<?xml version="1.0" encoding="UTF-8"?>
 <rdf:RDF xmlns="http://purl.org/rss/1.0/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:taxo="http://purl.org/rss/1.0/modules/taxonomy/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:cc="http://web.resource.org/cc/" xmlns:syn="http://purl.org/rss/1.0/modules/syndication/" xmlns:admin="http://webns.net/mvcb/">
  <channel rdf:about="http://pinboard.in">
    <title>Pinboard (cshalizi)</title>
    <link>https://pinboard.in/u:cshalizi/public/</link>
    <description>recent bookmarks from cshalizi</description>
    <items>
      <rdf:Seq>	<rdf:li rdf:resource="https://arxiv.org/abs/2209.08832"/>
	<rdf:li rdf:resource="https://journals.aps.org/pre/abstract/10.1103/PhysRevE.108.014304"/>
	<rdf:li rdf:resource="https://royalsocietypublishing.org/doi/abs/10.1098/rsta.2016.0338?download=true"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2106.15487"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2106.06511"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2105.09398"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1911.06770"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2105.11482"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1809.05243"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2105.03398"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2105.03470"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1812.00995"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2104.09299"/>
	<rdf:li rdf:resource="https://journals.aps.org/pre/abstract/10.1103/PhysRevE.103.L040302"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2004.08351"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2101.05873"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2010.06025"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2004.05272"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2012.12689"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2012.01068"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1904.04918"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2011.05686"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2005.06623"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2011.10127"/>
	<rdf:li rdf:resource="https://journals.sagepub.com/doi/10.1177/0049124116626174"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1905.10391"/>
	<rdf:li rdf:resource="https://link.springer.com/book/10.1007/978-3-319-24877-6"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1910.00544"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1909.13758"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1908.09741"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1908.06057"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1907.12881"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1907.13490"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1907.09256"/>
	<rdf:li rdf:resource="https://journals.aps.org/pre/abstract/10.1103/PhysRevE.99.062118"/>
	<rdf:li rdf:resource="https://journals.aps.org/pre/abstract/10.1103/PhysRevE.99.060101"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1905.10402"/>
	<rdf:li rdf:resource="https://www.annualreviews.org/doi/abs/10.1146/annurev-conmatphys-033117-054252"/>
	<rdf:li rdf:resource="https://link.springer.com/book/10.1007/1-84628-186-5#about"/>
	<rdf:li rdf:resource="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2684776"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1704.06279"/>
	<rdf:li rdf:resource="http://www.pnas.org/content/115/22/5714"/>
	<rdf:li rdf:resource="https://link.springer.com/article/10.1007/s11229-017-1341-z"/>
	<rdf:li rdf:resource="http://link.springer.com/chapter/10.1007/978-3-319-11520-7_27"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1512.07942"/>
	<rdf:li rdf:resource="http://www.pitt.edu/~pittcntr/Events/All/Conferences/others/other_conf_2015-16/10-02-15_scalemodel/scalemodel.html"/>
	<rdf:li rdf:resource="http://www.pitt.edu/~pittcntr/Events/All/Conferences/others/other_conf_2014-15/2-7-15_emergence/emergence.html"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1404.0667"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1404.1466"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1306.4880"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1312.0115"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1310.3188"/>
	<rdf:li rdf:resource="http://www.nature.com/nphys/journal/v9/n10/full/nphys2741.html"/>
	<rdf:li rdf:resource="http://pre.aps.org/abstract/PRE/v88/i3/e032704"/>
	<rdf:li rdf:resource="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1508697/"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1301.7697"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1304.7700"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1304.6603"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1303.6738"/>
	<rdf:li rdf:resource="http://www.jstor.org/stable/10.1086/505471"/>
	<rdf:li rdf:resource="http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.aoap/1360682022"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1212.4375"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1208.3080"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1207.2692"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1207.2255"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1206.6846"/>
	<rdf:li rdf:resource="http://riscd2.eco.ub.es/~josepgon/documents/Felipe_Fisher.pdf"/>
	<rdf:li rdf:resource="http://prl.aps.org/abstract/PRL/v108/i22/e228101"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1204.0321"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1110.5216"/>
      </rdf:Seq>
    </items>
  </channel><item rdf:about="https://arxiv.org/abs/2209.08832">
    <title>[2209.08832] From microscopic to macroscopic scale equations: mean field, hydrodynamic and graph limits</title>
    <dc:date>2025-09-27T11:00:41+00:00</dc:date>
    <link>https://arxiv.org/abs/2209.08832</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Considering finite particle systems, we elaborate on various ways to pass to the limit as the number of agents tends to infinity, either by mean field limit, deriving the Vlasov equation, or by hydrodynamic or graph limit, obtaining the Euler equation. We provide convergence estimates. We also show how to pass from Liouville to Vlasov or to Euler by taking adequate moments. Our results encompass and generalize a number of known results of the this http URL a surprising consequence of our analysis, we show that sufficiently regular solutions of any linear PDE can be approximated by solutions of systems of N particles, to within 1/ log log(N )."]]></description>
<dc:subject>to:NB interacting_particle_systems macro_from_micro via:mraginsky</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:fcb71d380a0d/</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:interacting_particle_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:mraginsky"/>
</rdf:Bag></taxo:topics>
</item>
<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>
<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:via:rvenkat"/>
	<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:transfer_entropy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://royalsocietypublishing.org/doi/abs/10.1098/rsta.2016.0338?download=true">
    <title>Coarse-graining as a downward causation mechanism | Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences</title>
    <dc:date>2023-05-02T19:30:20+00:00</dc:date>
    <link>https://royalsocietypublishing.org/doi/abs/10.1098/rsta.2016.0338?download=true</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Downward causation is the controversial idea that ‘higher’ levels of organization can causally influence behaviour at ‘lower’ levels of organization. Here I propose that we can gain traction on downward causation by being operational and examining how adaptive systems identify regularities in evolutionary or learning time and use these regularities to guide behaviour. I suggest that in many adaptive systems components collectively compute their macroscopic worlds through coarse-graining. I further suggest we move from simple feedback to downward causation when components tune behaviour in response to estimates of collectively computed macroscopic properties. I introduce a weak and strong notion of downward causation and discuss the role the strong form plays in the origins of new organizational levels. I illustrate these points with examples from the study of biological and social systems and deep neural networks."]]></description>
<dc:subject>to:NB coarse-graining macro_from_micro causality flack.jessica via:henry_farrell</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:443b9dc47537/</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:coarse-graining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:flack.jessica"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:henry_farrell"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2106.15487">
    <title>[2106.15487] Becoming Large, Becoming Infinite: The Anatomy of Thermal Physics and Phase Transitions in Finite Systems</title>
    <dc:date>2021-06-30T18:43:03+00:00</dc:date>
    <link>https://arxiv.org/abs/2106.15487</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper presents an in-depth analysis of the anatomy of both thermodynamics and statistical mechanics, together with the relationships between their constituent parts. Based on this analysis, using the renormalization group and finite-size scaling, we give a definition of a large but finite system and argue that phase transitions are represented correctly, as incipient singularities in such systems. We describe the role of the thermodynamic limit. And we explore the implications of this picture of critical phenomena for the questions of reduction and emergence."]]></description>
<dc:subject>to:NB philosophy_of_science thermodynamics statistical_mechanics phase_transitions macro_from_micro</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:81fb8ba877e4/</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:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:thermodynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_mechanics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:phase_transitions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
</rdf:Bag></taxo:topics>
</item>
<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>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4229df5da19b/</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:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:cris_moore"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:abstraction"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2105.09398">
    <title>[2105.09398] Global and Local Reduced Models for Interacting, Heterogeneous Agents</title>
    <dc:date>2021-05-30T20:47:59+00:00</dc:date>
    <link>https://arxiv.org/abs/2105.09398</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Large collections of coupled, heterogeneous agents can manifest complex dynamical behavior presenting difficulties for simulation and analysis. However, if the collective dynamics lie on a low-dimensional manifold then the original agent-based model may be approximated with a simplified surrogate model on and near the low-dimensional space where the dynamics live. This is typically accomplished by deriving coarse variables that summarize the collective dynamics, these may take the form of either a collection of scalars or continuous fields (e.g. densities), which are then used as part of a reduced model. Analytically identifying such simplified models is challenging and has traditionally been accomplished through the use of mean-field reductions or an Ott-Antonsen ansatz, but is often impossible.
