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  </channel><item rdf:about="https://link.springer.com/book/10.1007/978-1-4684-8941-5">
    <title>Adaptive Control of Ill-Defined Systems [1984] | Springer Nature Link</title>
    <dc:date>2026-03-11T14:05:04+00:00</dc:date>
    <link>https://link.springer.com/book/10.1007/978-1-4684-8941-5</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["There are some types of complex systems that are built like clockwork, with well-defined parts that interact in well-defined ways, so that the action of the whole can be precisely analyzed and anticipated with accuracy and precision. Some systems are not themselves so well-defined, but they can be modeled in ways that are like trained pilots in well-built planes, or electrolyte balance in healthy humans. But there are many systems for which that is not true; and among them are many whose understanding and control we would value. For example, the model for the trained pilot above fails exactly where the pilot is being most human; that is, where he is exercising the highest levels of judgment, or where he is learning and adapting to new conditions. Again, sometimes the kinds of complexity do not lead to easily analyzable models at all; here we might include most economic systems, in all forms of societies. There are several factors that seem to contribute to systems being hard to model, understand, or control. The human participants may act in ways that are so variable or so rich or so interactive that the only adequate model of the system would be the entire system itself, so to speak. This is probably the case in true long term systems involving people learning and growing up in a changing society."]]></description>
<dc:subject>to:NB books:noted control_theory_and_control_engineering complexity selfridge.oliver arbib.michael_a. via:mraginsky downloaded</dc:subject>
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<item rdf:about="https://arxiv.org/abs/2312.14959">
    <title>[2312.14959] Remembering Nino Boccara (1931--2018)</title>
    <dc:date>2025-09-24T16:39:48+00:00</dc:date>
    <link>https://arxiv.org/abs/2312.14959</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In commemoration of the fifth anniversary since Nino Boccara's departure, this article offers some personal recollections and provides insight into his life and accomplishments. Detailed bibliography of his works is included together with commentary highlighting his major achievements."]]></description>
<dc:subject>have_read cellular_automata complexity lives_of_the_scientists re:genealogy_of_complexity in_NB</dc:subject>
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<dc:identifier>https://pinboard.in/u:cshalizi/b:0b4ad0c6ffa2/</dc:identifier>
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    <title>Cognitive microfoundations and social interaction dynamics. The implications of complexity for institutional theory | Theory and Society</title>
    <dc:date>2025-03-02T14:46:53+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/s11186-024-09574-3</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper investigates the intersection of cognitive sciences and social network theory and its counterpart, the complexity sciences, aiming to shed light on the compatibility and potential integration of these frameworks into institutional theory. Institutional scholars have for long selectively adopted notions linked with the cognitive sciences and complexity sciences, such as the notion of path dependence, without exploring the broader implications of systematically integrating such perspectives into institutionalism. This paper aims to advance such a comprehensive theoretical integration, by investigating the effective combination of these approaches and their significant implications. It shows how the complexity sciences contribute to dissolving the barriers between the cognitive and social realms and illustrates how this impacts notions of human agency and reflexivity. Theoretical integration also involves acknowledging considerable diversity in individual human agency, which in turn prompts a reconsideration of how notions of institutional stability, change, diffusion and adaptation are understood. Furthermore, the paper addresses the epistemological challenge presented by the complexity sciences, before it highlights the general relevance of institutional theory in analyzing complex social phenomena. Finally, the paper explores implications for research methodology, proposing that a fusion of institutional theory and the complexity sciences provides a metatheoretical framework for assessing the contextual suitability of different theoretical and methodological approaches."]]></description>
<dc:subject>to:NB institutions cognitive_science complexity re:do-institutions-evolve</dc:subject>
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<item rdf:about="https://www.aeaweb.org/articles?id=10.1257/aer.20221227">
    <title>Decisions under Risk Are Decisions under Complexity - American Economic Association</title>
    <dc:date>2025-02-03T00:55:27+00:00</dc:date>
    <link>https://www.aeaweb.org/articles?id=10.1257/aer.20221227</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We provide evidence that classic lottery anomalies like probability weighting and loss aversion are not special phenomena of risk. They also arise (and often with equal strength) when subjects evaluate deterministic, positive monetary payments that have been disaggregated to resemble lotteries. Thus, we find, e.g., apparent probability weighting in settings without probabilities and loss aversion in settings without scope for loss. Across subjects, anomalies in these deterministic tasks strongly predict the same anomalies in lotteries. These findings suggest that much of the behavior motivating our most important behavioral theories of risk derive from complexity-driven mistakes rather than true risk preferences."]]></description>
<dc:subject>to:NB decision-making economics complexity</dc:subject>
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<dc:identifier>https://pinboard.in/u:cshalizi/b:f93b7fe27dea/</dc:identifier>
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<item rdf:about="https://archive.org/details/puppetswithoutstrings">
    <title>Puppets Without Strings (Science For Everyone) : V. I. Varshavsky, D. A. Pospelov : Free Download, Borrow, and Streaming : Internet Archive</title>
    <dc:date>2024-05-14T13:46:44+00:00</dc:date>
    <link>https://archive.org/details/puppetswithoutstrings</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- 1988 translation of 1984 book.]]></description>
<dc:subject>in_NB complexity distributed_systems self-organization downloaded via:mraginsky books:noted</dc:subject>
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<dc:identifier>https://pinboard.in/u:cshalizi/b:fd4aa4677f78/</dc:identifier>
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    <title>Vol. 18, No. 2, 2010, COMPLEXITY AND THE ORGANIZATION OF ECONOMIC LIFE (_History of Economic Ideas_)</title>
    <dc:date>2023-05-02T20:40:10+00:00</dc:date>
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    <dc:creator>cshalizi</dc:creator><dc:subject>to_read economics complexity history_of_economics history_of_ideas history_of_science re:genealogy_of_complexity via:rvenkat downloaded in_NB</dc:subject>
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<item rdf:about="https://arxiv.org/abs/2111.14377">
    <title>[2111.14377] Collective Intelligence for Deep Learning: A Survey of Recent Developments</title>
    <dc:date>2023-03-24T16:57:45+00:00</dc:date>
    <link>https://arxiv.org/abs/2111.14377</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In the past decade, we have witnessed the rise of deep learning to dominate the field of artificial intelligence. Advances in artificial neural networks alongside corresponding advances in hardware accelerators with large memory capacity, together with the availability of large datasets enabled practitioners to train and deploy sophisticated neural network models that achieve state-of-the-art performance on tasks across several fields spanning computer vision, natural language processing, and reinforcement learning. However, as these neural networks become bigger, more complex, and more widely used, fundamental problems with current deep learning models become more apparent. State-of-the-art deep learning models are known to suffer from issues that range from poor robustness, inability to adapt to novel task settings, to requiring rigid and inflexible configuration assumptions. Collective behavior, commonly observed in nature, tends to produce systems that are robust, adaptable, and have less rigid assumptions about the environment configuration. Collective intelligence, as a field, studies the group intelligence that emerges from the interactions of many individuals. Within this field, ideas such as self-organization, emergent behavior, swarm optimization, and cellular automata were developed to model and explain complex systems. It is therefore natural to see these ideas incorporated into newer deep learning methods. In this review, we will provide a historical context of neural network research's involvement with complex systems, and highlight several active areas in modern deep learning research that incorporate the principles of collective intelligence to advance its current capabilities. We hope this review can serve as a bridge between the complex systems and deep learning communities."]]></description>
<dc:subject>to:NB neural_networks distributed_systems ensemble_methods complexity self-organization cellular_automata to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
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<item rdf:about="https://arxiv.org/abs/2206.02279">
    <title>[2206.02279] Assembly Theory Explains and Quantifies the Emergence of Selection and Evolution</title>
    <dc:date>2022-06-09T08:39:12+00:00</dc:date>
