of organization. In each case where such a cooperative transition has been studied, we have some understanding of how the transition came about, but it is difficult to formulate a unified theory that covers all of these transitions. John Stewart has proposed a theoretical framework called Management Theory, which attempts to explain all of the major cooperative transitions in biological and cultural evolution. The idea is that successful transitions require the integration of managers and workers into a cooperative organization. This theory seems appropriate when we consider the cultural evolution of corporations, where managers and workers are clearly essential, but it seems less plausible when we consider the biological evolution of entities that do not invite anthropomorphic projection. However, in the following article, we define managers and workers in an abstract way that enables us to apply these terms over a broad range of cases, including cultural evolution, biological evolution, and computational simulations of evolution. The core idea is that a worker is an entity that takes the main role in the production of something and a manager is an entity that plays a supporting role in the production of something. We apply this abstract view of managers and workers to a computational simulation of evolving cooperative transitions in John Conway's Game of Life. The simulation confirms the expectations of Management Theory: Manager-worker relations result in robust and productive cooperation, whereas workers without managers tend to lack robustness, and managers without workers tend to lack productivity."

]]></description>
<dc:subject>to:NB cellular_automata emergence color_me_skeptical self-organization</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:00d15ea43533/</dc:identifier>
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<item rdf:about="https://www.journals.uchicago.edu/doi/abs/10.1086/710027">
    <title>Compressibility and the Reality of Patterns | Philosophy of Science: Vol 88, No 1</title>
    <dc:date>2021-03-24T20:38:10+00:00</dc:date>
    <link>https://www.journals.uchicago.edu/doi/abs/10.1086/710027</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Daniel Dennett distinguishes real patterns from bogus patterns by appeal to compressibility. As information theorists have shown, data are compressible if and only if those data exhibit a pattern. Noting that high-level models are much simpler than their low-level counterparts, Dennett interprets high-level models as compressed representations of the fine-grained behavior of their target system. As such, he argues that high-level models depend on patterns in this behavior. Unfortunately, data scientific practice complicates Dennett’s interpretation, undermining the traditional justification for real patterns and suggesting a revised research program for its defenders."]]></description>
<dc:subject>to:NB to_read patterns information_theory philosophy_of_science emergence dennett.daniel</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:021006e7b90f/</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:patterns"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:emergence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dennett.daniel"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2010.09390">
    <title>[2010.09390] Causal Geometry</title>
    <dc:date>2021-01-12T20:57:02+00:00</dc:date>
    <link>https://arxiv.org/abs/2010.09390</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Information geometry has offered a way to formally study the efficacy of scientific models by quantifying the impact of model parameters on the predicted effects. However, there has been little formal investigation of causation in this framework, despite causal models being a fundamental part of science and explanation. Here we introduce causal geometry, which formalizes not only how outcomes are impacted by parameters, but also how the parameters of a model can be intervened upon. Therefore we introduce a geometric version of "effective information" -- a known measure of the informativeness of a causal relationship. We show that it is given by the matching between the space of effects and the space of interventions, in the form of their geometric congruence. Therefore, given a fixed intervention capability, an effective causal model is one that matches those interventions. This is a consequence of "causal emergence," wherein macroscopic causal relationships may carry more information than "fundamental" microscopic ones. We thus argue that a coarse-grained model may, paradoxically, be more informative than the microscopic one, especially when it better matches the scale of accessible interventions -- as we illustrate on toy examples."]]></description>
<dc:subject>to:NB information_geometry emergence causality color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f87cabb88637/</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_geometry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:emergence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<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/1907.03902">
    <title>[1907.03902] Uncertainty and causal emergence in complex networks</title>
    <dc:date>2019-08-19T16:04:59+00:00</dc:date>
    <link>https://arxiv.org/abs/1907.03902</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The connectivity of a network conveys information about the dependencies between nodes. We show that this information can be analyzed by measuring the uncertainty (and certainty) contained in paths along nodes and links in a network. Specifically, we derive from first principles a measure known as effective information and describe its behavior in common network models. Networks with higher effective information contain more information within the dependencies between nodes. We show how subgraphs of nodes can be grouped into macro-nodes, reducing the size of a network while increasing its effective information, a phenomenon known as causal emergence. We find that causal emergence is common in simulated and real networks across biological, social, informational, and technological domains. Ultimately, these results show that the emergence of higher scales in networks can be directly assessed, and that these higher scales offer a way to create certainty out of uncertainty."]]></description>
<dc:subject>to:NB information_theory network_data_analysis emergence via:vaguery color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e545d74064d3/</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:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:emergence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:vaguery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.talyarkoni.org/blog/2018/08/18/if-we-already-understood-the-brain-would-we-even-know-it/">
    <title>If we already understood the brain, would we even know it? – [citation needed]</title>
    <dc:date>2018-08-20T19:55:15+00:00</dc:date>
    <link>http://www.talyarkoni.org/blog/2018/08/18/if-we-already-understood-the-brain-would-we-even-know-it/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["What I’m suggesting is that, when we say things like “we don’t really understand the brain yet”, we’re not really expressing factual statements about the collective sum of neuroscience knowledge currently held by all human beings. What each of us really means is something more like there are questions I personally am able to pose about the brain that seem to make sense in my head, but that I don’t currently know the answer to–and I don’t think I could piece together the answer even if you handed me a library of books containing all of the knowledge we’ve accumulated about the brain."