"Here we present a data-driven coarse-graining methodology for discovering such reduced models. We consider two types of reduced models: globally-based models which use global information and predict dynamics using information from the whole ensemble, and locally-based models that use local information, that is, information from just a subset of agents close (close in heterogeneity space, not physical space) to an agent, to predict the dynamics of an agent. For both approaches we are able to learn laws governing the behavior of the reduced system on the low-dimensional manifold directly from time series of states from the agent-based system. A nontrivial conclusion is that the dynamics can be equally well reproduced by an all-to-all coupled as well as by a locally coupled model of the same agents."]]></description>
<dc:subject>agent-based_models interacting_particle_systems macro_from_micro re:do-institutions-evolve in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:39ec37fb76b1/</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:interacting_particle_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1911.06770">
    <title>[1911.06770] Probabilistic Foundations of Spatial Mean-field Models in Ecology and Applications</title>
    <dc:date>2021-05-30T20:44:54+00:00</dc:date>
    <link>https://arxiv.org/abs/1911.06770</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Deterministic models of vegetation often summarize, at a macroscopic scale, a multitude of intrinsically random events occurring at a microscopic scale. We bridge the gap between these scales by demonstrating convergence to a mean-field limit for a general class of stochastic models representing each individual ecological event in the limit of large system size. The proof relies on classical stochastic coupling techniques that we generalize to cover spatially extended interactions. The mean-field limit is a spatially extended non-Markovian process characterized by nonlocal integro-differential equations describing the evolution of the probability for a patch of land to be in a given state (the generalized Kolmogorov equations of the process, GKEs). We thus provide an accessible general framework for spatially extending many classical finite-state models from ecology and population dynamics. We demonstrate the practical effectiveness of our approach through a detailed comparison of our limiting spatial model and the finite-size version of a specific savanna-forest model, the so-called Staver-Levin model. There is remarkable dynamic consistency between the GKEs and the finite-size system, in spite of almost sure forest extinction in the finite-size system. To resolve this apparent paradox, we show that the extinction rate drops sharply when nontrivial equilibria emerge in the GKEs, and that the finite-size system's quasi-stationary distribution (stationary distribution conditional on non-extinction) closely matches the bifurcation diagram of the GKEs. Furthermore, the limit process can support periodic oscillations of the probability distribution, thus providing an elementary example of a jump process that does not converge to a stationary distribution. In spatially extended settings, environmental heterogeneity can lead to waves of invasion and front-pinning phenomena."]]></description>
<dc:subject>to:NB ecology macro_from_micro random_fields levin.simon</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e6ad02fba8ee/</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:ecology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:random_fields"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:levin.simon"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2105.11482">
    <title>[2105.11482] Essential renormalisation group</title>
    <dc:date>2021-05-26T15:55:30+00:00</dc:date>
    <link>https://arxiv.org/abs/2105.11482</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We propose a novel scheme for the exact renormalisation group motivated by the desire of reducing the complexity of practical computations. The key idea is to specify renormalisation conditions for all inessential couplings, leaving us with the task of computing only the flow of the essential ones. To achieve this aim, we utilise a renormalisation group equation for the effective average action which incorporates general non-linear field reparameterisations. A prominent feature of the scheme is that, apart from the renormalisation of the mass, the propagator evaluated at any constant value of the field maintains its unrenormalised form. Conceptually, the scheme provides a clearer picture of renormalisation itself since the redundant, non-physical content is automatically disregarded in favour of a description based only on quantities that enter expressions for physical observables. To exemplify the scheme's utility, we investigate the Wilson-Fisher fixed point in three dimensions at order two in the derivative expansion. In this case, the scheme removes all order ∂2 operators apart from the canonical term. Further simplifications occur at higher orders in the derivative expansion. Although we concentrate on a minimal scheme that reduces the complexity of computations, we propose more general schemes where inessential couplings can be tuned to optimise a given approximation. We further discuss the applicability of the scheme to a broad range of physical theories."]]></description>
<dc:subject>to:NB renormalization macro_from_micro statistical_mechanics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:38e20c32b31f/</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:renormalization"/>
	<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:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1809.05243">
    <title>[1809.05243] Random Fixed Points, Limits and Systemic risk</title>
    <dc:date>2021-05-18T14:00:45+00:00</dc:date>
    <link>https://arxiv.org/abs/1809.05243</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We consider vector fixed point (FP) equations in large dimensional spaces involving random variables, and study their realization-wise solutions. We have an underlying directed random graph, that defines the connections between various components of the FP equations. Existence of an edge between nodes i, j implies the i th FP equation depends on the j th component. We consider a special case where any component of the FP equation depends upon an appropriate aggregate of that of the random neighbor components. We obtain finite dimensional limit FP equations (in a much smaller dimensional space), whose solutions approximate the solution of the random FP equations for almost all realizations, in the asymptotic limit (number of components increase). Our techniques are different from the traditional mean-field methods, which deal with stochastic FP equations in the space of distributions to describe the stationary distributions of the systems. In contrast our focus is on realization-wise FP solutions. We apply the results to study systemic risk in a large financial heterogeneous network with many small institutions and one big institution, and demonstrate some interesting phenomenon."

--- This _sounds_ weird, but possibly interesting, in a "what does a generic randomly-wired system do anyway?" vein.]]></description>
<dc:subject>to:NB dynamical_systems stochastic_processes macro_from_micro color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:27faf6ca4b62/</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:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<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/2105.03398">
    <title>[2105.03398] A method to coarse-grain multi-agent stochastic systems with regions of multistability</title>
    <dc:date>2021-05-14T02:02:54+00:00</dc:date>
    <link>https://arxiv.org/abs/2105.03398</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Hybrid multiscale modelling has emerged as a useful framework for modelling complex biological phenomena. However, when accounting for stochasticity in the internal dynamics of agents, these models frequently become computationally expensive. Traditional techniques to reduce the computational intensity of such models can lead to a reduction in the richness of the dynamics observed, compared to the original system. Here we use large deviation theory to decrease the computational cost of a spatially-extended multi-agent stochastic system with a region of multi-stability by coarse-graining it to a continuous time Markov chain on the state space of stable steady states of the original system. Our technique preserves the original description of the stable steady states of the system and accounts for noise-induced transitions between them. We apply the method to a bistable system modelling phenotype specification of cells driven by a lateral inhibition mechanism. For this system, we demonstrate how the method may be used to explore different pattern configurations and unveil robust patterns emerging on longer timescales. We then compare the full stochastic, coarse-grained and mean-field descriptions via pattern quantification metrics and in terms of the numerical cost of each method. Our results show that the coarse-grained system exhibits the lowest computational cost while preserving the rich dynamics of the stochastic system. The method has the potential to reduce the computational complexity of hybrid multiscale models, making them more tractable for analysis, simulation and hypothesis testing."]]></description>
<dc:subject>to:NB coarse-graining agent-based_models interacting_particle_systems metastability macro_from_micro re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4542b40ccf48/</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:coarse-graining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:agent-based_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:interacting_particle_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:metastability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
</rdf:Bag></taxo:topics>
</item>
<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>
<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:lebowitz.joel"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_mechanics"/>
	<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:arrow_of_time"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1812.00995">
    <title>[1812.00995] Computational Chemistry as Voodoo Quantum Mechanics : Models, Parameterization, and Software</title>
    <dc:date>2021-04-22T15:30:21+00:00</dc:date>
    <link>https://arxiv.org/abs/1812.00995</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Computational chemistry grew in a new era of "desktop modeling", which coincided with a growing demand for modeling software, especially from the pharmaceutical industry. Parameterization of models in computational chemistry is an arduous enterprise, and we argue that this activity leads, in this specific context, to tensions among scientists regarding the lack of epistemic transparency of parameterized methods and the software implementing them. To explicit these tensions, we rely on a corpus which is suited for revealing them, namely the Computational Chemistry mailing List (CCL), a professional scientific discussion forum. We relate one flame war from this corpus in order to assess in detail the relationships between modeling methods, parameterization, software and the various forms of their enclosure or disclosure. Our claim is that parameterization issues are a source of epistemic opacity and that this opacity is entangled in methods and software alike. Models and software must be addressed together to understand the epistemological tensions at stake."]]></description>
<dc:subject>to:NB macro_from_micro chemistry physics sociology_of_science networked_life social_life_of_the_mind</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:01ced07d4c69/</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:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:chemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:physics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sociology_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networked_life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_life_of_the_mind"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2104.09299">
    <title>[2104.09299] Complex networks of interacting stochastic tipping elements: cooperativity of phase separation in the large-system limit</title>
    <dc:date>2021-04-21T19:45:48+00:00</dc:date>
    <link>https://arxiv.org/abs/2104.09299</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Tipping elements in the Earth System receive increased scientific attention over the recent years due to their nonlinear behavior and the risks of abrupt state changes. While being stable over a large range of parameters, a tipping element undergoes a drastic shift in its state upon an additional small parameter change when close to its tipping point. Recently, the focus of research broadened towards emergent behavior in networks of tipping elements, like global tipping cascades triggered by local perturbations. Here, we analyze the response to the perturbation of a single node in a system that initially resides in an unstable equilibrium. The evolution is described in terms of coupled nonlinear equations for the cumulants of the distribution of the elements. We show that drift terms acting on individual elements and offsets in the coupling strength are sub-dominant in the limit of large networks, and we derive an analytical prediction for the evolution of the expectation (i.e., the first cumulant). It behaves like a single aggregated tipping element characterized by a dimensionless parameter that accounts for the network size, its overall connectivity, and the average coupling strength. The resulting predictions are in excellent agreement with numerical data for Erdös-Rényi, Barabási-Albert and Watts-Strogatz networks of different size and with different coupling parameters."]]></description>
<dc:subject>to:NB dynamical_systems networks macro_from_micro re:do-institutions-evolve to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f7b3f6fc4328/</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:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.aps.org/pre/abstract/10.1103/PhysRevE.103.L040302">
    <title>Phys. Rev. E 103, L040302 (2021) - Reduction of the collective dynamics of neural populations with realistic forms of heterogeneity</title>
    <dc:date>2021-04-21T16:11:18+00:00</dc:date>
    <link>https://journals.aps.org/pre/abstract/10.1103/PhysRevE.103.L040302</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Reduction of collective dynamics of large heterogeneous populations to low-dimensional mean-field models is an important task of modern theoretical neuroscience. Such models can be derived from microscopic equations, for example with the help of Ott-Antonsen theory. An often used assumption of the Lorentzian distribution of the unit parameters makes the reduction especially efficient. However, the Lorentzian distribution is often implausible as having undefined moments, and the collective behavior of populations with other distributions needs to be studied. In the present Letter we propose a method which allows efficient reduction for an arbitrary distribution and show how it performs for the Gaussian distribution. We show that a reduced system for several macroscopic complex variables provides an accurate description of a population of thousands of neurons. Using this reduction technique we demonstrate that the population dynamics depends significantly on the form of its parameter distribution. In particular, the dynamics of populations with Lorentzian and Gaussian distributions with the same center and width differ drastically."]]></description>
<dc:subject>to:NB macro_from_micro neuroscience</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:27948fb67ef0/</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:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neuroscience"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2004.08351">
    <title>[2004.08351] Convergence of large population games to mean field games with interaction through the controls</title>
    <dc:date>2021-04-10T04:28:07+00:00</dc:date>
    <link>https://arxiv.org/abs/2004.08351</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This work considers stochastic differential games with a large number of players, whose costs and dynamics interact through the empirical distribution of both their states and their controls. We develop a new framework to prove convergence of finite-player games to the asymptotic mean field game. Our approach is based on the concept of propagation of chaos for forward and backward weakly interacting particles which we investigate by fully probabilistic methods, and which appear to be of independent interest. These propagation of chaos arguments allow to derive moment and concentration bounds for the convergence of both Nash equilibria and social optima in non-cooperative and cooperative games, respectively. Incidentally, we also obtain convergence of a system of second order parabolic partial differential equations on finite dimensional spaces to a second order parabolic partial differential equation on the Wasserstein space."]]></description>