    <link>https://arxiv.org/abs/2206.02279</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Since the time of Darwin, scientists have struggled to reconcile the evolution of biological forms in a universe determined by fixed laws. These laws underpin the origin of life, evolution, human culture and technology, as set by the boundary conditions of the universe, however these laws cannot predict the emergence of these things. By contrast evolutionary theory works in the opposite direction, indicating how selection can explain why some things exist and not others. To understand how open-ended forms can emerge in a forward-process from physics that does not include their design, a new approach to understand the non-biological to biological transition is necessary. Herein, we present a new theory, Assembly Theory (AT), which explains and quantifies the emergence of selection and evolution. In AT, the complexity of an individual observable object is measured by its Assembly Index (a), defined as the minimal number of steps needed to construct the object from basic building blocks. Combining a with the copy number defines a new quantity called Assembly which quantifies the amount of selection required to produce a given ensemble of objects. We investigate the internal structure and properties of assembly space and quantify the dynamics of undirected exploratory processes as compared to the directed processes that emerge from selection. The implementation of assembly theory allows the emergence of selection in physical systems to be quantified at any scale as the transition from undirected-discovery dynamics to a selected process within the assembly space. This yields a mechanism for the onset of selection and evolution and a formal approach to defining life. Because the assembly of an object is easily calculatable and measurable it is possible to quantify a lower limit on the amount of selection and memory required to produce complexity uniquely linked to biology in the universe."]]></description>
<dc:subject>evolutionary_biology complexity complexity_measures lachmann.michael kith_and_kin in_NB</dc:subject>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:lachmann.michael"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://doi.org/10.12987/9780300256130">
    <title>What Is a Complex System? | Yale University Press</title>
    <dc:date>2021-06-29T12:53:01+00:00</dc:date>
    <link>https://doi.org/10.12987/9780300256130</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["What is a complex system? Although “complexity science” is used to understand phenomena as diverse as the behavior of honeybees, the economic markets, the human brain, and the climate, there is no agreement about its foundations. In this introduction for students, academics, and general readers, philosopher of science James Ladyman and physicist Karoline Wiesner develop an account of complexity that brings the different concepts and mathematical measures applied to complex systems into a single framework. They introduce the different features of complex systems, discuss different conceptions of complexity, and develop their own account. They explain why complexity science is so important in today’s world."]]></description>
<dc:subject>books:noted complexity wiesner.karoline books:owned to_read downloaded in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e2ad9a16e073/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:wiesner.karoline"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
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</item>
<item rdf:about="https://www.journals.uchicago.edu/doi/abs/10.1086/711501">
    <title>Why Simpler Computer Simulation Models Can Be Epistemically Better for Informing Decisions | Philosophy of Science: Vol 88, No 2</title>
    <dc:date>2021-04-09T17:46:15+00:00</dc:date>
    <link>https://www.journals.uchicago.edu/doi/abs/10.1086/711501</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["For computer simulation models to usefully inform climate risk management, uncertainties in model projections must be explored and characterized. Because doing so requires running the model many times over, and because computing resources are finite, uncertainty assessment is more feasible using models that demand less computer processor time. Such models are generally simpler in the sense of being more idealized, or less realistic. So modelers face a trade-off between realism and uncertainty quantification. Seeing this trade-off for the important epistemic issue that it is requires a shift in perspective from the established simplicity literature in philosophy of science."]]></description>
<dc:subject>simulation philosophy_of_science risk_assessment climate_change complexity in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:410a0b30abb9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:risk_assessment"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:climate_change"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://doi.org/10.1093/oso/9780198821939.001.0001">
    <title>Introduction to the Theory of Complex Systems - Oxford Scholarship</title>
    <dc:date>2021-01-16T07:53:53+00:00</dc:date>
    <link>https://doi.org/10.1093/oso/9780198821939.001.0001</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This book is a comprehensive introduction to quantitative approaches to complex adaptive systems. Practically all areas of life on this planet are constantly confronted with complex systems, be it ecosystems, societies, traffic, financial markets, opinion formation, epidemic spreading, or the internet and social media. Complex systems are systems composed of many elements that interact with each other, which makes them extremely rich dynamical systems showing a huge range of phenomena. Properties of complex systems that are of particular importance are their efficiency, robustness, resilience, and proneness to collapse. The quantitative tools and concepts needed to understand the co-evolutionary nature of networked systems and their properties are challenging. The intention of the book is to give a self-contained introduction to these concepts so that the reader will be equipped with a conceptual and mathematical toolset that allows her to engage in the science of complex systems. Topics covered include random processes of path-dependent processes, co-evolutionary dynamics, the statistics of driven nonequilibrium systems, dynamics of networks, the theory of scaling, and approaches from statistical mechanics and information theory. The book extends well beyond the early classical literature in the field of complex systems and summarizes the methodological progress over the past twenty years in a clear, structured, and comprehensive way. The book is intended for natural scientists and graduate students."]]></description>
<dc:subject>books:noted complexity in_NB downloaded</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:375a2f44b3cb/</dc:identifier>
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<item rdf:about="https://journals.sagepub.com/doi/abs/10.1177/0073275320938295">
    <title>The failed institutionalization of “complexity science”: A focus on the Santa Fe Institute’s legitimization strategy - Fabrizio Li Vigni, 2020</title>
    <dc:date>2020-10-01T21:42:23+00:00</dc:date>
    <link>https://journals.sagepub.com/doi/abs/10.1177/0073275320938295</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["“Complexity sciences” are an interdisciplinary and transnational domain of study that aims at modeling natural and social “complex systems.” They appeared in the 1970s in Europe and the United States, but were boosted in the mid-1980s by the Santa Fe Institute (SFI) under the formula of “science of complexity.” This small but famous institution is the object of the present article. According to their promissory ambitions and to the enthusiastic claims of some scientific journalists, complexity sciences were going to revolutionize all of knowledge and even private and public actors who had learned to master them. In the light of this, one would expect to observe a well-established and autonomous research and educational field, capable of reproducing itself through professional institutions. Yet this is not the case. To explain the paradox, I propose to combine different models of history and sociology of emergent and declining domains, in order to give account of the rise and failure of complexity sciences."]]></description>
<dc:subject>to_read complexity history_of_science in_NB re:genealogy_of_complexity</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8e87d4682517/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_science"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:genealogy_of_complexity"/>
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<item rdf:about="https://press.princeton.edu/books/paperback/9780691139180/data-analysis-for-complex-systems">
    <title>Data Analysis for Complex Systems | Princeton University Press</title>
    <dc:date>2020-07-07T16:12:28+00:00</dc:date>
    <link>https://press.princeton.edu/books/paperback/9780691139180/data-analysis-for-complex-systems</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The analysis of complex systems—from financial markets and voting patterns to ecosystems and food webs—can be daunting for newcomers to the subject, in part because existing methods often require expertise across multiple disciplines. This book shows how a single technique—the partition decoupling method—can serve as a useful first step for modeling and analyzing complex systems data. Accessible to a broad range of backgrounds and widely applicable to complex systems represented as high-dimensional or network data, this powerful methodology draws on core concepts in network modeling and analysis, cluster analysis, and a range of techniques for dimension reduction. The book explains these and other essential concepts and provides several real-world examples to illustrate how a data-driven approach can illuminate complex systems."

--- The idea of writing a stats-for-complexity book emphasizing _linear algebra_ strikes me as wonderfully perverse, but Dan's great so I suspend judgment until I can lay hands on a copy.

--- ETA: Of course part of my reaction may be mere cattiness that they actually have a finished book on this subject while I have been not finishing mine for more than a decade.]]></description>
<dc:subject>to:NB books:noted complexity statistics linear_algebra network_data_analysis rockmore.dan books:suggest_to_library</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3d338648bc2b/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:linear_algebra"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:rockmore.dan"/>
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<item rdf:about="https://press.princeton.edu/books/paperback/9780691192949/islands-of-order">
    <title>Islands of Order | Princeton University Press</title>
    <dc:date>2020-04-14T17:44:59+00:00</dc:date>
    <link>https://press.princeton.edu/books/paperback/9780691192949/islands-of-order</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Over the past two decades, anthropologist J. Stephen Lansing and geneticist Murray Cox have explored dozens of villages on the islands of the Malay Archipelago, combining ethnographic research with research into genetic and linguistic markers to shed light on how these societies change over time. Islands of Order draws on their pioneering fieldwork to show how the science of complexity can be used to better understand unstable dynamics in culture, language, cooperation, and the emergence of hierarchies.