]]></description>
<dc:subject>have_read complexity emergence explanation neuroscience yarkoni.tal</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:517b1fc1e88a/</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"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:emergence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:explanation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neuroscience"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:yarkoni.tal"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.argmin.net/2018/01/25/optics/">
    <title>Lessons from Optics, The Other Deep Learning – arg min blog</title>
    <dc:date>2018-02-02T21:05:57+00:00</dc:date>
    <link>http://www.argmin.net/2018/01/25/optics/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>neural_networks optics design emergence rahimi.ali have_read abstraction your_favorite_deep_neural_network_sucks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a109413f900e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neural_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:optics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:emergence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:rahimi.ali"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:abstraction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:your_favorite_deep_neural_network_sucks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.aeaweb.org/articles?id=10.1257/jel.20151372">
    <title>Ontology, Methodological Individualism, and the Foundations of the Social Sciences</title>
    <dc:date>2016-12-13T18:16:27+00:00</dc:date>
    <link>https://www.aeaweb.org/articles?id=10.1257/jel.20151372</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This is a review essay based on a critical assessment of The Ant Trap: Rebuilding the Foundations of the Social Sciences by Brian Epstein. Epstein argues that models in the social sciences are inadequate because they are based on a false ontology of methodological individualism, and proposes a new model of social ontology. I examine this model and point to flaws in it. More generally, I argue against Epstein's methodological approach, which treats social ontology as prior to social scientific modeling and as certifying the "building blocks" that modelers then use. I argue that modelers can legitimately shape the building blocks for their own models."]]></description>
<dc:subject>to_read book_reviews social_science_methodology emergence</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:62cc17ee099b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:book_reviews"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_science_methodology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:emergence"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://edinburghuniversitypress.com/book-assemblage-theory.html">
    <title>Assemblage Theory - Edinburgh University Press</title>
    <dc:date>2016-12-07T20:08:40+00:00</dc:date>
    <link>https://edinburghuniversitypress.com/book-assemblage-theory.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Manuel DeLanda provides the first detailed overview of the assemblage theory found in germ in Deleuze and Guattari’s writings. Through a series of case studies DeLanda shows how the concept can be applied to economic, linguistic and military history as well as to metaphysics, science and mathematics.
"DeLanda then presents the real power of assemblage theory by advancing it beyond its original formulation – allowing for the integration of communities, institutional organisations, cities and urban regions. And he challenges Marxist orthodoxy with a Leftist politics of assemblages."]]></description>
<dc:subject>to:NB books:noted barely-comprehensible_metaphysics philosophy_of_science emergence delanda.manuel</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d25a424da1c3/</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:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:barely-comprehensible_metaphysics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:emergence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:delanda.manuel"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.pitt.edu/~pittcntr/Events/All/Conferences/others/other_conf_2015-16/10-02-15_scalemodel/scalemodel.html">
    <title>Center for Philosophy of Science ::: Effective Theories, Mixed Scale Modeling, and Emergence :::</title>
    <dc:date>2015-05-26T18:40:30+00:00</dc:date>
    <link>http://www.pitt.edu/~pittcntr/Events/All/Conferences/others/other_conf_2015-16/10-02-15_scalemodel/scalemodel.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Calling for abstracts on multiscale models, effective theories, and emergence with a main focus on relations between theories and models at different scales. 