<dc:subject>learning_in_games macro_from_micro re:do-institutions-evolve in_NB mean-field_games</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:213df8fb1e87/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mean-field_games"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2101.05873">
    <title>[2101.05873] Data-driven learning for the Mori--Zwanzig formalism: a generalization of the Koopman learning framework</title>
    <dc:date>2021-01-19T20:57:23+00:00</dc:date>
    <link>https://arxiv.org/abs/2101.05873</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A theoretical framework which unifies the conventional Mori--Zwanzig formalism and the approximate Koopman learning is presented. In this framework, the Mori--Zwanzig formalism, developed in statistical mechanics to tackle the hard problem of construction of reduced-order dynamics for high-dimensional dynamical systems, can be considered as a natural generalization of the Koopman description of the dynamical system. We next show that similar to the approximate Koopman learning methods, data-driven methods can be developed for the Mori--Zwanzig formalism with Mori's linear projection operator. We developed two algorithms to extract the key operators, the Markov and the memory kernel, using time series of a reduced set of observables in a dynamical system. We adopted the Lorenz `96 system as a test problem and solved for the operators, which exhibit complex behaviors which are unlikely to be captured by traditional modeling approaches, in Mori--Zwanzig analysis. The nontrivial Generalized Fluctuation Dissipation relationship, which relates the memory kernel with the two-time correlation statistics of the orthogonal dynamics, was numerically verified as a validation of the solved operators. We present numerical evidence that the Generalized Langevin Equation, a key construct in the Mori--Zwanzig formalism, is more advantageous in predicting the evolution of the reduced set of observables than the conventional approximate Koopman operators."]]></description>
<dc:subject>macro_from_micro stochastic_processes approximation in_NB mori-zwanzig koopman_operators</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5c04db814345/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:approximation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mori-zwanzig"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:koopman_operators"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2010.06025">
    <title>[2010.06025] The replicator equation in stochastic spatial evolutionary games</title>
    <dc:date>2021-01-14T15:53:19+00:00</dc:date>
    <link>https://arxiv.org/abs/2010.06025</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We study the multi-strategy stochastic evolutionary game with death-birth updating in expanding spatial populations of size N→∞. The model is a voter model perturbation. For typical populations, we require perturbation strengths satisfying 1/N≪w≪1. Under appropriate conditions on the space, the limiting density processes of strategy are proven to obey the replicator equation, and the normalized fluctuations converge to a Gaussian process with the Wright-Fisher covariance function in the limiting densities. As an application, we resolve in the positive a conjecture from the biological literature that the expected density processes approximate the replicator equation on many non-regular graphs."]]></description>
<dc:subject>to:NB evolutionary_biology replicator_dynamics macro_from_micro re:do-institutions-evolve stochastic_processes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:304c6af14fe6/</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:evolutionary_biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:replicator_dynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2004.05272">
    <title>[2004.05272] Modeling the Heterogeneity in COVID-19's Reproductive Number and its Impact on Predictive Scenarios</title>
    <dc:date>2021-01-12T22:30:39+00:00</dc:date>
    <link>https://arxiv.org/abs/2004.05272</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The correct evaluation of the reproductive number R for COVID-19 -- which characterizes the average number of secondary cases generated by each typical primary case -- is central in the quantification of the potential scope of the pandemic and the selection of an appropriate course of action. In most models, R is modeled as a universal constant for the virus across outbreak clusters and individuals -- effectively averaging out the inherent variability of the transmission process due to varying individual contact rates, population densities, demographics, or temporal factors amongst many. Yet, due to the exponential nature of epidemic growth, the error due to this simplification can be rapidly amplified and lead to inaccurate predictions and/or risk evaluation. From the statistical modeling perspective, the magnitude of the impact of this averaging remains an open question: how can this intrinsic variability be percolated into epidemic models, and how can its impact on uncertainty quantification and predictive scenarios be better quantified? In this paper, we propose to study this question through a Bayesian perspective, creating a bridge between the agent-based and compartmental approaches commonly used in the literature. After deriving a Bayesian model that captures at scale the heterogeneity of a population and environmental conditions, we simulate the spread of the epidemic as well as the impact of different social distancing strategies, and highlight the strong impact of this added variability on the reported results. We base our discussion on both synthetic experiments -- thereby quantifying of the reliability and the magnitude of the effects -- and real COVID-19 data."]]></description>
<dc:subject>to:NB macro_from_micro holmes.susan epidemic_on_networks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:321100749ee6/</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:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:holmes.susan"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epidemic_on_networks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2012.12689">
    <title>[2012.12689] The Less Intelligent the Elements, the More Intelligent the Whole. Or, Possibly Not?</title>
    <dc:date>2020-12-26T17:47:24+00:00</dc:date>
    <link>https://arxiv.org/abs/2012.12689</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We dare to make use of a possible analogy between neurons in a brain and people in society, asking ourselves whether individual intelligence is necessary in order to collective wisdom to emerge and, most importantly, what sort of individual intelligence is conducive of greater collective wisdom. We review insights and findings from connectionism, agent-based modeling, group psychology, economics and physics, casting them in terms of changing structure of the system's Lyapunov function. Finally, we apply these insights to the sort and degrees of intelligence of preys and predators in the Lotka-Volterra model, explaining why certain individual understandings lead to co-existence of the two species whereas other usages of their individual intelligence cause global extinction."]]></description>
<dc:subject>to:NB macro_from_micro collective_cognition color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:088d9a57add1/</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:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<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/2012.01068">
    <title>[2012.01068] Reduced-Order Models for Coupled Dynamical Systems: Koopman Operator and Data-driven Methods</title>
    <dc:date>2020-12-04T21:40:04+00:00</dc:date>
    <link>https://arxiv.org/abs/2012.01068</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Providing efficient and accurate parametrizations for model reduction is a key goal in many areas of science and technology. Here we demonstrate a link between data-driven and theoretical approaches to achieving this goal. Formal perturbation expansions of the Koopman operator allow us to derive general stochastic parametrizations of weakly coupled dynamical systems. Such parametrizations yield a set of stochastic integro-differential equations with explicit noise and memory kernel formulas to describe the effects of unresolved variables. We show that the perturbation expansions involved need not be truncated when the coupling is additive. The unwieldy integro-differential equations can be recast as a simpler multilevel Markovian model, and we establish an intutive link with the formalism of a generalized Langevin equation. This link helps setting up a clear connection between the top-down, equations-based methodology herein and the well-established empirical model reduction (EMR) methodology that has been shown to provide efficient dynamical closures to partially observed systems. Hence, our findings support, on the one hand, the physical basis and robustness of the EMR methodology and, on the other hand, illustrate the practical relevance of the perturbative expansion used for deriving the parametrizations."]]></description>
<dc:subject>stochastic_processes dynamical_systems macro_from_micro in_NB koopman_operators</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c3a153af7d79/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:koopman_operators"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1904.04918">
    <title>[1904.04918] How to Win Friends and Influence Functionals: Deducing Stochasticity From Deterministic Dynamics</title>
    <dc:date>2020-12-02T15:21:35+00:00</dc:date>
    <link>https://arxiv.org/abs/1904.04918</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The longstanding question of how stochastic behaviour arises from deterministic Hamiltonian dynamics is of great importance, and any truly holistic theory must be capable of describing this transition. In this review, we introduce the influence functional formalism in both the quantum and classical regimes. Using this technique, we demonstrate how irreversible behaviour arises generically from the reduced microscopic dynamics of a system-environment amalgam. The influence functional is then used to rigorously derive stochastic equations of motion from a microscopic Hamiltonian. In this method stochastic terms are not identified heuristically, but instead arise from an exact mapping only available in the path-integral formalism. The interpretability of the individual stochastic trajectories arising from the mapping is also discussed. As a consequence of these results, we are also able to show that the proper classical limit of stochastic quantum dynamics corresponds non-trivially to a generalised Langevin equation derived with the classical influence functional. This provides a further unifying link between open quantum systems and their classical equivalent, highlighting the utility of influence functionals and their potential as a tool in both fundamental and applied research."]]></description>
<dc:subject>to:NB physics statistical_mechanics stochastic_processes macro_from_micro</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f6a354d0c29e/</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:physics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_mechanics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2011.05686">
    <title>[2011.05686] A large deviation principle for Markovian slow-fast systems</title>
    <dc:date>2020-11-25T14:51:04+00:00</dc:date>
    <link>https://arxiv.org/abs/2011.05686</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We prove pathwise large deviation principles of slow variables in slow-fast systems in the limit of time-scale separation tending to infinity. In the limit regime we consider, the convergence of the slow variable to its deterministic limit and the convergence of the fast variable to equilibrium are competing at the same scale. The large deviation principle is proven by relating the large deviation problem to solutions of Hamilton-Jacobi-Bellman equations, for which well-posedness was established in the companion paper [arXiv:1912.06579].
"We cast the rate functions in action-integral form and interpret the Lagrangians in two ways. First, in terms of a double-optimization problem of the slow variable's velocity and the fast variable's distribution, similar in spirit to what one obtains from the contraction principle. Second, in terms of a principal-eigenvalue problem associated to the slow-fast system. The first representation proves in particular useful in the derivation of averaging principles from the large deviations principles.
"As main example of our general results, we consider empirical measure-flux pairs coupled to a fast diffusion on a compact manifold. We prove large deviations and use the Lagrangian in double-optimization form to demonstrate the validity of the averaging principle in this system."]]></description>
<dc:subject>to:NB large_deviations stochastic_processes macro_from_micro</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:acb336533736/</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:large_deviations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2005.06623">
    <title>[2005.06623] Kernel Analog Forecasting: Multiscale Test Problems</title>
    <dc:date>2020-11-25T14:36:08+00:00</dc:date>
    <link>https://arxiv.org/abs/2005.06623</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Data-driven prediction is becoming increasingly widespread as the volume of data available grows and as algorithmic development matches this growth. The nature of the predictions made, and the manner in which they should be interpreted, depends crucially on the extent to which the variables chosen for prediction are Markovian, or approximately Markovian. Multiscale systems provide a framework in which this issue can be analyzed. In this work kernel analog forecasting methods are studied from the perspective of data generated by multiscale dynamical systems. The problems chosen exhibit a variety of different Markovian closures, using both averaging and homogenization; furthermore, settings where scale-separation is not present and the predicted variables are non-Markovian, are also considered. The studies provide guidance for the interpretation of data-driven prediction methods when used in practice."]]></description>
<dc:subject>to:NB prediction markov_models time_series to_read macro_from_micro kernel_smoothing</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0145978fda9c/</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:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:markov_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<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:kernel_smoothing"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2011.10127">
    <title>[2011.10127] State Predictive Information Bottleneck</title>
    <dc:date>2020-11-23T17:40:32+00:00</dc:date>
    <link>https://arxiv.org/abs/2011.10127</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The ability to make sense of the massive amounts of high-dimensional data generated from molecular dynamics (MD) simulations is heavily dependent on the knowledge of a low dimensional manifold (known as reaction coordinate or RC) that typically distinguishes between relevant metastable states and which captures the relevant slow dynamics of interest. Methods based on machine learning and artificial intelligence have been proposed over the years to deal with learning such low-dimensional manifolds, but they are often criticized for a disconnect from more traditional and physically interpretable approaches. To deal with such concerns, in this work, we propose a deep learning based State Predictive Information Bottleneck (SPIB) approach to learn the RC from high dimensional molecular simulation trajectories. We demonstrate analytically and numerically how the RC learnt in this approach is deeply connected to the committor in chemical physics, and can be used to accurately identify transition states. A crucial hyperparameter in this approach is the time-delay, or how far into the future should the algorithm make predictions about. Through careful comparisons for benchmark systems, we demonstrate that this hyperparameter choice gives useful control over how coarse-grained we want the metastable state classification of the system to be. We thus believe that this work represents a step forward in systematic application of deep learning based ideas to molecular simulations in a way that bridges the gap between artificial intelligence and traditional chemical physics."