"Complexity science has opened exciting new vistas in physics and biology, but poses challenges for social scientists. What triggers fundamental, discontinuous social change? And what brings stable patterns—islands of order—into existence? Lansing and Cox begin with an incisive and accessible introduction to models of change, from simple random drift to coupled interactions, phase transitions, co-phylogenies, and adaptive landscapes. Then they take readers on a series of journeys to the islands of the Indo-Pacific to demonstrate how social scientists can harness these powerful tools to discover out-of-equilibrium social dynamics. Lansing and Cox address empirical questions surrounding the colonization of the Pacific, the relationship of language to culture, the emergence and disappearance of male and female hierarchies, and more."]]></description>
<dc:subject>books:noted complexity anthropology lansing.j._stephen social_science_methodology books:owned in_NB downloaded</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:35c3541bab02/</dc:identifier>
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<item rdf:about="https://www.jstor.org/stable/j.ctt7rs7b">
    <title>Resolving Ecosystem Complexity (MPB-47) on JSTOR</title>
    <dc:date>2020-01-27T01:24:42+00:00</dc:date>
    <link>https://www.jstor.org/stable/j.ctt7rs7b</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>to:NB books:noted ecology downloaded complexity</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:773a6f516e35/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
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</item>
<item rdf:about="https://www.jstor.org/stable/j.ctt7s3kx">
    <title>Complex Adaptive Systems: An Introduction to Computational Models of Social Life on JSTOR</title>
    <dc:date>2020-01-25T05:58:59+00:00</dc:date>
    <link>https://www.jstor.org/stable/j.ctt7s3kx</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>complexity books:recommended agent-based_models social_science_methodology miller.john page.scott_e. kith_and_kin downloaded</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:58d77000e13e/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:agent-based_models"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:miller.john"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:downloaded"/>
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</item>
<item rdf:about="https://www.jstor.org/stable/j.ctt6wq04g">
    <title>Complexity and the Art of Public Policy: Solving Society's Problems from the Bottom Up on JSTOR</title>
    <dc:date>2020-01-25T05:58:39+00:00</dc:date>
    <link>https://www.jstor.org/stable/j.ctt6wq04g</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Complexity science-made possible by modern analytical and computational advances-is changing the way we think about social systems and social theory. Unfortunately, economists' policy models have not kept up and are stuck in either a market fundamentalist or government control narrative. While these standard narratives are useful in some cases, they are damaging in others, directing thinking away from creative, innovative policy solutions. Complexity and the Art of Public Policy outlines a new, more flexible policy narrative, which envisions society as a complex evolving system that is uncontrollable but can be influenced.
"David Colander and Roland Kupers describe how economists and society became locked into the current policy framework, and lay out fresh alternatives for framing policy questions. Offering original solutions to stubborn problems, the complexity narrative builds on broader philosophical traditions, such as those in the work of John Stuart Mill, to suggest initiatives that the authors call "activist laissez-faire" policies. Colander and Kupers develop innovative bottom-up solutions that, through new institutional structures such as for-benefit corporations, channel individuals' social instincts into solving societal problems, making profits a tool for change rather than a goal. They argue that a central role for government in this complexity framework is to foster an ecostructure within which diverse forms of social entrepreneurship can emerge and blossom."]]></description>
<dc:subject>in_NB downloaded public_policy complexity color_me_skeptical appropriations_of_complexity defenses_of_liberalism</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d2ae2170289e/</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:downloaded"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:public_policy"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:appropriations_of_complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:defenses_of_liberalism"/>
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</item>
<item rdf:about="https://royalsocietypublishing.org/doi/full/10.1098/rsfs.2019.0058">
    <title>Probing complexity: thermodynamics and computational mechanics approaches to origins studies | Interface Focus</title>
    <dc:date>2019-10-23T19:19:38+00:00</dc:date>
    <link>https://royalsocietypublishing.org/doi/full/10.1098/rsfs.2019.0058</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper proposes new avenues for origins research that apply modern concepts from stochastic thermodynamics, information thermodynamics and complexity science. Most approaches to the emergence of life prioritize certain compounds, reaction pathways, environments or phenomena. What they all have in common is the objective of reaching a state that is recognizably alive, usually positing the need for an evolutionary process. As with life itself, this correlates with a growth in the complexity of the system over time. Complexity often takes the form of an intuition or a proxy for a phenomenon that defies complete understanding. However, recent progress in several theoretical fields allows the rigorous computation of complexity. We thus propose that measurement and control of the complexity and information content of origins-relevant systems can provide novel insights that are absent in other approaches. Since we have no guarantee that the earliest forms of life (or alien life) used the same materials and processes as extant life, an appeal to complexity and information processing provides a more objective and agnostic approach to the search for life's beginnings. This paper gives an accessible overview of the three relevant branches of modern thermodynamics. These frameworks are not commonly applied in origins studies, but are ideally suited to the analysis of such non-equilibrium systems. We present proposals for the application of these concepts in both theoretical and experimental origins settings."]]></description>
<dc:subject>to:NB to_read non-equilibrium statistical_mechanics origin_of_life complexity complexity_measures self-organization flashbacks_to_my_dissertation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:cee3d4db6f45/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:non-equilibrium"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_mechanics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:origin_of_life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:self-organization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:flashbacks_to_my_dissertation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.jstor.org/stable/j.ctt7pfdp">
    <title>Diversity and Complexity on JSTOR</title>
    <dc:date>2019-08-22T04:52:10+00:00</dc:date>
    <link>https://www.jstor.org/stable/j.ctt7pfdp</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>books:recommended complexity diversity page.scott_e. kith_and_kin in_NB downloaded</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1eef85f996ea/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:recommended"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:diversity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:page.scott_e."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:downloaded"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://link.springer.com/article/10.1007%2Fs10539-017-9569-z">
    <title>Complexity revisited | SpringerLink</title>
    <dc:date>2018-08-24T13:28:11+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007%2Fs10539-017-9569-z</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["I look back at my 1996 book Complexity and the Function of Mind in Nature, responding to papers by Pamela Lyon, Fred Keijzer and Argyris Arnellos, and Matt Grove."]]></description>
<dc:subject>evolution_of_cognition complexity godfrey-smith.peter philosophy_of_mind via:rvenkat</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6b4231ac8a8b/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:godfrey-smith.peter"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:rvenkat"/>
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</item>
<item rdf:about="http://www.talyarkoni.org/blog/2018/08/18/if-we-already-understood-the-brain-would-we-even-know-it/">
    <title>If we already understood the brain, would we even know it? – [citation needed]</title>
    <dc:date>2018-08-20T19:55:15+00:00</dc:date>
    <link>http://www.talyarkoni.org/blog/2018/08/18/if-we-already-understood-the-brain-would-we-even-know-it/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["What I’m suggesting is that, when we say things like “we don’t really understand the brain yet”, we’re not really expressing factual statements about the collective sum of neuroscience knowledge currently held by all human beings. What each of us really means is something more like there are questions I personally am able to pose about the brain that seem to make sense in my head, but that I don’t currently know the answer to–and I don’t think I could piece together the answer even if you handed me a library of books containing all of the knowledge we’ve accumulated about the brain."

]]></description>
<dc:subject>have_read complexity emergence explanation neuroscience yarkoni.tal</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:517b1fc1e88a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:emergence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:explanation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neuroscience"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:yarkoni.tal"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://dx.doi.org/10.1063/1.5021130">
    <title>Local causal states and discrete coherent structures (Rupe and Crutchfield, 2018)</title>
    <dc:date>2018-08-10T13:23:15+00:00</dc:date>
    <link>http://dx.doi.org/10.1063/1.5021130</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Coherent structures form spontaneously in nonlinear spatiotemporal systems and are found at all spatial scales in natural phenomena from laboratory hydrodynamic flows and chemical reactions to ocean, atmosphere, and planetary climate dynamics. Phenomenologically, they appear as key components that organize the macroscopic behaviors in such systems. Despite a century of effort, they have eluded rigorous analysis and empirical prediction, with progress being made only recently. As a step in this, we present a formal theory of coherent structures in fully discrete dynamical field theories. It builds on the notion of structure introduced by computational mechanics, generalizing it to a local spatiotemporal setting. The analysis’ main tool employs the local causal states, which are used to uncover a system’s hidden spatiotemporal symmetries and which identify coherent structures as spatially localized deviations from those symmetries. The approach is behavior-driven in the sense that it does not rely on directly analyzing spatiotemporal equations of motion, rather it considers only the spatiotemporal fields a system generates. As such, it offers an unsupervised approach to discover and describe coherent structures. We illustrate the approach by analyzing coherent structures generated by elementary cellular automata, comparing the results with an earlier, dynamic-invariant-set approach that decomposes fields into domains, particles, and particle interactions."