"This will be an open call conference bringing together philosophers interested in modeling, effective theories, emergence and reduction with scientists and applied mathematicians working on analytic and computational multiscale techniques.
"How can data be extracted from observations of systems at a variety of spatial and temporal scales and then be combined to understand phenomena without any attempt to reduce the theories or models appropriate at some scale to those appropriate at another? Many such "mixed-level" explanations are, it seems, essential to successful scientific investigation. Multiscale modeling is playing an increasing role in many areas of science, including climate science, materials science, and developmental biology. This work suggests that interesting methods have by and large been overlooked by philosophers who primarily treat modeling (and intertheory relations) as restricted to two (spatial) scales---the "macroscopic" and the "microscopic." One aim of the conference is to consider the implication of recent work on the nature of multiscale modeling for our understanding of material behaviors, effective theories, and the kind of autonomy that often accompanies claims about emergence."

--- Goldenfeld and Kadanoff as speakers...]]></description>
<dc:subject>conferences philosophy_of_science renormalization macro_from_micro physics emergence</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e4cd8728a8a7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:conferences"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:renormalization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:physics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:emergence"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://global.oup.com/academic/product/the-ant-trap-9780199381104?cc=us&amp;lang=en&amp;#">
    <title>The Ant Trap - Brian Epstein - Oxford University Press</title>
    <dc:date>2015-04-21T20:27:59+00:00</dc:date>
    <link>https://global.oup.com/academic/product/the-ant-trap-9780199381104?cc=us&amp;lang=en&amp;#</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We live in a world of crowds and corporations, artworks and artifacts, legislatures and languages, money and markets. These are all social objects - they are made, at least in part, by people and by communities. But what exactly are these things? How are they made, and what is the role of people in making them?
"In The Ant Trap, Brian Epstein rewrites our understanding of the nature of the social world and the foundations of the social sciences. Epstein explains and challenges the three prevailing traditions about how the social world is made. One tradition takes the social world to be built out of people, much as traffic is built out of cars. A second tradition also takes people to be the building blocks of the social world, but focuses on thoughts and attitudes we have toward one another. And a third tradition takes the social world to be a collective projection onto the physical world. Epstein shows that these share critical flaws. Most fundamentally, all three traditions overestimate the role of people in building the social world: they are overly anthropocentric.
"Epstein starts from scratch, bringing the resources of contemporary metaphysics to bear. In the place of traditional theories, he introduces a model based on a new distinction between the grounds and the anchors of social facts. Epstein illustrates the model with a study of the nature of law, and shows how to interpret the prevailing traditions about the social world. Then he turns to social groups, and to what it means for a group to take an action or have an intention. Contrary to the overwhelming consensus, these often depend on more than the actions and intentions of group members."]]></description>
<dc:subject>to:NB books:noted philosophy_of_science social_science_methodology emergence institutions</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2ca04a788ba8/</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:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_science_methodology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:emergence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:institutions"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://mitpress.mit.edu/books/intelligence-emerging">
    <title>Intelligence Emerging | The MIT Press</title>
    <dc:date>2015-03-16T03:38:41+00:00</dc:date>
    <link>http://mitpress.mit.edu/books/intelligence-emerging</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Emergence—the formation of global patterns from solely local interactions—is a frequent and fascinating theme in the scientific literature both popular and academic. In this book, Keith Downing undertakes a systematic investigation of the widespread (if often vague) claim that intelligence is an emergent phenomenon. Downing focuses on neural networks, both natural and artificial, and how their adaptability in three time frames—phylogenetic (evolutionary), ontogenetic (developmental), and epigenetic (lifetime learning)—underlie the emergence of cognition. Integrating the perspectives of evolutionary biology, neuroscience, and artificial intelligence, Downing provides a series of concrete examples of neurocognitive emergence. Doing so, he offers a new motivation for the expanded use of bio-inspired concepts in artificial intelligence (AI), in the subfield known as Bio-AI.