--- Grumble grumble https://arxiv.org/abs/cond-mat/0303625 grumble grumble https://arxiv.org/abs/1205.4591 grumble grumble why did GMG never publish his ADA?]]></description>
<dc:subject>to:NB to_read information_bottleneck macro_from_micro</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:158a4a81db88/</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:information_bottleneck"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.sagepub.com/doi/10.1177/0049124116626174">
    <title>Identifying Complex Dynamics in Social Systems: A New Methodological Approach Applied to Study School Segregation - Viktoria Spaiser, Peter Hedström, Shyam Ranganathan, Kim Jansson, Monica K. Nordvik, David J. T. Sumpter, 2018</title>
    <dc:date>2019-11-27T00:45:41+00:00</dc:date>
    <link>https://journals.sagepub.com/doi/10.1177/0049124116626174</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["It is widely recognized that segregation processes are often the result of complex nonlinear dynamics. Empirical analyses of complex dynamics are however rare, because there is a lack of appropriate empirical modeling techniques that are capable of capturing complex patterns and nonlinearities. At the same time, we know that many social phenomena display nonlinearities. In this article, we introduce a new modeling tool in order to partly fill this void in the literature. Using data of all secondary schools in Stockholm county during the years 1990 to 2002, we demonstrate how the methodology can be applied to identify complex dynamic patterns like tipping points and multiple phase transitions with respect to segregation. We establish critical thresholds in schools’ ethnic compositions, in general, and in relation to various factors such as school quality and parents’ income, at which the schools are likely to tip and become increasingly segregated."]]></description>
<dc:subject>to:NB sociology macro_from_micro dynamical_systems hedstrom.peter via:rvenkat schelling_model</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b4bc5768ef5a/</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:sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hedstrom.peter"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:rvenkat"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:schelling_model"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1905.10391">
    <title>[1905.10391] Coarse Graining of Partitioned Cellular Automata</title>
    <dc:date>2019-10-15T18:15:36+00:00</dc:date>
    <link>https://arxiv.org/abs/1905.10391</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Partitioned cellular automata are known to be an useful tool to simulate linear and nonlinear problems in physics, specially because they allow for a straightforward way to define conserved quantities and reversible dynamics. Here we show how to construct a local coarse graining description of partitioned cellular automata. By making use of this tool we investigate the effective dynamics in this model of computation. All examples explored are in the scenario of lattice gases, so that the information lost after the coarse graining is related to the number of particles. It becomes apparent how difficult it is to remain with a deterministic dynamics after coarse graining. Several examples are shown where an effective stochastic dynamics is obtained after a deterministic dynamics is coarse grained. These results suggest why random processes are so common in nature. Although all the cases presented assume one-dimensional lattices, we show how our approach can be extended to higher dimensions."]]></description>
<dc:subject>to:NB cellular_automata macro_from_micro stochastic_processes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a388a9cac9bb/</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:cellular_automata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://link.springer.com/book/10.1007/978-3-319-24877-6">
    <title>Markov Chain Aggregation for Agent-Based Models | SpringerLink</title>
    <dc:date>2019-10-03T14:35:55+00:00</dc:date>
    <link>https://link.springer.com/book/10.1007/978-3-319-24877-6</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This self-contained text develops a Markov chain approach that makes the rigorous analysis of a class of microscopic models that specify the dynamics of complex systems at the individual level possible. It presents a general framework of aggregation in agent-based and related computational models, one which makes use of lumpability and information theory in order to link the micro and macro levels of observation. The starting point is a microscopic Markov chain description of the dynamical process in complete correspondence with the dynamical behavior of the agent-based model (ABM), which is obtained by considering the set of all possible agent configurations as the state space of a huge Markov chain. An explicit formal representation of a resulting “micro-chain” including microscopic transition rates is derived for a class of models by using the random mapping representation of a Markov process. The type of probability distribution used to implement the stochastic part of the model, which defines the updating rule and governs the dynamics at a Markovian level, plays a crucial part in the analysis of “voter-like” models used in population genetics, evolutionary game theory and social dynamics. The book demonstrates that the problem of aggregation in ABMs - and the lumpability conditions in particular - can be embedded into a more general framework that employs information theory in order to identify different levels and relevant scales in complex dynamical systems"]]></description>
<dc:subject>to:NB books:noted macro_from_micro agent-based_models interacting_particle_systems markov_models downloaded</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:360ab70ea952/</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:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:agent-based_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:interacting_particle_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:markov_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:downloaded"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1910.00544">
    <title>[1910.00544] A machine learning approach to predicting dynamical observables from network structure</title>
    <dc:date>2019-10-02T15:51:49+00:00</dc:date>
    <link>https://arxiv.org/abs/1910.00544</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Estimating the outcome of a given dynamical process from structural features is a key unsolved challenge in network science. The goal is hindered by difficulties associated to nonlinearities, correlations and feedbacks between the structure and dynamics of complex systems. In this work, we develop an approach based on machine learning algorithms that is shown to provide an answer to the previous challenge. Specifically, we show that it is possible to estimate the outbreak size of a disease starting from a single node as well as the degree of synchronicity of a system made up of Kuramoto oscillators. In doing so, we show which topological features of the network are key for this estimation, and provide a rank of the importance of network metrics with higher accuracy than previously done. Our approach is general and can be applied to any dynamical process running on top of complex networks. Likewise, our work constitutes an important step towards the application of machine learning methods to unravel dynamical patterns emerging in complex networked systems."

--- I don't see any way this can be done which won't be entirely at the mercy of data-set shift.]]></description>
<dc:subject>to:NB network_data_analysis macro_from_micro statistics kurths.jurgen color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1245b3b0ffad/</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:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kurths.jurgen"/>
	<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/1909.13758">
    <title>[1909.13758] Macroscopic approximation methods for the analysis of adaptive networked agent-based models: The example of a two-sector investment model</title>
    <dc:date>2019-10-01T16:26:35+00:00</dc:date>
    <link>https://arxiv.org/abs/1909.13758</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this paper, we propose a statistical aggregation method for agent-based models with heterogeneous agents that interact both locally on a complex adaptive network and globally on a market. The method combines three approaches from statistical physics: (a) moment closure, (b) pair approximation of adaptive network processes, and (c) thermodynamic limit of the resulting stochastic process. As an example of use, we develop a stochastic agent-based model with heterogeneous households that invest in either a fossil-fuel or renewables-based sector while allocating labor on a competitive market. Using the adaptive voter model, the model describes agents as social learners that interact on a dynamic network. We apply the approximation methods to derive a set of ordinary differential equations that approximate the macro-dynamics of the model. A comparison of the reduced analytical model with numerical simulations shows that the approximation fits well for a wide range of parameters. The proposed method makes it possible to use analytical tools to better understand the dynamical properties of models with heterogeneous agents on adaptive networks. We showcase this with a bifurcation analysis that identifies parameter ranges with multi-stabilities. The method can thus help to explain emergent phenomena from network interactions and make them mathematically traceable."]]></description>
<dc:subject>to:NB macro_from_micro agent-based_models interacting_particle_systems stochastic_processes re:do-institutions-evolve kurths.jurgen</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3b87375021e5/</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:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:agent-based_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:interacting_particle_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kurths.jurgen"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1908.09741">
    <title>[1908.09741] Manifestations of Projection-Induced Memory: General Theory and the Tilted Single File</title>
    <dc:date>2019-08-27T15:46:49+00:00</dc:date>
    <link>https://arxiv.org/abs/1908.09741</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Over the years the field of non-Markovian stochastic processes and anomalous diffusion evolved from a specialized topic to mainstream theory, which transgressed the realms of physics to chemistry, biology and ecology. Numerous phenomenological approaches emerged, which can more or less successfully reproduce or account for experimental observations in condensed matter, biological and/or single-particle systems. However, as far as their predictions are concerned these approaches are not unique, often build on conceptually orthogonal ideas, and are typically employed on an ad hoc basis. It therefore seems timely and desirable to establish a systematic, mathematically unifying and clean approach starting from more fine-grained principles. Here we analyze projection-induced ergodic non-Markovian dynamics, both reversible as well as irreversible, using spectral theory. We investigate dynamical correlations between histories of projected and latent observables that give rise to memory in projected dynamics, and rigorously establish conditions under which projected dynamics is Markovian or renewal. A systematic metric is proposed for quantifying the degree of non-Markovianity. As a simple, illustrative but non-trivial example we study single file diffusion in a tilted box, which, for the first time, we solve exactly using the coordinate Bethe ansatz. Our results provide a solid foundation for a deeper and more systematic analysis of projection-induced non-Markovian dynamics and anomalous diffusion."]]></description>
<dc:subject>to:NB macro_from_micro stochastic_processes statistics markov_models re:what_is_a_macrostate</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6252f1d4364b/</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:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:markov_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:what_is_a_macrostate"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1908.06057">
    <title>[1908.06057] Generalized group-based epidemic model for spreading processes on networks: GgroupEM</title>
    <dc:date>2019-08-19T13:20:28+00:00</dc:date>
    <link>https://arxiv.org/abs/1908.06057</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We develop a generalized group-based epidemic model (GgroupEM) framework for any compartmental epidemic model (for example; susceptible-infected-susceptible, susceptible-infected-recovered, susceptible-exposed-infected-recovered). Here, a group consists of a collection of individual nodes. This model can be used to understand the important dynamic characteristics of a stochastic epidemic spreading over very large complex networks, being informative about the state of groups. Aggregating nodes by groups, the state space becomes smaller than the individual-based approach at the cost of aggregation error, which is strongly bounded by the isoperimetric inequality. We also develop a mean-field approximation of this framework to further reduce the state-space size. Finally, we extend the GgroupEM to multilayer networks. Since the group-based framework is computationally less expensive and faster than an individual-based framework, then this framework is useful when the simulation time is important."]]></description>
<dc:subject>epidemics_on_networks epidemic_models macro_from_micro graph_theory in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:aeab934e4e4d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epidemics_on_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epidemic_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graph_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1907.12881">
    <title>[1907.12881] Response and Sensitivity Using Markov Chains</title>
    <dc:date>2019-08-07T17:23:54+00:00</dc:date>
    <link>https://arxiv.org/abs/1907.12881</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Dynamical systems are often subject to forcing or changes in their governing parameters and it is of interest to study how this affects their statistical properties. A prominent real-life example of this class of problems is the investigation of climate response to perturbations. In this respect, it is crucial to determine what the linear response of a system is to small perturbations as a quantification of sensitivity. Alongside previous work, here we use the transfer operator formalism to study the response and sensitivity of a dynamical system undergoing perturbations. By projecting the transfer operator onto a suitable finite dimensional vector space, one is able to obtain matrix representations which determine finite Markov processes. Further, using perturbation theory for Markov matrices, it is possible to determine the linear and nonlinear response of the system given a prescribed forcing. Here, we suggest a methodology which puts the scope on the evolution law of densities (the Liouville/Fokker-Planck equation), allowing to effectively calculate the sensitivity and response of two representative dynamical systems."]]></description>
<dc:subject>stochastic_processes dynamical_systems markov_models fluctuation-response macro_from_micro in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:738f0588e87a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:markov_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:fluctuation-response"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1907.13490">
    <title>[1907.13490] Linear response for macroscopic observables in high-dimensional systems</title>
    <dc:date>2019-08-01T17:03:16+00:00</dc:date>
    <link>https://arxiv.org/abs/1907.13490</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The long-term average response of observables of chaotic systems to dynamical perturbations can often be predicted using linear response theory, but not all chaotic systems possess a linear response. Macroscopic observables of complex dissipative chaotic systems, however, are widely assumed to have a linear response even if the microscopic variables do not, but the mechanism for this is not well-understood. 