--- *ahem* *cough* https://arxiv.org/abs/nlin/0508001 *ahem*]]></description>
<dc:subject>to:NB have_read pattern_formation complexity prediction stochastic_processes spatio-temporal_statistics cellular_automata crutchfield.james_p. modesty_forbids_further_comment</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6c79c23a2a12/</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_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:pattern_formation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:spatio-temporal_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cellular_automata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:crutchfield.james_p."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modesty_forbids_further_comment"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://link.springer.com/article/10.1023%2FB%3AHIST.0000038267.09413.0d">
    <title>Nicolas Rashevsky's Mathematical Biophysics | SpringerLink</title>
    <dc:date>2018-07-07T16:42:32+00:00</dc:date>
    <link>https://link.springer.com/article/10.1023%2FB%3AHIST.0000038267.09413.0d</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper explores the work of Nicolas Rashevsky, a Russian émigré theoretical physicist who developed a program in “mathematical biophysics” at the University of Chicago during the 1930s. Stressing the complexity of many biological phenomena, Rashevsky argued that the methods of theoretical physics – namely mathematics – were needed to “simplify” complex biological processes such as cell division and nerve conduction. A maverick of sorts, Rashevsky was a conspicuous figure in the biological community during the 1930s and early 1940s: he participated in several Cold Spring Harbor symposia and received several years of funding from the Rockefeller Foundation. However, in contrast to many other physicists who moved into biology, Rashevsky's work was almost entirely theoretical, and he eventually faced resistance to his mathematical methods. Through an examination of the conceptual, institutional, and scientific context of Rashevsky's work, this paper seeks to understand some of the reasons behind this resistance."]]></description>
<dc:subject>to_read history_of_science history_of_physics history_of_biology biology biophysics complexity rashevsky.nicolas via:? in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:48df976039f3/</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:history_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_physics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biophysics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:rashevsky.nicolas"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://link.springer.com/article/10.1007/s11229-015-0726-0">
    <title>Network representation and complex systems | SpringerLink</title>
    <dc:date>2017-12-10T23:20:13+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/s11229-015-0726-0</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this article, network science is discussed from a methodological perspective, and two central theses are defended. The first is that network science exploits the very properties that make a system complex. Rather than using idealization techniques to strip those properties away, as is standard practice in other areas of science, network science brings them to the fore, and uses them to furnish new forms of explanation. The second thesis is that network representations are particularly helpful in explaining the properties of non-decomposable systems. Where part-whole decomposition is not possible, network science provides a much-needed alternative method of compressing information about the behavior of complex systems, and does so without succumbing to problems associated with combinatorial explosion. The article concludes with a comparison between the uses of network representation analyzed in the main discussion, and an entirely distinct use of network representation that has recently been discussed in connection with mechanistic modeling."]]></description>
<dc:subject>philosophy_of_science networks complexity color_me_skeptical in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f7b8d3bd3e47/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.aps.org/pre/abstract/10.1103/PhysRevE.95.042140">
    <title>Phys. Rev. E 95, 042140 (2017) - Thermodynamics of complexity and pattern manipulation</title>
    <dc:date>2017-06-23T17:00:16+00:00</dc:date>
    <link>https://journals.aps.org/pre/abstract/10.1103/PhysRevE.95.042140</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Many organisms capitalize on their ability to predict the environment to maximize available free energy and reinvest this energy to create new complex structures. This functionality relies on the manipulation of patterns—temporally ordered sequences of data. Here, we propose a framework to describe pattern manipulators—devices that convert thermodynamic work to patterns or vice versa—and use them to build a “pattern engine” that facilitates a thermodynamic cycle of pattern creation and consumption. We show that the least heat dissipation is achieved by the provably simplest devices, the ones that exhibit desired operational behavior while maintaining the least internal memory. We derive the ultimate limits of this heat dissipation and show that it is generally nonzero and connected with the pattern's intrinsic crypticity—a complexity theoretic quantity that captures the puzzling difference between the amount of information the pattern's past behavior reveals about its future and the amount one needs to communicate about this past to optimally predict the future."]]></description>
<dc:subject>to:NB to_read complexity complexity_measures prediction thermodynamics maxwells_demon</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:461e0c65660b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:thermodynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:maxwells_demon"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.aeaweb.org/articles?id=10.1257/jel.54.2.534">
    <title>Complexity and Economic Policy: A Paradigm Shift or a Change in Perspective? A Review Essay on David Colander and Roland Kupers's Complexity and the Art of Public Policy</title>
    <dc:date>2016-06-06T22:16:24+00:00</dc:date>
    <link>https://www.aeaweb.org/articles?id=10.1257/jel.54.2.534</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In their recent book, Colander and Kupers (2014) argue that viewing the economy as a complex adaptive system should change the way in which we make economic policy. This would necessitate a paradigm shift. Economics has, over time, tried to produce a coherent model to underpin the dominant laissez-faire liberal approach. But we have never proved, in that model, that left to their own devices, the participants in an economy will self-organize into a satisfactory state. This is an assumption. Complex interactive systems with direct interaction between heterogeneous agents may show no tendency to self-equilibrate and will undergo endogenous crises. Economists should concentrate on the emergence of certain patterns. Colander and Kupers suggest that we may be able to nudge the system into "good" basins of attraction. A more radical view is that there are no fixed basins of attraction; these change with the evolution of the system and it is illusory to believe that we can choose good basins. We may be able to recognize and influence the emergence of certain states of the economy, but we are far from Leon Walras's dream of economics as a science like astrophysics."]]></description>
<dc:subject>books:noted book_reviews economics economic_policy complexity kirman.alan macroeconomics in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d7ec2115508e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:book_reviews"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economic_policy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kirman.alan"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.amazon.com/Methods-Techniques-Complex-Systems-Science-ebook/dp/B01EDLKQZU/ref=sr_1_1?ie=UTF8&amp;qid=1463104639&amp;sr=8-1&amp;keywords=shalizi">
    <title>Methods and Techniques of Complex Systems Science, Cosma Rohilla Shalizi, eBook - Amazon.com</title>
    <dc:date>2016-05-13T02:13:27+00:00</dc:date>
    <link>https://www.amazon.com/Methods-Techniques-Complex-Systems-Science-ebook/dp/B01EDLKQZU/ref=sr_1_1?ie=UTF8&amp;qid=1463104639&amp;sr=8-1&amp;keywords=shalizi</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[I've been plagiarized before, but I've never had someone use my name to charge people $8.99 for something they could read, _with_ the figures, for free (https://arxiv.org/abs/nlin/0307015).  We'll see if Amazon does anything.
]]></description>
<dc:subject>self-centered complexity fraud</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:40a37bb89617/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:self-centered"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:fraud"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.washingtonpost.com/blogs/monkey-cage/wp/2015/08/19/goodbye-to-the-genius-who-changed-the-way-we-think-and-you-didnt-know-even-know-it/">
    <title>Goodbye to the genius who changed the way we think (and you didn’t even know it) - The Washington Post</title>
    <dc:date>2015-08-22T15:36:15+00:00</dc:date>
    <link>http://www.washingtonpost.com/blogs/monkey-cage/wp/2015/08/19/goodbye-to-the-genius-who-changed-the-way-we-think-and-you-didnt-know-even-know-it/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Scott Page's memorial notice for the late, lamented John Holland.  (I am morally certain Scott did not write the headline.)
I was never close to Holland, but he was an inspiration: a figure from the mad old days when things were still so new and formless that no one could tell the difference between genius and inanity, who'd come out of them with genuinely great contributions and an oddly innocent indifference to disciplinary boundaries.  I remember how reading and working my way through his _Adaptation in Natural and Artificial Systems_ seemed to open up new worlds...]]></description>
<dc:subject>obituaries cellular_automata adaptive_behavior genetic_algorithms page.scott cognitive_science agent-based_models complexity to:blog holland.john_h.</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f7745e4b5d13/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:obituaries"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cellular_automata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:adaptive_behavior"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:genetic_algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:page.scott"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:agent-based_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:blog"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:holland.john_h."/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://ukcatalogue.oup.com/product/academic/society/anthropology/9780199604357.do">
    <title>Sociolinguistic Typology: Social Determinants of Linguistic Complexity</title>
    <dc:date>2015-01-30T14:25:09+00:00</dc:date>
    <link>http://ukcatalogue.oup.com/product/academic/society/anthropology/9780199604357.do</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Peter Trudgill looks at why human societies at different times and places produce different kinds of language. He considers how far social factors influence language structure and compares languages and dialects spoken across the globe, from Vietnam to Nigeria, Polynesia to Scandinavia, and from Canada to Amazonia.