"One of Downing’s central claims is that two key concepts from traditional AI, search and representation, are key to understanding emergent intelligence as well. He first offers introductory chapters on five core concepts: emergent phenomena, formal search processes, representational issues in Bio-AI, artificial neural networks (ANNs), and evolutionary algorithms (EAs). Intermediate chapters delve deeper into search, representation, and emergence in ANNs, EAs, and evolving brains. Finally, advanced chapters on evolving artificial neural networks and information-theoretic approaches to assessing emergence in neural systems synthesize earlier topics to provide some perspective, predictions, and pointers for the future of Bio-AI."]]></description>
<dc:subject>books:noted artificial_intelligence neural_networks genetic_algorithms cognitive_science emergence in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4ecafe377a4a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:artificial_intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neural_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:genetic_algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:emergence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.mitpressjournals.org/doi/abs/10.1162/ARTL_a_00143">
    <title>Characterizing Autopoiesis in the Game of Life</title>
    <dc:date>2015-02-15T20:13:58+00:00</dc:date>
    <link>http://www.mitpressjournals.org/doi/abs/10.1162/ARTL_a_00143</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Maturana and Varela's concept of autopoiesis defines the essential organization of living systems and serves as a foundation for their biology of cognition and the enactive approach to cognitive science. As an initial step toward a more formal analysis of autopoiesis, this article investigates its application to the compact, recurrent spatiotemporal patterns that arise in Conway's Game-of-Life cellular automaton. In particular, we demonstrate how such entities can be formulated as self-constructing networks of interdependent processes that maintain their own boundaries. We then characterize the specific organizations of several such entities, suggest a way to simplify the descriptions of these organizations, and briefly consider the transformation of such organizations over time."]]></description>
<dc:subject>to:NB self-organization cellular_automata emergence artificial_life beer.randall</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:199f46fdc3f6/</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:self-organization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cellular_automata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:emergence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:artificial_life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:beer.randall"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.pitt.edu/~pittcntr/Events/All/Conferences/others/other_conf_2014-15/2-7-15_emergence/emergence.html">
    <title>Multiscale Modeling and Emergence</title>
    <dc:date>2014-12-01T16:55:42+00:00</dc:date>
    <link>http://www.pitt.edu/~pittcntr/Events/All/Conferences/others/other_conf_2014-15/2-7-15_emergence/emergence.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["There has been much interest of late in issues of emergence and reduction in the philosophy of science literature. The battle line is largely drawn between reductive "bottom-up" modeling and "top-down" modeling employing so-called "phenomenological" theories. This workshop aims to examine the nature and plausibility of structuring the debate in this way. We bring physicists and mathematicians together with philosophers interested in modeling systems across scales. Multiscale models and beginning to succeed in showing how to upscale from statistical/atomistic models to continuum/hydrodynamic models. A proper understanding of the mathematics involved in such multiscale modeling should show how overly simplified the philosophical debates have been and should refocus the debate on questions of explaining the (relative) autonomy of upper scale models and theories."]]></description>
<dc:subject>emergence conferences macro_from_micro philosophy_of_science pittsburgh</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:dbf121310712/</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:conferences"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:pittsburgh"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.springer.com/physics/history+%26+philosophical+foundations+of+physics/book/978-3-319-06360-7">
    <title>Reductionism, Emergence and Levels of Reality - The Importance of Being Borderline</title>
    <dc:date>2014-06-04T17:35:42+00:00</dc:date>
    <link>http://www.springer.com/physics/history+%26+philosophical+foundations+of+physics/book/978-3-319-06360-7</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Scientists have always attempted to explain the world in terms of a few unifying principles. In the fifth century B.C. Democritus boldly claimed that reality is simply a collection of indivisible and eternal parts or atoms. Over the centuries his doctrine has remained a landmark, and much progress in physics is due to its distinction between subjective perception and objective reality. This book discusses theory reduction in physics, which states that the whole is nothing more than the sum of its parts: the properties of things are directly determined by their constituent parts. Reductionism deals with the relation between different theories that address different levels of reality, and uses extrapolations to apply that relation in different sciences. Reality shows a complex structure of connections, and the dream of a unified interpretation of all phenomena in several simple laws continues to attract anyone with genuine philosophical and scientific interests. If the most radical reductionist point of view is correct, the relationship between disciplines is strictly inclusive: chemistry becomes physics, biology becomes chemistry, and so on. Eventually, only one science, indeed just a single theory, would survive, with all others merging in the Theory of Everything. Is the current coexistence of different sciences a mere historical venture which will end when the Theory of Everything has been established? Can there be a unified description of nature? 