"We present a comprehensive picture for the linear response of macroscopic observables in high-dimensional weakly coupled deterministic dynamical systems, where the weak coupling is via a mean field and the microscopic subsystems may or may not obey linear response theory. We derive stochastic reductions of the dynamics of these observables from statistics of the microscopic system, and provide conditions for linear response theory to hold in finite dimensional systems and in the thermodynamic limit. In particular, we show that for large systems of finite size, linear response is induced via self-generated noise. 
"We present examples in the thermodynamic limit where the macroscopic observable satisfies LRT, although the microscopic subsystems individually violate LRT, as well a converse example where the macroscopic observable does not satisfy LRT despite all microscopic subsystems satisfying LRT when uncoupled. This latter, maybe surprising, example is associated with emergent non-trivial dynamics of the macroscopic observable. We provide numerical evidence for our results on linear response as well as some analytical intuition"]]></description>
<dc:subject>to:NB dynamical_systems statistical_mechanics macro_from_micro</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ede814540328/</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:dynamical_systems"/>
	<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:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1907.09256">
    <title>[1907.09256] Strong and weak convergence in the averaging principle for SDEs with Hölder coefficients</title>
    <dc:date>2019-07-24T14:10:46+00:00</dc:date>
    <link>https://arxiv.org/abs/1907.09256</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Using Zvonkin's transform and the Poisson equation in Rd with a parameter, we prove the averaging principle for stochastic differential equations with time-dependent Hölder continuous coefficients. Sharp convergence rates with order (α∧1)/2 in the strong sense and (α/2)∧1 in the weak sense are obtained, considerably extending the existing results in the literature. Moreover, we prove that the convergence of the multi-scale system to the effective equation depends only on the regularity of the coefficients of the equation for the slow variable, and does not depend on the regularity of the coefficients of the equation for the fast component."]]></description>
<dc:subject>to:NB stochastic_differential_equations stochastic_processes macro_from_micro</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e83b29be3cba/</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:stochastic_differential_equations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.aps.org/pre/abstract/10.1103/PhysRevE.99.062118">
    <title>Phys. Rev. E 99, 062118 (2019) - Mori-Zwanzig projection operator formalism for far-from-equilibrium systems with time-dependent Hamiltonians</title>
    <dc:date>2019-06-17T17:01:46+00:00</dc:date>
    <link>https://journals.aps.org/pre/abstract/10.1103/PhysRevE.99.062118</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The Mori-Zwanzig projection operator formalism is a powerful method for the derivation of mesoscopic and macroscopic theories based on known microscopic equations of motion. It has applications in a large number of areas including fluid mechanics, solid-state theory, spin relaxation theory, and particle physics. In its present form, however, the formalism cannot be directly applied to systems with time-dependent Hamiltonians. Such systems are relevant in many scenarios such as driven soft matter or nuclear magnetic resonance. In this article we derive a generalization of the present Mori-Zwanzig formalism that is able to treat also time-dependent Hamiltonians. The extended formalism can be applied to classical and quantum systems, close to and far from thermodynamic equilibrium, and even in the case of explicitly-time-dependent observables. Moreover, we develop a variety of approximation techniques that enhance the practical applicability of our formalism. Generalizations and approximations are developed for both equations of motion and correlation functions. Our formalism is demonstrated for the important case of spin relaxation in a time-dependent external magnetic field. The Bloch equations are derived together with microscopic expressions for the relaxation times."]]></description>
<dc:subject>to:NB macro_from_micro stochastic_processes statistical_mechanics physics non-equilibrium</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4ab77cc771d6/</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:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_mechanics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:physics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:non-equilibrium"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.aps.org/pre/abstract/10.1103/PhysRevE.99.060101">
    <title>Phys. Rev. E 99, 060101(R) (2019) - Derivation of a Langevin equation in a system with multiple scales: The case of negative temperatures</title>
    <dc:date>2019-06-14T12:35:58+00:00</dc:date>
    <link>https://journals.aps.org/pre/abstract/10.1103/PhysRevE.99.060101</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We consider the problem of building a continuous stochastic model, i.e., a Langevin or Fokker-Planck equation, through a well-controlled coarse-graining procedure. Such a method usually involves the elimination of the fast degrees of freedom of the “bath” to which the particle is coupled. Specifically, we look into the general case where the bath may be at negative temperatures, as found, for instance, in models and experiments with bounded effective kinetic energy. Here, we generalize previous studies by considering the case in which the coarse graining leads to (i) a renormalization of the potential felt by the particle, and (ii) spatially dependent viscosity and diffusivity. In addition, a particular relevant example is provided, where the bath is a spin system and a sort of phase transition takes place when going from positive to negative temperatures. A Chapman-Enskog-like expansion allows us to rigorously derive the Fokker-Planck equation from the microscopic dynamics. Our theoretical predictions show excellent agreement with numerical simulations."]]></description>
<dc:subject>to:NB macro_from_micro statistical_mechanics stochastic_processes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:95ec797bed3e/</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: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:stochastic_processes"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1905.10402">
    <title>[1905.10402] Description of spreading dynamics by microscopic network models and macroscopic branching processes can differ due to coalescence</title>
    <dc:date>2019-05-28T16:54:45+00:00</dc:date>
    <link>https://arxiv.org/abs/1905.10402</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Spreading processes are conventionally monitored on a macroscopic level by counting the number of incidences over time. The spreading process can then be modeled either on the microscopic level, assuming an underlying interaction network, or directly on the macroscopic level, assuming that microscopic contributions are negligible. The macroscopic characteristics of both descriptions are commonly assumed to be identical. In this work, we show that these characteristics of microscopic and macroscopic descriptions can be different due to coalescence, i.e., a node being activated at the same time by multiple sources. In particular, we consider a (microscopic) branching network (probabilistic cellular automaton) with annealed connectivity disorder, record the macroscopic activity, and then approximate this activity by a (macroscopic) branching process. In this framework, we analytically calculate the effect of coalescence on the collective dynamics. We show that coalescence leads to a universal non-linear scaling function for the conditional expectation value of successive network activity. This allows us to quantify the difference between the microscopic model parameter and established macroscopic estimates. To overcome this difference, we propose a non-linear estimator that correctly infers the model branching parameter for all system sizes."]]></description>
<dc:subject>branching_processes macro_from_micro stochastic_processes epidemics_on_networks in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:93134456618b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:branching_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epidemics_on_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.annualreviews.org/doi/abs/10.1146/annurev-conmatphys-033117-054252">
    <title>The Fokker–Planck Approach to Complex Spatiotemporal Disordered Systems | Annual Review of Condensed Matter Physics</title>
    <dc:date>2019-05-26T17:57:18+00:00</dc:date>
    <link>https://www.annualreviews.org/doi/abs/10.1146/annurev-conmatphys-033117-054252</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["When the complete understanding of a complex system is not available, as, e.g., for systems considered in the real world, we need a top-down approach to complexity. In this approach, one may desire to understand general multipoint statistics. Here, such a general approach is presented and discussed based on examples from turbulence and sea waves. Our main idea is based on the cascade picture of turbulence, entangling fluctuations from large to small scales. Inspired by this cascade picture, we express the general multipoint statistics by the statistics of scale-dependent fluctuations of variables and relate it to a scale-dependent process, which finally is a stochastic cascade process. We show how to extract from empirical data a Fokker–Planck equation for this cascade process, which allows the generation of surrogate data to forecast extreme events as well as to develop a nonequilibrium thermodynamics for the complex systems. For each cascade event, an entropy production can be determined. These entropies accurately fulfill a rigorous law, namely the integral fluctuations theorem."

]]></description>
<dc:subject>to:NB stochastic_processes random_fields physics statistical_mechanics markov_models macro_from_micro non-equilibrium to_read color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:302abad9e4e5/</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:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:random_fields"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:physics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_mechanics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:markov_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:non-equilibrium"/>
	<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://link.springer.com/book/10.1007/1-84628-186-5#about">
    <title>Noise-Induced Phenomena in Slow-Fast Dynamical Systems | SpringerLink</title>
    <dc:date>2019-01-06T16:22:27+00:00</dc:date>
    <link>https://link.springer.com/book/10.1007/1-84628-186-5#about</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Stochastic differential equations play an increasingly important role in modeling the dynamics of a large variety of systems in the natural sciences, and in technological applications. This book is aimed at advanced undergraduate and graduate students, and researchers in mathematics, physics, the natural sciences, and engineering. It presents a new constructive approach to the quantitative description of solutions to systems of stochastic differential equations evolving on well-separated timescales. The method, which combines techniques from stochastic analysis and singular perturbation theory, allows the domains of concentration for typical sample paths to be determined, and provides precise estimates on the transition probabilities between these domains.