"Modesty prevents Pennsylvanian Dutch Mennonites using the verb wotte ('want'); stratified society lies behind complicated Japanese honorifics; and a mountainous homeland suggests why speakers of Tibetan-Burmese Lahu have words for up there and down there. But culture and environment don't explain why Amazonian Jarawara needs three past tenses, nor why Nigerian Igbo can make do with eight adjectives, nor why most languages spoken in high altitudes do not exhibit an array of spatial demonstratives. Nor do they account for some languages changing faster than others or why some get more complex while others get simpler. The author looks at these and many other puzzles, exploring the social, linguistic, and other factors that might explain them and in the context of a huge range of languages and societies."]]></description>
<dc:subject>books:noted cultural_evolution linguistics complexity in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:88a151e69d63/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cultural_evolution"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:linguistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://faculty.salisbury.edu/~xswang/Research/Papers/SERelated/no-silver-bullet.pdf">
    <title>No Silver Bullet (Brooks, 1986)</title>
    <dc:date>2014-10-31T18:25:57+00:00</dc:date>
    <link>http://faculty.salisbury.edu/~xswang/Research/Papers/SERelated/no-silver-bullet.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Interesting to think about what this got right, and what (if anything) it didn't.]]></description>
<dc:subject>have_read via:? programming software_engineering complexity to:blog</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3bdef30faf5d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:software_engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:blog"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://sss.sagepub.com/content/44/4/555.abstract?etoc">
    <title>Multivariate statistics and the enactment of metabolic complexity</title>
    <dc:date>2014-07-29T14:59:49+00:00</dc:date>
    <link>http://sss.sagepub.com/content/44/4/555.abstract?etoc</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This ethnographic study, based on fieldwork at the Computational and Systems Medicine laboratory at Imperial College London, shows how researchers in the field of metabolomics – the post-genomic study of the molecules and processes that make up metabolism – enact and coproduce complex views of biology with multivariate statistics. From this data-driven science, metabolism emerges as a multiple, informational and statistical object, which is both produced by and also necessitates particular forms of data production and analysis. Multivariate statistics emerge as ‘natural’ and ‘correct’ ways of engaging with a metabolism that is made up of many variables. In this sense, multivariate statistics allow researchers to engage with and conceptualize metabolism, and also disease and processes of life, as complex entities. Consequently, this article builds on studies of scientific practice and visualization to examine data as material objects rather than black-boxed representations. Data practices are not merely the technological components of experimentation, but are simultaneously technologies and methods and are intertwined with ways of seeing and enacting the biological world. Ultimately, this article questions the increasing invocation and role of complexity within biology, suggesting that discourses of complexity are often imbued with reductionist and determinist ways of thinking about biology, as scientists engage with complexity in calculated and controlled, but also limited, ways."]]></description>
<dc:subject>to:NB to_read ethnography science_as_a_social_process biochemical_networks biology statistics complexity data_analysis</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:944c59a7a680/</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:ethnography"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:science_as_a_social_process"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_analysis"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://doi.org/10.2307/j.ctt6wq04g">
    <title>Colander, D. and Kupers, R.: Complexity and the Art of Public Policy: Solving Society's Problems from the Bottom Up. (eBook and Hardcover)</title>
    <dc:date>2014-06-10T18:05:24+00:00</dc:date>
    <link>https://doi.org/10.2307/j.ctt6wq04g</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["...Complexity and the Art of Public Policy outlines a new, more flexible policy narrative, which envisions society as a complex evolving system that is uncontrollable but can be influenced.
"David Colander and Roland Kupers describe how economists and society became locked into the current policy framework, and lay out fresh alternatives for framing policy questions. Offering original solutions to stubborn problems, the complexity narrative builds on broader philosophical traditions, such as those in the work of John Stuart Mill, to suggest initiatives that the authors call "activist laissez-faire" policies. Colander and Kupers develop innovative bottom-up solutions that, through new institutional structures such as for-benefit corporations, channel individuals' social instincts into solving societal problems, making profits a tool for change rather than a goal. They argue that a central role for government in this complexity framework is to foster an ecostructure within which diverse forms of social entrepreneurship can emerge and blossom."]]></description>
<dc:subject>books:noted complexity public_policy color_me_skeptical downloaded in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d0b901bc130a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:public_policy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:downloaded"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.jstor.org/action/showArticleInfo?doi=10.1086%2F673937">
    <title>When to Expect Violations of Causal Faithfulness and Why It Matters</title>
    <dc:date>2014-02-10T22:18:11+00:00</dc:date>
    <link>http://www.jstor.org/action/showArticleInfo?doi=10.1086%2F673937</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["I present three reasons why philosophers of science should be more concerned about violations of causal faithfulness (CF). In complex evolved systems, mechanisms for maintaining equilibrium states are highly likely to violate CF. Even when such systems do not precisely violate CF, they may nevertheless generate precisely the same problems for inferring causal structure from probabilistic relationships in data as do genuine CF violations. Thus, potential CF violations are particularly germane to experimental science when we rely on probabilistic information to uncover causal structures since we cannot then use those structures to predict the right experiments to ‘catch out’ hidden causal relationships."]]></description>
<dc:subject>to:NB causal_inference causality complexity</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6b72e8ea0514/</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:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1308.0317">
    <title>[1308.0317] Complexity in genetic networks: topology vs. strength of interactions</title>
    <dc:date>2013-08-03T02:37:50+00:00</dc:date>
    <link>http://arxiv.org/abs/1308.0317</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Genetic regulatory networks are defined by their topology and by a multitude of continuously adjustable parameters. Here we present a class of simple models within which the relative importance of topology vs. interaction strengths becomes a well-posed problem. We find that complexity - the ability of the network to adopt multiple stable states - is dominated by the adjustable parameters. We comment on the implications for real networks and their evolution."]]></description>
<dc:subject>to:NB gene_regulation networks complexity bialek.william</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:91a4b93fad42/</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:gene_regulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bialek.william"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1307.5364">
    <title>[1307.5364] No complexity-stability relationship in natural communities</title>
    <dc:date>2013-07-23T12:53:59+00:00</dc:date>
    <link>http://arxiv.org/abs/1307.5364</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We performed a stability analysis of 119 quantitative food webs which were compiled using a standard methodology to build Ecopath mass-balance models. Our analysis reveals that classic descriptors of complexity do not affect stability in natural food webs. Food web structure, which is non-random in real communities, reflects another form of complexity that we found influences dramatically the stability of real communities. We conclude that the occurrence of complex communities in nature is possible owing to their trophic structure."]]></description>
<dc:subject>ecology complexity stability_and_complexity_in_model_ecosystems in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d597392445d5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ecology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stability_and_complexity_in_model_ecosystems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://store.doverpublications.com/048649098x.html">
    <title>The Recursive Universe: Cosmic Complexity and the Limits of Scientific Knowledge</title>
    <dc:date>2013-07-16T17:29:42+00:00</dc:date>
    <link>http://store.doverpublications.com/048649098x.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[I am _very_ happy that this book has come back into print.]]></description>
<dc:subject>books:recommended complexity maxwells_demon entropy information_theory philosophy_of_science popular_science popular_philosophy cellular_automata self-organization statistical_mechanics to_teach:complexity-and-inference</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e04b0d23f0f6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:recommended"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:maxwells_demon"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:entropy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:popular_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:popular_philosophy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cellular_automata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:self-organization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_mechanics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:complexity-and-inference"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.springer.com/physics/complexity/book/978-1-4614-7217-9?cm_mmc=NBA-_-Jul-13_WEST_13201678-_-product-_-978-1-4614-7217-9&amp;otherVersion=978-1-4614-7218-6">
    <title>Predicting the Future - Completing Models of Observed Complex Systems</title>
    <dc:date>2013-07-11T20:11:13+00:00</dc:date>
    <link>http://www.springer.com/physics/complexity/book/978-1-4614-7217-9?cm_mmc=NBA-_-Jul-13_WEST_13201678-_-product-_-978-1-4614-7217-9&amp;otherVersion=978-1-4614-7218-6</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Predicting the Future: Completing Models of Observed Complex Systems provides a general framework for the discussion of model building and validation across a broad spectrum of disciplines. This is accomplished through the development of an exact path integral for use in transferring information from observations to a model of the observed system. Through many illustrative examples drawn from models in neuroscience, fluid dynamics, geosciences, and nonlinear electrical circuits, the concepts are exemplified in detail. Practical numerical methods for approximate evaluations of the path integral are explored, and their use in designing experiments and determining a model's consistency with observations is investigated.