"Rather than an analysis of full reductionism, this book focuses on aspects of theory reduction in physics and stimulates reflection on related questions: is there any evidence of actual reduction? Are the examples used in the philosophy of science too simplistic? What has been endangered by the search for (the) ultimate truth? Has the dream of reductionist reason created any monsters? Is big science one such monster? What is the point of embedding science Y within science X, if predictions cannot be made on that basis?"]]></description>
<dc:subject>books:noted philosophy_of_science emergence reductionism statistical_mechanics philosophy_of_science_by_scientists physics quantum_mechanics chemistry in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ec742dea9185/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:emergence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:reductionism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_mechanics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science_by_scientists"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:physics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:quantum_mechanics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:chemistry"/>
	<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/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://orgtheory.wordpress.com/2013/09/04/a-word-on-critical-realism/">
    <title>A Word on Critical Realism | orgtheory.net</title>
    <dc:date>2013-09-05T03:16:47+00:00</dc:date>
    <link>http://orgtheory.wordpress.com/2013/09/04/a-word-on-critical-realism/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Unsurprisingly, Kieran talks sense.  (Do go read his paper.)]]></description>
<dc:subject>social_science_methodology emergence philosophy_of_science healy.kieran</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4f91adf32d3f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_science_methodology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:emergence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:healy.kieran"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1303.6738">
    <title>[1303.6738] Parameter Space Compression Underlies Emergent Theories and Predictive Models</title>
    <dc:date>2013-03-28T16:28:48+00:00</dc:date>
    <link>http://arxiv.org/abs/1303.6738</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We report a similarity between the microscopic parameter dependance of emergent theories in physics and that of multiparameter models common in other areas of science. In both cases, predictions are possible despite large uncertainties in the microscopic parameters because these details are compressed into just a few governing parameters that are sufficient to describe relevant observables. We make this commonality explicit by examining parameter sensitivity in a hopping model of diffusion and a generalized Ising model of ferromagnetism. We trace the emergence of a smaller effective model to the development of a hierarchy of parameter importance quantified by the eigenvalues of the Fisher Information Matrix. Strikingly, the same hierarchy appears ubiquitously in models taken from diverse areas of science. We conclude that the emergence of effective continuum and universal theories in physics is due to the same parameter space hierarchy that underlies predictive modeling in other areas of science."

--- Hmmm, yes, small eigenvalues in a Fisher matrix would correspond to nearly-non-identifiable (and so nearly-irrelevant) combinations of parameters.  Compare to Gonerup & Nilsson-Jacobi's approach based on eigendecomposition of Markov transition matrices.]]></description>
<dc:subject>to_read macro_from_micro emergence fisher_information sethna.james re:what_is_a_macrostate partial_identification identifiability low-dimensional_summaries in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:bc0320c8297b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:emergence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:fisher_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sethna.james"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:what_is_a_macrostate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:partial_identification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:identifiability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:low-dimensional_summaries"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://press.princeton.edu/titles/9909.html">
    <title>Padgett, J.F. and Powell, W.W.: The Emergence of Organizations and Markets.</title>
    <dc:date>2012-10-24T00:23:52+00:00</dc:date>
    <link>http://press.princeton.edu/titles/9909.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The social sciences have sophisticated models of choice and equilibrium but little understanding of the emergence of novelty. Where do new alternatives, new organizational forms, and new types of people come from? Combining biochemical insights about the origin of life with innovative and historically oriented social network analyses, John Padgett and Walter Powell develop a theory about the emergence of organizational, market, and biographical novelty from the coevolution of multiple social networks. They demonstrate that novelty arises from spillovers across intertwined networks in different domains. In the short run actors make relations, but in the long run relations make actors.