"In addition to the detailed presentation of the set-up and mathematical results, applications to problems in physics, biology, and climatology are discussed. The emphasis lies on noise-induced phenomena such as stochastic resonance, hysteresis, excitability, and the reduction of bifurcation delay."]]></description>
<dc:subject>to:NB books:noted downloaded stochastic_differential_equations macro_from_micro stochastic_processes re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c44eff012fc4/</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:downloaded"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_differential_equations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2684776">
    <title>The Non-Existence of Representative Agents by Matthew O. Jackson, Leeat Yariv :: SSRN</title>
    <dc:date>2018-07-18T14:33:24+00:00</dc:date>
    <link>https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2684776</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We characterize environments in which there exists a representative agent: an agent who inherits the structure of preferences of the population that she represents. The existence of such a representative agent imposes strong restrictions on individual utility functions -- requiring them to be linear in the allocation and additively separable in any parameter that characterizes agents' preferences (e.g., a risk aversion parameter, a discount factor, etc.). Commonly used classes of utility functions (exponentially discounted utility functions, CRRA or CARA utility functions, logarithmic functions, etc.) do not admit a representative agent."]]></description>
<dc:subject>economics macroeconomics macro_from_micro aggregation jackson.matthew_o. re:your_favorite_dsge_sucks in_NB have_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:92ae0b7d8444/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:aggregation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:jackson.matthew_o."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1704.06279">
    <title>[1704.06279] Mutual Information, Neural Networks and the Renormalization Group</title>
    <dc:date>2018-07-07T17:32:04+00:00</dc:date>
    <link>https://arxiv.org/abs/1704.06279</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Physical systems differing in their microscopic details often display strikingly similar behaviour when probed at low energies. Those universal properties, largely determining their physical characteristics, are revealed by the powerful renormalization group (RG) procedure, which systematically retains "slow" degrees of freedom and integrates out the rest. However, the important degrees of freedom may be difficult to identify. Here we demonstrate a machine learning (ML) algorithm capable of identifying the relevant degrees of freedom without any prior knowledge about the system. We introduce an artificial neural network based on a model-independent, information-theoretic characterization of a real-space RG procedure, performing this task. We apply the algorithm to classical statistical physics problems in two dimensions."]]></description>
<dc:subject>to:NB to_read information_theory renormalization statistical_mechanics macro_from_micro</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d27bba8416fa/</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:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:renormalization"/>
	<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:Bag></taxo:topics>
</item>
<item rdf:about="http://www.pnas.org/content/115/22/5714">
    <title>Weak Galilean invariance as a selection principle for coarse-grained diffusive models | PNAS</title>
    <dc:date>2018-05-31T19:16:56+00:00</dc:date>
    <link>http://www.pnas.org/content/115/22/5714</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["How does the mathematical description of a system change in different reference frames? Galilei first addressed this fundamental question by formulating the famous principle of Galilean invariance. It prescribes that the equations of motion of closed systems remain the same in different inertial frames related by Galilean transformations, thus imposing strong constraints on the dynamical rules. However, real world systems are often described by coarse-grained models integrating complex internal and external interactions indistinguishably as friction and stochastic forces. Since Galilean invariance is then violated, there is seemingly no alternative principle to assess a priori the physical consistency of a given stochastic model in different inertial frames. Here, starting from the Kac–Zwanzig Hamiltonian model generating Brownian motion, we show how Galilean invariance is broken during the coarse-graining procedure when deriving stochastic equations. Our analysis leads to a set of rules characterizing systems in different inertial frames that have to be satisfied by general stochastic models, which we call “weak Galilean invariance.” Several well-known stochastic processes are invariant in these terms, except the continuous-time random walk for which we derive the correct invariant description. Our results are particularly relevant for the modeling of biological systems, as they provide a theoretical principle to select physically consistent stochastic models before a validation against experimental data."]]></description>
<dc:subject>to:NB stochastic_processes macro_from_micro hydrodynamics physics statistical_mechanics classical_mechanics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:dbeca2cc3731/</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:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hydrodynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:physics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_mechanics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:classical_mechanics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://link.springer.com/article/10.1007/s11229-017-1341-z">
    <title>Intervening on structure | SpringerLink</title>
    <dc:date>2018-04-17T13:04:10+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/s11229-017-1341-z</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Some explanations appeal to facts about the causal structure of a system in order to shed light on a particular phenomenon; these are explanations which do more than cite the causes X and Y of some state-of-affairs Z, but rather appeal to “macro-level” causal features—for example the fact that A causes B as well as C, or perhaps that D is a strong inhibitor of E—in order to explain Z. Appeals to these kinds of “macro-level” causal features appear in a wide variety of social scientific and biological research; statements about features such as “patriarchy,” “healthcare infrastructure,” and “functioning DNA repair mechanism,” for instance, can be understood as claims about what would be different (with respect to some target phenomenon) in a system with a different causal structure. I suggest interpreting counterfactual questions involving structural features as questions about alternative parameter settings of causal models, and propose an extension of the usual interventionist framework for causal explanation which enables scientists to explore the consequences of interventions on “macro-level” structure."]]></description>
<dc:subject>to:NB causality macro_from_micro</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d7c39d81d24a/</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:macro_from_micro"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://link.springer.com/chapter/10.1007/978-3-319-11520-7_27">
    <title>A Novel Algorithm for Coarse-Graining of Cellular Automata - Springer</title>
    <dc:date>2016-12-09T16:11:04+00:00</dc:date>
    <link>http://link.springer.com/chapter/10.1007/978-3-319-11520-7_27</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The coarse-graining is an approximation procedure widely used for simplification of mathematical and numerical models of multiscale systems. It reduces superfluous – microscopic – degrees of freedom. Israeli and Goldenfeld demonstrated in [1,2] that the coarse-graining can be employed for elementary cellular automata (CA), producing interesting interdependences between them. However, extending their investigation on more complex CA rules appeared to be impossible due to the high computational complexity of the coarse-graining algorithm. We demonstrate here that this complexity can be substantially decreased. It allows for scrutinizing much broader class of cellular automata in terms of their coarse graining. By using our algorithm we found out that the ratio of the numbers of elementary CAs having coarse grained representation to “degenerate” – irreducible – cellular automata, strongly increases with increasing the “grain” size of the approximation procedure. This rises principal questions about the formal limits in modeling of realistic multiscale systems."]]></description>
<dc:subject>to:NB macro_from_micro approximation cellular_automata via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b8c0580f7c68/</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:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:approximation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cellular_automata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1512.07942">
    <title>[1512.07942] Multi-Level Cause-Effect Systems</title>
    <dc:date>2016-01-05T18:51:03+00:00</dc:date>
    <link>http://arxiv.org/abs/1512.07942</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We present a domain-general account of causation that applies to settings in which macro-level causal relations between two systems are of interest, but the relevant causal features are poorly understood and have to be aggregated from vast arrays of micro-measurements. Our approach generalizes that of Chalupka et al. (2015) to the setting in which the macro-level effect is not specified. We formalize the connection between micro- and macro-variables in such situations and provide a coherent framework describing causal relations at multiple levels of analysis. We present an algorithm that discovers macro-variable causes and effects from micro-level measurements obtained from an experiment. We further show how to design experiments to discover macro-variables from observational micro-variable data. Finally, we show that under specific conditions, one can identify multiple levels of causal structure. Throughout the article, we use a simulated neuroscience multi-unit recording experiment to illustrate the ideas and the algorithms."]]></description>
<dc:subject>to:NB to_read causality causal_inference macro_from_micro eberhardt.frederick kith_and_kin re:what_is_a_macrostate</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:28c034bbe5ac/</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:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:eberhardt.frederick"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:what_is_a_macrostate"/>
</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="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://arxiv.org/abs/1404.0667">
    <title>[1404.0667] ATLAS: A geometric approach to learning high-dimensional stochastic systems near manifolds</title>
    <dc:date>2014-04-14T19:47:28+00:00</dc:date>
    <link>http://arxiv.org/abs/1404.0667</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["When simulating multiscale stochastic differential equations (SDEs) in high-dimensions, separation of timescales, stochastic noise and high-dimensionality can make simulations prohibitively expensive. The computational cost is dictated by microscale properties and interactions of many variables, while interesting behavior often occurs at the macroscale level and at large time scales, often characterized by few important, but unknown, degrees of freedom. For many problems bridging the gap between the microscale and macroscale by direct simulation is computationally infeasible. In this work we propose a novel approach to automatically learn a reduced model with an associated fast macroscale simulator. Our unsupervised learning algorithm uses short parallelizable microscale simulations to learn provably accurate macroscale SDE models. The learning algorithm takes as input: the microscale simulator, a local distance function, and a homogenization spatial or temporal scale, which is the smallest time scale of interest in the reduced system. The learned macroscale model can then be used for fast computation and storage of long simulations. We discuss various examples, both low- and high-dimensional, as well as results about the accuracy of the fast simulators we construct, and its dependency on the number of short paths requested from the microscale simulator."]]></description>
<dc:subject>stochastic_differential_equations macro_from_micro simulation stochastic_processes in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5d4e3c2a7b80/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_differential_equations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:simulation"/>
	<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/1404.1466">
    <title>[1404.1466] Coarse-graining and fluctuations: Two birds with one stone</title>
    <dc:date>2014-04-14T19:44:46+00:00</dc:date>
    <link>http://arxiv.org/abs/1404.1466</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We show how the mathematical structure of large-deviation principles matches well with the concept of coarse-graining. For those systems with a large-deviation principle, this may lead to a general approach to coarse-graining through the variational form of the large-deviation functional."