"Using highly instructive examples, the problems of data assimilation and the means to treat them are clearly illustrated. This book will be useful for students and practitioners of physics, neuroscience, regulatory networks, meteorology and climate science, network dynamics, fluid dynamics, and other systematic investigations of complex systems."]]></description>
<dc:subject>to:NB books:noted statistical_inference_for_stochastic_processes complexity dynamical_systems filtering prediction abarbanel.henry</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:80a6a8c60d6c/</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:statistical_inference_for_stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:filtering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:abarbanel.henry"/>
</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="https://doi.org/10.1515/9781400845637">
    <title>Stein, D.L. and Newman, C.: Spin Glasses and Complexity.</title>
    <dc:date>2013-01-23T17:22:17+00:00</dc:date>
    <link>https://doi.org/10.1515/9781400845637</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Spin glasses are disordered magnetic systems that have led to the development of mathematical tools with an array of real-world applications, from airline scheduling to neural networks. Spin Glasses and Complexity offers the most concise, engaging, and accessible introduction to the subject, fully explaining what spin glasses are, why they are important, and how they are opening up new ways of thinking about complexity.
"This one-of-a-kind guide to spin glasses begins by explaining the fundamentals of order and symmetry in condensed matter physics and how spin glasses fit into--and modify--this framework. It then explores how spin-glass concepts and ideas have found applications in areas as diverse as computational complexity, biological and artificial neural networks, protein folding, immune response maturation, combinatorial optimization, and social network modeling.
"Providing an essential overview of the history, science, and growing significance of this exciting field, Spin Glasses and Complexity also features a forward-looking discussion of what spin glasses may teach us in the future about complex systems. This is a must-have book for students and practitioners in the natural and social sciences, with new material even for the experts."]]></description>
<dc:subject>books:recommended have_read statistical_mechanics complexity in_NB of_course_its_really_a_spin_glass downloaded</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:33250ab5433b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:recommended"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_mechanics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:of_course_its_really_a_spin_glass"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:downloaded"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1211.2878">
    <title>[1211.2878] Defensive complexity and the phylogenetic conservation of immune control</title>
    <dc:date>2012-12-31T01:35:04+00:00</dc:date>
    <link>http://arxiv.org/abs/1211.2878</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["One strategy for winning a coevolutionary struggle is to evolve rapidly. Most of the literature on host-pathogen coevolution focuses on this phenomenon, and looks for consequent evidence of coevolutionary arms races. An alternative strategy, less often considered in the literature, is to deter rapid evolutionary change by the opponent. To study how this can be done, we construct an evolutionary game between a controller that must process information, and an adversary that can tamper with this information processing. In this game, a species can foil its antagonist by processing information in a way that is hard for the antagonist to manipulate. We show that the structure of the information processing system induces a fitness landscape on which the adversary population evolves. Complex processing logic can carve long, deep fitness valleys that slow adaptive evolution in the adversary population. We suggest that this type of defensive complexity on the part of the vertebrate adaptive immune system may be an important element of coevolutionary dynamics between pathogens and their vertebrate hosts. Furthermore, we cite evidence that the immune control logic is phylogenetically conserved in mammalian lineages. Thus our model of defensive complexity suggests a new hypothesis for the lower rates of evolution for immune control logic compared to other immune structures."]]></description>
<dc:subject>complexity computational_complexity evolutionary_biology immunology kith_and_kin bergstrom.carl_t. in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:449e356ccd7a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:computational_complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evolutionary_biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:immunology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bergstrom.carl_t."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.pnas.org/content/109/35/13908">
    <title>Classic Period collapse of the Central Maya Lowlands: Insights about human–environment relationships for sustainability</title>
    <dc:date>2012-12-22T17:19:32+00:00</dc:date>
    <link>http://www.pnas.org/content/109/35/13908</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The ninth century collapse and abandonment of the Central Maya Lowlands in the Yucatán peninsular region were the result of complex human–environment interactions. Large-scale Maya landscape alterations and demands placed on resources and ecosystem services generated high-stress environmental conditions that were amplified by increasing climatic aridity. Coincident with this stress, the flow of commerce shifted from land transit across the peninsula to sea-borne transit around it. These changing socioeconomic and environmental conditions generated increasing societal conflicts, diminished control by the Maya elite, and led to decisions to move elsewhere in the peninsular region rather than incur the high costs of maintaining the human–environment systems in place. After abandonment, the environment of the Central Maya Lowlands largely recovered, although altered from its state before Maya occupation; the population never recovered. This history and the spatial and temporal variability in the pattern of collapse and abandonment throughout the Maya lowlands support the case for different conditions, opportunities, and constraints in the prevailing human–environment systems and the decisions to confront them. The Maya case lends insights for the use of paleo- and historical analogs to inform contemporary global environmental change and sustainability."]]></description>
<dc:subject>have_read maya_civilization archaeology complexity sabloff.jeremy ecology environmental_management climate_change in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:7ae419e60f90/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:maya_civilization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:archaeology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sabloff.jeremy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ecology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:environmental_management"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:climate_change"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://link.springer.com/article/10.1007%2Fs10955-012-0647-y">
    <title>A Not-So-Fundamental Limitation on Studying Complex Systems with Statistics: Comment on Rabin (2011) - Springer</title>
    <dc:date>2012-12-21T00:34:16+00:00</dc:date>
    <link>http://link.springer.com/article/10.1007%2Fs10955-012-0647-y</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Although living organisms are affected by many interrelated and unidentified variables, this complexity does not automatically impose a fundamental limitation on statistical inference. Nor need one invoke such complexity as an explanation of the “Truth Wears Off” or “decline” effect; similar “decline” effects occur with far simpler systems studied in physics. Selective reporting and publication bias, and scientists’ biases in favor of reporting eye-catching results (in general) or conforming to others’ results (in physics) better explain this feature of the “Truth Wears Off” effect than Rabin’s suggested limitation on statistical inference."

--- Rabin's paper was indeed so bizarre and fallacious that I suspected it of being a Sokaling of J. Stat. Phys....]]></description>
<dc:subject>evisceration complexity statistics probability to:blog</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:87f833b9fa4c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evisceration"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:blog"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1207.2743">
    <title>[1207.2743] The evolutionary origins of modularity</title>
    <dc:date>2012-07-12T01:52:54+00:00</dc:date>
    <link>http://arxiv.org/abs/1207.2743</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A central biological question is how natural organisms are so evolvable (capable of quickly adapting to new environments). A key driver of evolvability is the widespread modularity of biological networks--their organization as functional, sparsely connected subunits--but there is no consensus regarding why modularity itself evolved. While most hypotheses assume indirect selection for evolvability, here we demonstrate that the ubiquitous, direct selection pressure to reduce the cost of connections between network nodes causes the emergence of modular networks. Experiments with selection pressures to maximize network performance and minimize connection costs yield networks that are significantly more modular and more evolvable than control experiments that only select for performance. These results will catalyze research in numerous disciplines, including neuroscience, genetics and harnessing evolution for engineering purposes."

- Dude, modularity from selection for low connection costs goes back to Ramon y Cajal!]]></description>
<dc:subject>to:NB networks complexity evolutionary_biology lipson.hod color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d56310b31782/</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:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evolutionary_biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:lipson.hod"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/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://arxiv.org/abs/1206.2216">
    <title>[1206.2216] Complex Systems Science: Dreams of Universality, Reality of Interdisciplinarity</title>
    <dc:date>2012-06-23T15:16:25+00:00</dc:date>
    <link>http://arxiv.org/abs/1206.2216</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Using a large database (~ 215 000 records) of relevant articles, we empirically study the "complex systems" field and its claims to find universal principles applying to systems in general. The study of references shared by the papers allows us to obtain a global point of view on the structure of this highly interdisciplinary field. We show that its overall coherence does not arise from a universal theory but instead from computational techniques and fruitful adaptations of the idea of self-organization to specific systems. We also find that communication between different disciplines goes through specific "trading zones", ie sub-communities that create an interface around specific tools (a DNA microchip) or concepts (a network)."]]></description>
<dc:subject>sociology_of_science complexity bibliometry kith_and_kin rouquier.jean-baptiste social_life_of_the_mind in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:12ffc13e309b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sociology_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bibliometry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:rouquier.jean-baptiste"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_life_of_the_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.andrew.cmu.edu/org/cfe/ockam-foundations.html">
    <title>Ockham's Razor: Foundations - Carnegie Mellon Center for Formal Epistemology</title>
    <dc:date>2012-04-17T12:14:22+00:00</dc:date>
    <link>http://www.andrew.cmu.edu/org/cfe/ockam-foundations.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Despite my presence on the program, this should actually be really good.