"This theory of novelty emerging from intersecting production and biographical flows is developed through formal deductive modeling and through a wide range of original historical case studies. Padgett and Powell build on the biochemical concept of autocatalysis--the chemical definition of life--and then extend this autocatalytic reasoning to social processes of production and communication. Padgett and Powell, along with other colleagues, analyze a very wide range of cases of emergence. They look at the emergence of organizational novelty in early capitalism and state formation; they examine the transformation of communism; and they analyze with detailed network data contemporary science-based capitalism: the biotechnology industry, regional high-tech clusters, and the open source community."]]></description>
<dc:subject>to:NB books:noted emergence networks institutions organizations powell.walter padgett.john books:owned</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d631d6c44e30/</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:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:emergence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:institutions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:organizations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:powell.walter"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:padgett.john"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:owned"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&amp;tid=13057">
    <title>Signals and Boundaries: Building Blocks for Complex Adaptive Systems - The MIT Press</title>
    <dc:date>2012-08-15T16:43:30+00:00</dc:date>
    <link>http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&amp;tid=13057</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Complex adaptive systems (cas), including ecosystems, governments, biological cells, and markets, are characterized by intricate hierarchical arrangements of boundaries and signals. In ecosystems, for example, niches act as semi-permeable boundaries, and smells and visual patterns serve as signals; governments have departmental hierarchies with memoranda acting as signals; and so it is with other cas. Despite a wealth of data and descriptions concerning different cas, there remain many unanswered questions about “steering” these systems. In Signals and Boundaries, John Holland argues that understanding the origin of the intricate signal/border hierarchies of these systems is the key to answering such questions. He develops an overarching framework for comparing and steering cas through the mechanisms that generate their signal/boundary hierarchies.
"Holland lays out a path for developing the framework that emphasizes agents, niches, theory, and mathematical models. He discusses, among other topics, theory construction; signal-processing agents; networks as representations of signal/boundary interaction; adaptation; recombination and reproduction; the use of tagged urn models (adapted from elementary probability theory) to represent boundary hierarchies; finitely generated systems as a way to tie the models examined into a single framework; the framework itself, illustrated by a simple finitely generated version of the development of a multi-celled organism; and Markov processes."]]></description>
<dc:subject>to:NB books:noted emergence self-organization holland.john_h. books:owned</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2f32d4d71136/</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:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:emergence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:self-organization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:holland.john_h."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:owned"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1207.2255">
    <title>[1207.2255] Aggregation and Emergence in Agent-Based Models: A Markov Chain Approach</title>
    <dc:date>2012-07-11T16:59:25+00:00</dc:date>
    <link>http://arxiv.org/abs/1207.2255</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We analyze the dynamics of agent--based models (ABMs) from a Markovian perspective and derive explicit statements about the possibility of linking a microscopic agent model to the dynamical processes of macroscopic observables that are useful for a precise understanding of the model dynamics. In this way the dynamics of collective variables may be studied, and a description of macro dynamics as emergent properties of micro dynamics, in particular during transient times, is possible."]]></description>
<dc:subject>agent-based_models emergence macro_from_micro re:stacs in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f3a31b2cd86e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:agent-based_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:emergence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<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://orgtheory.wordpress.com/2012/05/24/a-physicist-mathematician-economist-orgtheorist-walk-into-a-bar-collective-behavior/">
    <title>a physicist, psychologist, mathematician, economist, orgtheorist…walk into a bar…and talk about collective behavior « orgtheory.net</title>
    <dc:date>2012-05-29T19:38:26+00:00</dc:date>
    <link>http://orgtheory.wordpress.com/2012/05/24/a-physicist-mathematician-economist-orgtheorist-walk-into-a-bar-collective-behavior/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>track_down_references institutions emergence organizations re:do-institutions-evolve re:democratic_cognition</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d1f393b47512/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:track_down_references"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:institutions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:emergence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:organizations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://prl.aps.org/abstract/PRL/v107/i14/e148501">
    <title>Phys. Rev. Lett. 107, 148501 (2011): Emergence of El Niño as an Autonomous Component in the Climate Network</title>
    <dc:date>2011-10-01T23:04:42+00:00</dc:date>
    <link>http://prl.aps.org/abstract/PRL/v107/i14/e148501</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[We construct and analyze a climate network which represents the interdependent structure of the climate in different geographical zones and find that the network responds in a unique way to El Niño events. Analyzing the dynamics of the climate network shows that when El Niño events begin, the El Niño basin partially loses its influence on its surroundings. After typically three months, this influence is restored while the basin loses almost all dependence on its surroundings and becomes autonomous. The formation of an autonomous basin is the missing link to understand the seemingly contradicting phenomena of the afore-noticed weakening of the interdependencies in the climate network during El Niño and the known impact of the anomalies inside the El Niño basin on the global climate system.]]></description>