--- I don't see anything that novel here, frankly.]]></description>
<dc:subject>large_deviations macro_from_micro stochastic_processes in_NB have_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:72c26e1d800b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_deviations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<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:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1306.4880">
    <title>[1306.4880] Derivation of Hydrodynamics from the Hamiltonian description of particle systems</title>
    <dc:date>2014-02-11T21:36:51+00:00</dc:date>
    <link>http://arxiv.org/abs/1306.4880</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Hamiltonian particle systems may exhibit non-linear hydrodynamic phenomena as the time evolution of the density fields of energy, momentum, and mass. In this Letter, an exact equation describing the time evolution is derived assuming the local Gibbs distribution at initial time. The key concept in the derivation is an identity similar to the fluctuation theorems. The Navier-Stokes equation is obtained as a result of simple perturbation expansions in a small parameter that represents the scale separation."]]></description>
<dc:subject>statistical_mechanics fluid_mechanics macro_from_micro physics in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:71b1c0587a58/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_mechanics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:fluid_mechanics"/>
	<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:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1312.0115">
    <title>[1312.0115] Fluctuation Spectra and Coarse Graining in Stochastic Dynamics</title>
    <dc:date>2014-01-02T17:54:10+00:00</dc:date>
    <link>http://arxiv.org/abs/1312.0115</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Fluctuations in small biological systems can be crucial for their function. Large-deviation theory characterizes such rare events from the perspective of stochastic processes. In most cases it is very difficult to directly determine the large-deviation functions. Circumventing this necessity, I present a method to quantify the fluctuation spectra for arbitrary Markovian models with finite state space. Under non-equilibrium conditions, current-like observables are of special interest. The space of all current-like observables has a natural decomposition into orthogonal complements. Remarkably, the fluctuation spectrum of any observable is entirely determined by only one of these components. The method is applied to study differences of fluctuations in setups sampling the same dynamics at different resolutions. Coarse graining relates these models and can be done in a way that preserves expectation values of observables. However, the effects of the coarse graining on the fluctuations are not obvious. These differences are explicitly worked out for a simple model system."]]></description>
<dc:subject>to:NB macro_from_micro large_deviations markov_models stochastic_processes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b433a11f8ae8/</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:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_deviations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:markov_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1310.3188">
    <title>[1310.3188] Renormalisation as an inference problem</title>
    <dc:date>2013-10-17T13:08:03+00:00</dc:date>
    <link>http://arxiv.org/abs/1310.3188</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In physics we attempt to infer the rules governing a system given only the results of imprecise measurements. This is an ill-posed problem because certain features of the system's state cannot be resolved by the measurements. However, by ignoring the irrelevant features, an effective theory can be made for the remaining observable relevant features. We explain how these relevant and irrelevant degrees of freedom can be concretely characterised using quantum distinguishability metrics, thus solving the ill-posed inference problem. This framework then allows us to provide an information-theoretic formulation of the renormalisation group, applicable to both statistical physics and quantum field theory. Using this formulation we argue that, given a natural model for an experimentalist's spatial and field-strength measurement uncertainties, the set of Gaussian states emerges as the relevant manifold of effective states and the n-point correlation functions correspond to the relevant observables. Our methods also provide a way to extend renormalisation techniques to effective models which are not based on the usual quantum field formalism. In particular, we can explain in elementary terms, using the example of a simple classical system, some of the problems occurring in quantum field theory and their solution."]]></description>
<dc:subject>to:NB statistical_mechanics renormalization macro_from_micro statistics color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8cc969c142fc/</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:renormalization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.nature.com/nphys/journal/v9/n10/full/nphys2741.html">
    <title>Universality in network dynamics : Nature Physics : Nature Publishing Group</title>
    <dc:date>2013-10-12T00:14:50+00:00</dc:date>
    <link>http://www.nature.com/nphys/journal/v9/n10/full/nphys2741.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Despite significant advances in characterizing the structural properties of complex networks, a mathematical framework that uncovers the universal properties of the interplay between the topology and the dynamics of complex systems continues to elude us. Here we develop a self-consistent theory of dynamical perturbations in complex systems, allowing us to systematically separate the contribution of the network topology and dynamics. The formalism covers a broad range of steady-state dynamical processes and offers testable predictions regarding the system’s response to perturbations and the development of correlations. It predicts several distinct universality classes whose characteristics can be derived directly from the continuum equation governing the system’s dynamics and which are validated on several canonical network-based dynamical systems, from biochemical dynamics to epidemic spreading. Finally, we collect experimental data pertaining to social and biological systems, demonstrating that we can accurately uncover their universality class even in the absence of an appropriate continuum theory that governs the system’s dynamics."]]></description>
<dc:subject>to:NB macro_from_micro networks statistical_mechanics color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:828dc76c7774/</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:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_mechanics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://pre.aps.org/abstract/PRE/v88/i3/e032704">
    <title>Phys. Rev. E 88, 032704 (2013): Modeling biological tissue growth: Discrete to continuum representations</title>
    <dc:date>2013-09-04T23:52:25+00:00</dc:date>
    <link>http://pre.aps.org/abstract/PRE/v88/i3/e032704</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["There is much interest in building deterministic continuum models from discrete agent-based models governed by local stochastic rules where an agent represents a biological cell. In developmental biology, cells are able to move and undergo cell division on and within growing tissues. A growing tissue is itself made up of cells which undergo cell division, thereby providing a significant transport mechanism for other cells within it. We develop a discrete agent-based model where domain agents represent tissue cells. Each agent has the ability to undergo a proliferation event whereby an additional domain agent is incorporated into the lattice. If a probability distribution describes the waiting times between proliferation events for an individual agent, then the total length of the domain is a random variable. The average behavior of these stochastically proliferating agents defining the growing lattice is determined in terms of a Fokker-Planck equation, with an advection and diffusion term. The diffusion term differs from the one obtained Landman and Binder [ J. Theor. Biol. 259 541 (2009)] when the rate of growth of the domain is specified, but the choice of agents is random. This discrepancy is reconciled by determining a discrete-time master equation for this process and an associated asymmetric nonexclusion random walk, together with consideration of synchronous and asynchronous updating schemes. All theoretical results are confirmed with numerical simulations. This study furthers our understanding of the relationship between agent-based rules, their implementation, and their associated partial differential equations. Since tissue growth is a significant cellular transport mechanism during embryonic growth, it is important to use the correct partial differential equation description when combining with other cellular functions."]]></description>
<dc:subject>biophysics developmental_biology agent-based_models macro_from_micro physics stochastic_processes in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:dcaced5a255f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biophysics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:developmental_biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:agent-based_models"/>
	<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:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1508697/">
    <title>The right answer for the wrong question: consequences of type III error for public health research.</title>
    <dc:date>2013-07-09T15:12:35+00:00</dc:date>
    <link>http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1508697/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["OBJECTIVES: This study examined the impact of assessing the causes of interindividual variation within a population when the research question of interest is about causes of differences between populations or time periods. This discrepancy between the research focus and the research question is referred to as a type III error, one that provides the right answer for the wrong question. METHODS: Homelessness, obesity, and infant mortality were used to illustrate different consequences of type III errors. These different consequences depend on the relationships between the causes of within- and between-group variation. CONCLUSIONS: The causes of inter-individual variation and the causes of variation between populations and time periods may be distinct. The problem of examining invariant causes deserves attention."]]></description>
<dc:subject>to:NB have_skimmed causal_inference epidemiology to_teach:undergrad-ADA macro_from_micro social_science_methodology ecological_inference_and_the_ecological_fallacy</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ded101a3a4d7/</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:have_skimmed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epidemiology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_science_methodology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ecological_inference_and_the_ecological_fallacy"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1301.7697">
    <title>[1301.7697] Stochastic dynamics on slow manifolds</title>
    <dc:date>2013-07-01T18:52:09+00:00</dc:date>
    <link>http://arxiv.org/abs/1301.7697</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The theory of slow manifolds is an important tool in the study of deterministic dynamical systems, giving a practical method by which to reduce the number of relevant degrees of freedom in a model, thereby often resulting in a considerable simplification. In this article we demonstrate how the same basic methodology may also be applied to stochastic dynamical systems, by examining the behaviour of trajectories conditioned on the event that they do not depart the slow manifold. We apply the method to two models: one from ecology and one from epidemiology, achieving a reduction in model dimension and illustrating the high quality of the analytical approximations."]]></description>
<dc:subject>to:NB stochastic_processes low-dimensional_summaries dynamical_systems macro_from_micro averaged_equations_of_motion</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1b12860623eb/</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:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:low-dimensional_summaries"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:averaged_equations_of_motion"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1304.7700">
    <title>[1304.7700] Information-theoretic tools for parametrized coarse-graining of non-equilibrium extended systems</title>
    <dc:date>2013-05-01T20:31:17+00:00</dc:date>
    <link>http://arxiv.org/abs/1304.7700</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this paper we focus on the development of new methods suitable for efficient and reliable coarse-graining of {\it non-equilibrium} molecular systems. In this context, we propose error estimation and controlled-fidelity model reduction methods based on Path-Space Information Theory, and combine it with statistical parametric estimation of rates for non-equilibrium stationary processes. The approach we propose extends the applicability of existing information-based methods for deriving parametrized coarse-grained models to Non-Equilibrium systems with Stationary States (NESS). In the context of coarse-graining it allows for constructing optimal parametrized Markovian coarse-grained dynamics, by minimizing information loss (due to coarse-graining) on the path space. Furthermore, the associated path-space Fisher Information Matrix can provide confidence intervals for the corresponding parameter estimators. We demonstrate the proposed coarse-graining method in a non-equilibrium system with diffusing interacting particles, driven by out-of-equilibrium boundary conditions."]]></description>
<dc:subject>to:NB macro_from_micro stochastic_processes markov_models information_theory fisher_information non-equilibrium low-dimensional_summaries</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c38ba0ef4f26/</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:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<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:fisher_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:non-equilibrium"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:low-dimensional_summaries"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1304.6603">
    <title>[1304.6603] Optimal Kullback-Leibler Aggregation via Information Bottleneck</title>
    <dc:date>2013-04-25T16:42:40+00:00</dc:date>
    <link>http://arxiv.org/abs/1304.6603</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper presents a method for reduction of Markov models with large state spaces based on information-theoretic criteria. As a cost function we define the Kullback-Leibler divergence rate between the process obtained by simply partitioning the alphabet of the original chain (which in general is not Markov) and its best Markov approximation. We further show that the Kullback-Leibler divergence rate between the original chain and the lifting of the optimal Markov approximation yields an easy-to-compute upper bound on the cost function. 
"By properly defining the lifting, the present work obtains a reduction which is closely related to the notion of lumpability. It is further shown that the cost function can be minimized by employing the information bottleneck method, thus building a bridge between Markov theory, control systems, and machine learning."]]></description>
<dc:subject>to:NB information_theory macro_from_micro markov_models stochastic_processes information_bottleneck re:AoS_project to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5c22017d9090/</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:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:markov_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_bottleneck"/>
	<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: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://www.jstor.org/stable/10.1086/505471">
    <title>JSTOR: Philosophy of Science, Vol. 72, No. 4 (October 2005), pp. 531-556</title>
    <dc:date>2013-03-01T18:46:01+00:00</dc:date>
    <link>http://www.jstor.org/stable/10.1086/505471</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["To understand the behavior of a complex system, one must understand the interactions among its parts. Doing so is difficult for nondecomposable systems, in which the interactions strongly influence the short term behavior of the parts. Science’s principal tool for dealing with nondecomposable systems is a variety of probabilistic analysis that I call EPA. I show that EPA’s power derives from an assumption that appears to be false of nondecomposable complex systems, in virtue of their very nondecomposability. Yet EPA is extremely successful. I aim to find an interpretation of EPA’s assumption that is consistent with, indeed that explains, its success."