"Scientific theory choice is guided by judgments of simplicity, a bias frequently referred to as "Ockham's Razor". But what is simplicity and how, if at all, does it help science find the truth?  Should we view simple theories as means for obtaining accurate predictions, as classical statisticians recommend?  Or should we believe the theories themselves, as Bayesian methods seem to justify?  The aim of this workshop is to re-examine the foundations of Ockham's razor, with a firm focus on the connections, if any, between simplicity and truth. "

--- ETA: Conference reports, of a kind:
http://bactra.org/weblog/921.html
http://bactra.org/weblog/922.html
http://bactra.org/weblog/923.html]]></description>
<dc:subject>self-promotion occams_razor philosophy_of_science epistemology kelly.kevin_t. kith_and_kin mayo.deborah vapnik.v.n. sober.elliott leeb.hannes wasserman.larry model_selection statistics complexity machine_learning learning_theory grunwald.peter</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5d233b1231a4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:self-promotion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:occams_razor"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epistemology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kelly.kevin_t."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mayo.deborah"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:vapnik.v.n."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sober.elliott"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:leeb.hannes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:wasserman.larry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:model_selection"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:machine_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:grunwald.peter"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://prl.aps.org/abstract/PRL/v108/i12/e128702">
    <title>Phys. Rev. Lett. 108, 128702 (2012): Emergent Criticality through Adaptive Information Processing in Boolean Networks</title>
    <dc:date>2012-03-30T20:48:21+00:00</dc:date>
    <link>http://prl.aps.org/abstract/PRL/v108/i12/e128702</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[We study information processing in populations of Boolean networks with evolving connectivity and systematically explore the interplay between the learning capability, robustness, the network topology, and the task complexity. We solve a long-standing open question and find computationally that, for large system sizes N, adaptive information processing drives the networks to a critical connectivity Kc=2. For finite size networks, the connectivity approaches the critical value with a power law of the system size N. We show that network learning and generalization are optimized near criticality, given that the task complexity and the amount of information provided surpass threshold values. Both random and evolved networks exhibit maximal topological diversity near Kc. We hypothesize that this diversity supports efficient exploration and robustness of solutions. Also reflected in our observation is that the variance of the fitness values is maximal in critical network populations. Finally, we discuss implications of our results for determining the optimal topology of adaptive dynamical networks that solve computational tasks.]]></description>
<dc:subject>to:NB complexity</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:782acacf1c9b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.smbc-comics.com/index.php?db=comics&amp;id=2556">
    <title>Saturday Morning Breakfast Cereal: The Life Cycle of Physicists</title>
    <dc:date>2012-03-21T14:34:45+00:00</dc:date>
    <link>http://www.smbc-comics.com/index.php?db=comics&amp;id=2556</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[(Via http://jonfwilkins.blogspot.com/2012/03/life-cycle-of-physicist.html)]]></description>
<dc:subject>cartoons funny:geeky funny:malicious funny:because_its_true physics complexity to:blog via:jon_wilkins funny:sad i_resemble_that_remark</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:706594a004b3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cartoons"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:funny:geeky"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:funny:malicious"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:funny:because_its_true"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:physics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:blog"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:jon_wilkins"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:funny:sad"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:i_resemble_that_remark"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www2.warwick.ac.uk/fac/cross_fac/comcom/events/powerlawsandrareevents2012/">
    <title>Aggregation, Inference and Rare Events in the Natural and Socio-Economic Sciences</title>
    <dc:date>2012-03-15T15:47:17+00:00</dc:date>
    <link>http://www2.warwick.ac.uk/fac/cross_fac/comcom/events/powerlawsandrareevents2012/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>complexity self-promotion</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:00636ef9733a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:self-promotion"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www2.warwick.ac.uk/fac/cross_fac/comcom/events/summerschool2012/">
    <title>Summer School 2012: Complexity and Inference</title>
    <dc:date>2012-03-15T15:46:56+00:00</dc:date>
    <link>http://www2.warwick.ac.uk/fac/cross_fac/comcom/events/summerschool2012/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>complexity self-promotion</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:05e43e51ea19/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:self-promotion"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.tnr.com/blog/timothy-noah/100039/occupy-davos">
    <title>Tim Noah: The Igloos Of Occupy Davos | The New Republic</title>
    <dc:date>2012-01-26T20:14:03+00:00</dc:date>
    <link>http://www.tnr.com/blog/timothy-noah/100039/occupy-davos</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Incidentally, the competition is fierce, among panels that really are being held this year, for the one with the most risibly Davos-like name. "Global Risks 2012: The Safety of Our Safeguards" is a strong contender, as is "The New Context For Leadership." But I have to go with "Managing Complexity With The Santa Fe Institute." It combines a deeply serious tone with a total absence of discernible content; unsubtle institutional branding; and the gravest possible risk that attendees will perish from boredom. This is what you get when the best minds and/or fattest wallets assemble to congratulate one another for being on top."]]></description>
<dc:subject>complexity santa_fe_institute ouch via:probably_better_not_to_say to:blog</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c05fe224948c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:santa_fe_institute"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ouch"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:probably_better_not_to_say"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:blog"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1112.1440">
    <title>[1112.1440] Complex Systems: A Survey</title>
    <dc:date>2011-12-15T16:17:50+00:00</dc:date>
    <link>http://arxiv.org/abs/1112.1440</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A complex system is a system composed of many interacting parts, often called agents, which displays collective behavior that does not follow trivially from the behaviors of the individual parts. Examples include condensed matter systems, ecosystems, stock markets and economies, biological evolution, and indeed the whole of human society. Substantial progress has been made in the quantitative understanding of complex systems, particularly since the 1980s, using a combination of basic theory, much of it derived from physics, and computer simulation. The subject is a broad one, drawing on techniques and ideas from a wide range of areas. Here I give a survey of the main themes and methods of complex systems science and an annotated bibliography of resources, ranging from classic papers to recent books and reviews."]]></description>
<dc:subject>have_read complexity kith_and_kin newman.mark in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f63d941fbc7d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:newman.mark"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504687&amp;org=DMS&amp;from=home">
    <title>nsf.gov - DMS - Funding - Computational and Data-Enabled Science and Engineering in Mathematical and Statistical Sciences - US National Science Foundation (NSF)</title>
    <dc:date>2011-12-08T00:59:33+00:00</dc:date>
    <link>http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504687&amp;org=DMS&amp;from=home</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Statistical & data mining methods for complex systems and for networks are particularly mentioned.]]></description>
<dc:subject>grants statistics complexity network_data_analysis</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a514205392c7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:grants"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://exploringcomplexity.blogspot.com/2011/11/we-need-to-talk-about-scaling.html">
    <title>Exploring Complexity: We Need to Talk About Scaling</title>
    <dc:date>2011-12-08T00:46:22+00:00</dc:date>
    <link>http://exploringcomplexity.blogspot.com/2011/11/we-need-to-talk-about-scaling.html</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>scaling complexity heavy_tails mitchell.melanie blogged</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c0bea3b9944d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:scaling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:heavy_tails"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mitchell.melanie"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:blogged"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.springerlink.com/content/l617855012511078/">
    <title>Fundamental Limitation on Applicability of Statistical Methods to Study of Living Organisms and Other Complex Systems</title>
    <dc:date>2011-07-28T22:38:28+00:00</dc:date>
    <link>http://www.springerlink.com/content/l617855012511078/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Is this a joke?

ETA: See http://link.springer.com/article/10.1007%2Fs10955-012-0647-y for the refutation.]]></description>
<dc:subject>statistics have_read utter_stupidity bad_science complexity my_initial_skeptical_coloration_became_on_examination_a_permanent_stain</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0307ec4af11d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:utter_stupidity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bad_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:my_initial_skeptical_coloration_became_on_examination_a_permanent_stain"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.elsevier.com/wps/find/bookdescription.cws_home/720424/description#description">
    <title>Philosophy of Complex Systems - Elsevier</title>
    <dc:date>2011-07-18T17:22:50+00:00</dc:date>
    <link>http://www.elsevier.com/wps/find/bookdescription.cws_home/720424/description#description</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>books:noted philosophy_of_science complexity color_me_skeptical</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3541fc06ee2b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.crcpress.com/product/isbn/9781420060676">
    <title>CRC Press Online - Book: Introduction to the Modeling of Complex Systems</title>
    <dc:date>2011-05-02T17:07:08+00:00</dc:date>
    <link>http://www.crcpress.com/product/isbn/9781420060676</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[No further information shows up on the web, so I have no idea if this will be any good.