<dc:subject>climatology time_series macro_from_micro emergence pattern_formation in_NB climate_networks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:fc618331707a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:climatology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:emergence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:pattern_formation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:climate_networks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1106.5778">
    <title>[1106.5778] Effective Theories for Circuits and Automata</title>
    <dc:date>2011-09-13T12:44:56+00:00</dc:date>
    <link>http://arxiv.org/abs/1106.5778</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>emergence macro_from_micro automata_theory approximation de_deo.simon kith_and_kin to_read to:NB</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:dc735e933318/</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:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:automata_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:approximation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:de_deo.simon"/>
	<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:to:NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://philsci-archive.pitt.edu/8745/">
    <title>Laws, Causation and Dynamics at Different Levels - PhilSci-Archive</title>
    <dc:date>2011-08-07T13:36:58+00:00</dc:date>
    <link>http://philsci-archive.pitt.edu/8745/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>causality emergence macro_from_micro philosophy_of_science to:NB re:what_is_a_macrostate to_read</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6219d33c2c16/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:emergence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:what_is_a_macrostate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://philsci-archive.pitt.edu/8678/">
    <title>The Tyranny of Scales - PhilSci-Archive</title>
    <dc:date>2011-06-22T17:26:23+00:00</dc:date>
    <link>http://philsci-archive.pitt.edu/8678/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["... we have good models for material behaviors at small and large scales ...  hard to relate these ... models to one another. Macroscale models represent the integrated effects of very subtle factors that are practically invisible at the smallest, atomic, scales. ... notoriously difficult to model realistic materials with a simple bottom-up-from-the-atoms strategy.... forced physicists interested in overall macro-behavior of materials toward completely top-down modeling strategies familiar from traditional continuum mechanics. ...  whether we can exploit our rather rich knowledge of intermediate micro- (or meso-) scale behaviors in a manner that would allow us to bridge between these two dominant methodologies. Macroscopic scale behaviors often fall into large common classes of behaviors such as the class of isotropic elastic solids, characterized by two ... elastic coefficients. Can we employ knowledge of lower scale behaviors to ... determine the coefficients ... ?"
]]></description>
<dc:subject>philosophy_of_science macro_from_micro statistical_mechanics condensed-matter_physics physics emergence</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ba9bf706e249/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_mechanics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:condensed-matter_physics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:physics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:emergence"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://philsci-archive.pitt.edu/8382/">
    <title>Abstraction and Explanatory Relevance, or Why Do the Special Sciences Exist? - PhilSci-Archive</title>
    <dc:date>2010-11-10T15:56:42+00:00</dc:date>
    <link>http://philsci-archive.pitt.edu/8382/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>abstraction explanation reductionism emergence philosophy_of_science</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:49c155875582/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:abstraction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:explanation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:reductionism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:emergence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.springerlink.com/content/6032375664744422/">
    <title>Philippe Huneman, &quot;Emergence and Adaptation&quot;</title>
    <dc:date>2010-08-28T15:57:05+00:00</dc:date>
    <link>http://www.springerlink.com/content/6032375664744422/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>heard_the_talk emergence adaptation</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:60adfbc53b1d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:heard_the_talk"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:emergence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:adaptation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.andrew.cmu.edu/user/kk3n/found-phys-emerge.pdf">
    <title>Emergence, Singularities, and Symmetry Breaking</title>
    <dc:date>2010-08-26T14:59:16+00:00</dc:date>
    <link>http://www.andrew.cmu.edu/user/kk3n/found-phys-emerge.pdf</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>phase_transitions emergence philosophy_of_science statistical_mechanics field_theory via:kevin_kelly</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5f73f71f3947/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:phase_transitions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:emergence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_mechanics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:field_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:kevin_kelly"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://philsci-archive.pitt.edu/archive/00005413/">
    <title>PhilSci Archive - Are self-organizing biochemical networks emergent?</title>
    <dc:date>2010-06-17T22:01:52+00:00</dc:date>
    <link>http://philsci-archive.pitt.edu/archive/00005413/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Biochemical networks are often called upon to illustrate emergent properties of living systems. In this contribution, I question such emergentist claims by means of theoretical work on genetic regulatory models and random Boolean networks. If the existence of a critical connectivity Kc of such networks has often been coined “emergent” or “irreducible”, I propose on the contrary that the existence of a critical connectivity Kc is indeed mathematically explainable in network theory. This conclusion also applies to many other types of formal networks and weakens the emergentist claim attached to bio-molecular networks, and by extension to living systems."