--- Dude really needs to learn about Kurtz's Theorem.]]></description>
<dc:subject>in_NB have_read philosophy_of_science complexity macro_from_micro</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b67dc35c3ff1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.aoap/1360682022">
    <title>Kang , Kurtz : Separation of time-scales and model reduction for stochastic reaction networks</title>
    <dc:date>2013-02-14T14:13:58+00:00</dc:date>
    <link>http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.aoap/1360682022</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A stochastic model for a chemical reaction network is embedded in a one-parameter family of models with species numbers and rate constants scaled by powers of the parameter. A systematic approach is developed for determining appropriate choices of the exponents that can be applied to large complex networks. When the scaling implies subnetworks have different time-scales, the subnetworks can be approximated separately, providing insight into the behavior of the full network through the analysis of these lower-dimensional approximations."]]></description>
<dc:subject>to:NB stochastic_processes macro_from_micro convergence_of_stochastic_processes kurtz.thomas_g.</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e2a06c855c77/</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:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:convergence_of_stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kurtz.thomas_g."/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1212.4375">
    <title>[1212.4375] Lumped Markov chains and entropy rate</title>
    <dc:date>2012-12-19T13:36:13+00:00</dc:date>
    <link>http://arxiv.org/abs/1212.4375</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A lumping of a Markov chain is a coordinate-wise projection of the chain. We characterise the entropy rate loss of a lumping of an ergodic Markov chain on a countable state space in two ways: First, by the random growth rate of the number of trajectories with positive probability of the original chain lumped to the same image. Second, by the possibility to reconstruct original trajectories from their lumped images. The latter is a purely combinatorial criterion, depending only on the transition graph of the Markov chain and the lumping function. Every non-trivial lumping of a Markov chain with positive transition matrix incurs an entropy rate loss. We give sufficient conditions on the non-positive transition matrix and the lumping to preserve the entropy rate. In the sparse setting, we state sufficient conditions on the lumping to both preserve the entropy rate and result in a k-th order homogeneous Markov chain."]]></description>
<dc:subject>to:NB markov_models stochastic_processes information_theory macro_from_micro re:what_is_a_macrostate</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b073c29f603c/</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:markov_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:what_is_a_macrostate"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1208.3080">
    <title>[1208.3080] Multi-level Dynamical Systems: Connecting the Ruelle Response Theory and the Mori-Zwanzig Approach</title>
    <dc:date>2012-09-04T02:06:07+00:00</dc:date>
    <link>http://arxiv.org/abs/1208.3080</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this paper we consider the problem of disentangling multi-level systems by connecting the seemingly unrelated approaches of the Mori- Zwanzig projection operator technique and of the Ruelle response theory, for which we propose a new derivation. In a previous paper we have shown that by using the Ruelle response theory on a weakly coupled system it is possible to construct a surrogate dynamics for the slow variables, such that the expectation value of any observable agrees, up to second order in the coupling strength, to its expectation evaluated on the full dynamics, where both slow and fast variables are involved. We show here that such surrogate dynamics agrees up to second order to the effective dynamics one can derive by expanding perturbatively the Mori-Zwanzing projection operator, which creates, instead, an accurate representation of the trajec- tories of the slow variables. In the case of e.g. geophysical fluid dynamics, this implies that the parametrizations of unresolved processes suited for prediction (numerical weather forecast) and those suited for the represen- tation of long term statistical properties (climate) are closely related, if one takes into account, in addition to the widely adopted stochastic forc- ing, the usually neglected memory effects. This bears relevance for the current trend of aiming at seamless prediction."

Journal version: http://dx.doi.org/10.1007/s10955-013-0726-8 --- abstract suggests non-trivial changes]]></description>
<dc:subject>dynamical_systems macro_from_micro time_series prediction statistical_mechanics in_NB non-equilibrium stochastic_processes averaged_equations_of_motion low-dimensional_summaries mori-zwanzig</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:53f8aed04405/</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:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_mechanics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:non-equilibrium"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:averaged_equations_of_motion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:low-dimensional_summaries"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mori-zwanzig"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1207.2692">
    <title>[1207.2692] An optimization principle for deriving nonequilibrium statistical models of Hamiltonian dynamics</title>
    <dc:date>2012-07-12T01:55:26+00:00</dc:date>
    <link>http://arxiv.org/abs/1207.2692</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A general method for deriving closed reduced models of Hamiltonian dynamical systems is developed using techniques from optimization and statistical estimation. As in standard projection operator methods, a set of resolved variables is selected to capture the slow, macroscopic behavior of the system, and the family of quasi-equilibrium probability densities on phase space corresponding to these resolved variables is employed as a statistical model. The macroscopic dynamics of the mean resolved variables is determined by optimizing over paths of these probability densities. Specifically, a cost function is introduced that quantifies the lack-of-fit of such paths to the underlying microscopic dynamics; it is an ensemble-averaged, squared-norm of the residual that results from submitting a path of trial densities to the Liouville equation. The evolution of the macrostate is estimated by minimizing the time integral of the cost function. The value function for this optimization satisfies the associated Hamilton-Jacobi equation, and it determines the optimal relation between the statistical parameters and the irreversible fluxes of the resolved variables, thereby closing the reduced dynamics. The resulting equations for the macroscopic variables have the generic form of governing equations for nonequilibrium thermodynamics, and they furnish a rational extension of the classical equations of linear irreversible thermodynamics beyond the near-equilibrium regime. In particular, the value function is a thermodynamic potential that extends the classical dissipation function and supplies the nonlinear relation between thermodynamics forces and fluxes."

- Why would you want to minimize L2 distance between density trajectories?  (I mean, why specifically L2?  Why not L1 or Hellinger or KL divergence?)  Seems arbitrary.

Journal version: http://dx.doi.org/10.1007/s10955-013-0778-9 - the abstract suggests non-trivial changes]]></description>
<dc:subject>non-equilibrium macro_from_micro statistical_mechanics stochastic_processes re:almost_none optimization dynamical_systems re:what_is_a_macrostate averaged_equations_of_motion low-dimensional_summaries in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:513a091315d1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:non-equilibrium"/>
	<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:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:almost_none"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
	<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:averaged_equations_of_motion"/>
	<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://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://arxiv.org/abs/1206.6846">
    <title>[1206.6846] Approximate Separability for Weak Interaction in Dynamic Systems</title>
    <dc:date>2012-07-09T03:45:44+00:00</dc:date>
    <link>http://arxiv.org/abs/1206.6846</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["One approach to monitoring a dynamic system relies on decomposition of the system into weakly interacting subsystems. An earlier paper introduced a notion of weak interaction called separability, and showed that it leads to exact propagation of marginals for prediction. This paper addresses two questions left open by the earlier paper: can we define a notion of approximate separability that occurs naturally in practice, and do separability and approximate separability lead to accurate monitoring? The answer to both questions is afirmative. The paper also analyzes the structure of approximately separable decompositions, and provides some explanation as to why these models perform well."]]></description>
<dc:subject>to:NB dynamical_systems complexity macro_from_micro</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3a5adaba1315/</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:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://riscd2.eco.ub.es/~josepgon/documents/Felipe_Fisher.pdf">
    <title>AGGREGATION IN PRODUCTION FUNCTIONS: WHAT APPLIED ECONOMISTS SHOULD KNOW</title>
    <dc:date>2012-06-18T19:55:31+00:00</dc:date>
    <link>http://riscd2.eco.ub.es/~josepgon/documents/Felipe_Fisher.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper surveys the theoretical literature on aggregation of production functions. The objective is to make neoclassical economists aware of the insurmountable aggregation problems and their implications. We refer to both the Cambridge capital controversies and the aggregation conditions. The most salient results are summarized, and the problems that economists should be aware of from incorrect aggregation are discussed. The most important conclusion is that the conditions under which a well-behaved aggregate production function can be derived from micro production functions are so stringent that it is difficult to believe that actual economies satisfy them. Therefore, aggregate production functions do not have a sound theoretical foundation. For practical purposes this means that while generating GDP, for example, as the sum of the components of aggregate demand (or through the production or income sides of the economy) is correct, thinking of GDP as GDP = F(K, L), where K and L are aggregates of capital and labor, respectively, and F(•) is a well-defined neoclassical function, is most likely incorrect. Likewise, thinking of aggregate investment as a well-defined addition to
‘capital’ in production is also a mistake. The paper evaluates the standard reasons given by economists for continuing to use aggregate production functions in theoretical and applied work, and concludes that none of them provides a valid argument."

--- They are not altogether fair to the instrumentalist, it-works-doesn't-it, defense.  (I'm not saying that defense is right, just that they don't really treat it fairly, which would involve looking into how aggregate production functions are supposed to work, and assessing the evidence that they do in fact, do those jobs well.)]]></description>
<dc:subject>in_NB economics macro_from_micro re:your_favorite_dsge_sucks via:crooked_timber econometrics cobb-douglas_production_functions have_read fisher.franklin_m.</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:31f323baf3f8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:crooked_timber"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cobb-douglas_production_functions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:fisher.franklin_m."/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://prl.aps.org/abstract/PRL/v108/i22/e228101">
    <title>Phys. Rev. Lett. 108, 228101 (2012): Fluctuation-Preserving Coarse Graining for Biochemical Systems</title>
    <dc:date>2012-05-29T17:59:14+00:00</dc:date>
    <link>http://prl.aps.org/abstract/PRL/v108/i22/e228101</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Finite stochastic Markov models play a major role in modeling biological systems. Such models are a coarse-grained description of the underlying microscopic dynamics and can be considered mesoscopic. The level of coarse-graining is to a certain extent arbitrary since it depends on the resolution of accommodating measurements. Here we present a systematic way to simplify such stochastic descriptions which preserves both the meso-micro and the meso-macro connections. The former is achieved by demanding locality, the latter by considering cycles on the network of states. Our method preserves fluctuations of observables much better than naïve approaches."]]></description>
<dc:subject>to:NB stochastic_processes convergence_of_stochastic_processes markov_models macro_from_micro</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2e33f5482841/</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:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:convergence_of_stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:markov_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1204.0321">
    <title>[1204.0321] The averaging principle</title>
    <dc:date>2012-04-03T12:54:43+00:00</dc:date>
    <link>http://arxiv.org/abs/1204.0321</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Typically, models with a heterogeneous property are considerably harder to analyze than the corresponding homogeneous models, in which the heterogeneous property is replaced with its average value. In this study we show that any outcome of a heterogeneous model that satisfies the two properties of emph{differentiability} and emph{interchangibility}, is $O(epsilon^2)$ equivalent to the outcome of the corresponding homogeneous model, where $epsilon$ is the level of heterogeneity. We then use this emph{averaging principle} to obtain new results in queueing theory, game theory (auctions), and social networks (marketing)."

--- The claim in the abstract seems far too general to be true.]]></description>
<dc:subject>to:NB to_read macro_from_micro averaged_equations_of_motion</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:83ec16055f47/</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:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:averaged_equations_of_motion"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1110.5216">
    <title>[1110.5216] Large deviation approach to nonequilibrium systems</title>
    <dc:date>2011-10-25T12:17:49+00:00</dc:date>
    <link>http://arxiv.org/abs/1110.5216</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The theory of large deviations has been applied successfully in the last 30 years or so to study the properties of equilibrium systems and to put the foundations of equilibrium statistical mechanics on a clearer and more rigorous footing. A similar approach has been followed more recently for nonequilibrium systems, especially in the context of interacting particle systems. We review here the basis of this approach, emphasizing the similarities and differences that exist between the application of large deviation theory for studying equilibrium systems on the one hand and nonequilibrium systems on the other. Of particular importance are the notions of macroscopic, hydrodynamic, and long-time limits, which are analogues of the equilibrium thermodynamic limit, and the notion of statistical ensembles which can be generalized to nonequilibrium systems. For the purpose of illustrating our discussion, we focus on applications to Markov processes, in particular to simple random walks."]]></description>
<dc:subject>statistical_mechanics non-equilibrium large_deviations interacting_particle_systems hydrodynamic_limits macro_from_micro touchette.hugo in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:816604e4eea0/</dc:identifier>
<taxo:topics><rdf:Bag>	<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:large_deviations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:interacting_particle_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hydrodynamic_limits"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:touchette.hugo"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>