]]></description>
<dc:subject>books:noted complexity modeling</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:7cc16e21b5e8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://pre.aps.org/abstract/PRE/v83/i3/e036706">
    <title>Phys. Rev. E 83, 036706 (2011): Adaptive simplification of complex multiscale systems</title>
    <dc:date>2011-04-01T17:34:24+00:00</dc:date>
    <link>http://pre.aps.org/abstract/PRE/v83/i3/e036706</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>to:NB macro_from_micro complexity dynamical_systems</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2b6ab7971aab/</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:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://press.princeton.edu/titles/9483.html">
    <title>Solé, R.V.: Phase Transitions.</title>
    <dc:date>2011-03-11T17:00:53+00:00</dc:date>
    <link>http://press.princeton.edu/titles/9483.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Possibly suitable for adoption in the undergrad complex systems class?
]]></description>
<dc:subject>books:noted kith_and_kin phase_transitions statistical_mechanics complexity to_teach:complexity-and-inference to:NB sole.ricard</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d36bff58da95/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:phase_transitions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_mechanics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:complexity-and-inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sole.ricard"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://philsci-archive.pitt.edu/8496/">
    <title>What is a complex system? - PhilSci-Archive</title>
    <dc:date>2011-03-03T01:52:15+00:00</dc:date>
    <link>http://philsci-archive.pitt.edu/8496/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Unless this has changed drastically from the version Karoline showed me in Bristol in October.
]]></description>
<dc:subject>complexity_measures complexity kith_and_kin have_read wiesner.karoline</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:aa2d09919e23/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:wiesner.karoline"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://philsci-archive.pitt.edu/8442/">
    <title>Mechanisms in Dynamically Complex Systems - PhilSci-Archive</title>
    <dc:date>2011-01-05T20:16:53+00:00</dc:date>
    <link>http://philsci-archive.pitt.edu/8442/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In recent debates mechanisms are often discussed in the context of ‘complex systems’ which are understood as having a complicated compositional structure. ... another, radically different kind of complex system ... that many scientists regard as the only genuine kind of complex system ... highly non-trivial dynamical patterns on the basis of structurally simple arrangements of large numbers of non-linearly interacting constituents. The characteristic dynamical patterns ... arise from the interaction of the system’s parts largely irrespective of many properties of these parts. Dynamically complex systems can exhibit surprising statistical characteristics ... calls for an explanation in terms of underlying generating mechanisms. ... dynamically complex systems are not sufficiently covered by the available conceptions of mechanisms..."
]]></description>
<dc:subject>philosophy_of_science explanation_by_mechanisms complexity in_NB</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:08ad75f1ccff/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:explanation_by_mechanisms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.continuumbooks.com/books/detail.aspx?BookId=158700&amp;SubjectId=1020&amp;Subject2Id=1719">
    <title>Philosophy and Simulation: The Emergence of Synthetic Reason - Continuum</title>
    <dc:date>2011-01-02T02:21:48+00:00</dc:date>
    <link>http://www.continuumbooks.com/books/detail.aspx?BookId=158700&amp;SubjectId=1020&amp;Subject2Id=1719</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[I have liked DeLanda's recent books, though I still find _War in the Age of Intelligent Machines_ bad, and don't get what he sees in Deleuze.  I look forward to this one with interest.
]]></description>
<dc:subject>books:noted simulation complexity cellular_automata agent-based_models philosophy_of_science delanda.manuel post-structuralism books:owned</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:160d3bb6b2fb/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:simulation"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cellular_automata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:agent-based_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:delanda.manuel"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:post-structuralism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:owned"/>
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</item>
<item rdf:about="http://arxiv.org/abs/1012.3896">
    <title>[1012.3896] Searching for simplicity: Approaches to the analysis of neurons and behavior</title>
    <dc:date>2010-12-21T16:38:23+00:00</dc:date>
    <link>http://arxiv.org/abs/1012.3896</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Hmmm...
]]></description>
<dc:subject>complexity neuroscience bialek.william</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c228713073bc/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neuroscience"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bialek.william"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.jsmf.org/apply/apply.php">
    <title>James S. McDonnell Foundation: Apply for a Grant</title>
    <dc:date>2010-12-02T20:12:21+00:00</dc:date>
    <link>http://www.jsmf.org/apply/apply.php</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Deadline, 15 March.
]]></description>
<dc:subject>grants complexity</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b72055eace75/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:grants"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
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</item>
<item rdf:about="http://www.rss.org.uk/main.asp?group=&amp;page=1332&amp;event=1186&amp;month=&amp;year=&amp;date=">
    <title>The Royal Statistical Society - Future Events: Complexity and statistics: tipping points and crashes</title>
    <dc:date>2010-09-10T17:56:32+00:00</dc:date>
    <link>http://www.rss.org.uk/main.asp?group=&amp;page=1332&amp;event=1186&amp;month=&amp;year=&amp;date=</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>gigs conferences complexity statistics heavy_tails self-promotion</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e7d5326c9d67/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:conferences"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:heavy_tails"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:self-promotion"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://philsci-archive.pitt.edu/archive/00005304/">
    <title>PhilSci Archive - Inverse Ontomimetic Simulation: a window on complex systems</title>
    <dc:date>2010-04-10T14:23:00+00:00</dc:date>
    <link>http://philsci-archive.pitt.edu/archive/00005304/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>simulation complexity philosophy_of_science in_NB</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:05b369dbf83e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.wspc.com.sg/mathematics/7107.html">
    <title>APPLICATIONS OF AUTOMATA THEORY AND ALGEBRA</title>
    <dc:date>2010-02-10T03:12:42+00:00</dc:date>
    <link>https://www.wspc.com.sg/mathematics/7107.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[I actually read chunks of this while in graduate school.  It was... strange.
]]></description>
<dc:subject>complexity automata_theory borderline_psychoceramica books:noted</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:baa22a937313/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:automata_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:borderline_psychoceramica"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
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</item>
<item rdf:about="http://www.press.uchicago.edu/presssite/metadata.epl?mode=synopsis&amp;bookkey=8205034">
    <title>Sandra Mitchell: Unsimple Truths: Science, Complexity, and Policy</title>
    <dc:date>2010-02-02T19:08:56+00:00</dc:date>
    <link>http://www.press.uchicago.edu/presssite/metadata.epl?mode=synopsis&amp;bookkey=8205034</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The world is complex, but acknowledging its complexity requires an appreciation for the many roles context plays in shaping natural phenomena....   deference to reductive explanations founded on simple universal laws, linear causal models, and predict-and-act strategies fails to accommodate the kinds of knowledge that many contemporary sciences [provide] about the world. ... new understanding that represents the rich, variegated, interdependent fabric of many levels and kinds of explanation that ... ground effective prediction and action.  ... draws from diverse fields including psychiatry, social insect biology, and studies of climate change to defend ... a theory of scientific practices that makes sense of how [sciences] represent multi-level, multi-component, dynamic structures ... must revise how we conceptualize the world, how we investigate the world, and how we act in the world. ... the very idea of what should count as legitimate science itself should change."
]]></description>
<dc:subject>books:noted complexity public_policy decision-making philosophy_of_science color_me_skeptical</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:96b22473e333/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:public_policy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.aeaweb.org/annual_mtg_papers/2008/2008_545.pdf">
    <title>Beyond DSGE Models: Towards an Empirically-Based Macroeconomics</title>
    <dc:date>2009-08-19T01:34:30+00:00</dc:date>
    <link>http://www.aeaweb.org/annual_mtg_papers/2008/2008_545.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[My reaction to the first half is "preach it, brothers and sisters!"  Perhaps inevitably, the constructive proposals of the 2nd half are less compelling.
]]></description>
<dc:subject>economics macroeconomics macro_from_micro agent-based_models complexity econometrics economic_policy social_engineering via:? have_read re:your_favorite_dsge_sucks</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2ed1cd45d832/</dc:identifier>
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	<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:agent-based_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economic_policy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.fbo.gov/index?&amp;s=opportunity&amp;mode=form&amp;id=4539dc1cc3aa10b46cbf655811648137&amp;tab=core&amp;tabmode=list">
    <title>Physical intelligence (PI) - Federal Business Opportunities: Opportunities</title>
    <dc:date>2009-05-22T16:31:39+00:00</dc:date>
    <link>https://www.fbo.gov/index?&amp;s=opportunity&amp;mode=form&amp;id=4539dc1cc3aa10b46cbf655811648137&amp;tab=core&amp;tabmode=list</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>grants darpa complexity self-organization artificial_intelligence mad_science</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0131e0bef097/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:self-organization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:artificial_intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mad_science"/>
</rdf:Bag></taxo:topics>
</item>
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