]]></description>
<dc:subject>to_read emergence biochemical_networks</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:cbed4a33d8f4/</dc:identifier>
<taxo:topics><rdf:Bag>	<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:biochemical_networks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://philsci-archive.pitt.edu/archive/00005209/">
    <title>PhilSci Archive - Emergence: Postulates and Candidates</title>
    <dc:date>2010-03-30T13:42:45+00:00</dc:date>
    <link>http://philsci-archive.pitt.edu/archive/00005209/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["n the first part of this article we will formulate postulates, which must be satisfied by a reasonable concept of emergence. The postulates will articulate conditions of adequacy for an appropriate explication of the concept of emergence. These conditions of adequacy are based primarily upon the philosophical and scientific history of the concept of emergence, in which the intended role of the concept is expressed. In the second part we will discuss and evaluate some candidates for the concept of emergence in light of these conditions of adequacy."
]]></description>
<dc:subject>philosophy_of_science emergence color_me_skeptical</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d9be9d305c58/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:emergence"/>
	<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/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://philsci-archive.pitt.edu/archive/00004962/">
    <title>PhilSci Archive - Emergence and Singular Limits</title>
    <dc:date>2009-10-22T13:40:48+00:00</dc:date>
    <link>http://philsci-archive.pitt.edu/archive/00004962/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>emergence to:NB</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:437c1c4150de/</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:to:NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.mitpressjournals.org/doi/abs/10.1162/artl.2009.Gotts.009">
    <title>Ramifying Feedback Networks, Cross-Scale Interactions, and Emergent Quasi Individuals in Conway's Game of Life</title>
    <dc:date>2009-06-28T18:40:12+00:00</dc:date>
    <link>http://www.mitpressjournals.org/doi/abs/10.1162/artl.2009.Gotts.009</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Or: "The fixity of the internal environment is the condition for free life."
]]></description>
<dc:subject>cellular_automata conways_life feedback emergence autonomy macro_from_micro to:NB artificial_life pattern_formation self-organization gotts.nick</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:9fe8e8ecc9be/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cellular_automata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:conways_life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:feedback"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:emergence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:autonomy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:artificial_life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:pattern_formation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:self-organization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:gotts.nick"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.journals.uchicago.edu/doi/abs/10.1086/521968">
    <title>When Is a Brain Like the Planet? (Glymour)</title>
    <dc:date>2008-07-24T15:08:46+00:00</dc:date>
    <link>http://www.journals.uchicago.edu/doi/abs/10.1086/521968</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Damn you, Clark!  (Not really.)
]]></description>
<dc:subject>climatology causality macro_from_micro emergence graphical_models philosophy_of_science glymour.clark re:institutions_as_collective_degrees_of_freedom philosophy_of_mind in_NB</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ba0960aae4eb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:climatology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:emergence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:glymour.clark"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:institutions_as_collective_degrees_of_freedom"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.labyrinthbooks.com/sale_detail.aspx?isbn=9780942299328">
    <title>A Thousand Years of Nonlinear History - DeLanda (@Labyrinth)</title>
    <dc:date>2008-02-27T01:33:19+00:00</dc:date>
    <link>http://www.labyrinthbooks.com/sale_detail.aspx?isbn=9780942299328</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Surprisingly sane; notes at http://bactra.org/weblog/algae-2006-05.html
]]></description>
<dc:subject>delanda.manuel world_history great_transformation linguistics language_history globalization cities institutions memes complexity materialism philosophy emergence economics economic_history books:recommended</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f766ca0bb361/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:delanda.manuel"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:world_history"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:great_transformation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:linguistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:language_history"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:globalization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cities"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:institutions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:memes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:materialism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:emergence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economic_history"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:recommended"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0710.1239">
    <title>[0710.1239] On Connections between the Quantum and Hydrodynamical Pictures of Matter</title>
    <dc:date>2007-11-14T06:28:40+00:00</dc:date>
    <link>http://arxiv.org/abs/0710.1239</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>quantum_mechanics statistical_mechanics macro_from_micro emergence sewell.geoffrey</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:291cccad44db/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:quantum_mechanics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_mechanics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:emergence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sewell.geoffrey"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0710.4235">
    <title>[0710.4235] Top-Down Causation by Information Control: From a Philosophical Problem to a Scientific Research Program</title>
    <dc:date>2007-11-09T15:03:34+00:00</dc:date>
    <link>http://arxiv.org/abs/0710.4235</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Scarily, the abstract almost makes sense.
]]></description>
<dc:subject>to:NB causality emergence</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:bfe2b589589c/</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:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:emergence"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>