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    <title>Pinboard (cshalizi)</title>
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    <description>recent bookmarks from cshalizi</description>
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	<rdf:li rdf:resource="http://mathbabe.org/2013/03/17/modeling-in-plain-english/"/>
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	<rdf:li rdf:resource="http://www.pnas.org/content/107/43/18243.abstract?etoc"/>
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  </channel><item rdf:about="https://doi.org/10.1017/9781009029346">
    <title>Scientific Models and Decision Making</title>
    <dc:date>2026-04-17T02:56:41+00:00</dc:date>
    <link>https://doi.org/10.1017/9781009029346</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This Element introduces the philosophical literature on models, with an emphasis on normative considerations relevant to models for decision-making. Chapter 1 gives an overview of core questions in the philosophy of modeling. Chapter 2 examines the concept of model adequacy for purpose, using three examples of models from the atmospheric sciences to describe how this sort of adequacy is determined in practice. Chapter 3 explores the significance of using models that are not adequate for purpose, including the purpose of informing public decisions. Chapter 4 provides a basic framework for values in modelling, using a case study to highlight the ethical challenges in building models for decision making. It concludes by establishing the need for strategies to manage value judgments in modelling, including the potential for public participation in the process."]]></description>
<dc:subject>to:NB books:noted philosophy_of_science modeling political_philosophy science_as_a_social_process</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6cc19b65d04c/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
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<item rdf:about="https://direct.mit.edu/books/oa-monograph/6002/The-Idealized-MindFrom-Model-Based-Science-to">
    <title>The Idealized Mind: From Model-Based Science to Cognitive Science | Books Gateway | MIT Press</title>
    <dc:date>2026-02-15T16:09:47+00:00</dc:date>
    <link>https://direct.mit.edu/books/oa-monograph/6002/The-Idealized-MindFrom-Model-Based-Science-to</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We study nature, including the mind and brain, by building scientific models. In The Idealized Mind, Michael Kirchhoff brings together ideas from the philosophy of cognitive science and the philosophy of science to reconcile scientific realism with model-based science. His defense of scientific realism—the view that one reasonable aim of science is to provide true (or approximately true) descriptions of reality—is based on the role of idealization in the cognitive sciences. Idealization, he claims, is inevitable in cognitive science; at the same time, any understanding of the mind and brain must show how it is possible for scientific models to be reliably used to make truth-conditional assertions about their target phenomena.
"A central error in most theorizing about the mind, Kirchhoff claims, is to confuse the properties of scientific models with those of the system being modeled. But scientific models are, almost exclusively and unavoidably, idealizations of the world we seek to understand. They are descriptions of hypothetical systems, things that do not actually exist in nature. Specifically, Kirchhoff uses insights on idealization in science to assess the status and standing of three foundational issues in cognitive science: neural representation, neural computation, and the prospects for explanatory unification. He also explains why it is a mistake to approach neural representation and neural computation through the metaphysical stances of realism, fictionalism, or eliminativism."]]></description>
<dc:subject>books:noted downloaded philosophy_of_science philosophy_of_mind cognitive_science modeling in_NB</dc:subject>
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<dc:identifier>https://pinboard.in/u:cshalizi/b:faeee5ac6bbf/</dc:identifier>
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<item rdf:about="http://econospeak.blogspot.com/2023/03/escape-from-muddle-land.html">
    <title>EconoSpeak: Escape from Muddle Land</title>
    <dc:date>2023-08-10T19:21:49+00:00</dc:date>
    <link>http://econospeak.blogspot.com/2023/03/escape-from-muddle-land.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- This does not make me want to read the book, to say the least.]]></description>
<dc:subject>book_reviews have_read modeling</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:cb241670866e/</dc:identifier>
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<item rdf:about="https://www.physics.upenn.edu/biophys/PMLS2e/index.html">
    <title>Physical Models of Living Systems Second Edition | Philip Nelson</title>
    <dc:date>2023-06-24T20:06:46+00:00</dc:date>
    <link>https://www.physics.upenn.edu/biophys/PMLS2e/index.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Physical Models of Living Systems is a textbook intended for intermediate-level undergraduates in any science or engineering major. The only prerequisite for this course is first-year physics. Supplementary sections make the book also suitable as the basis of a graduate-level course."]]></description>
<dc:subject>biophysics modeling books:noted in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ff770711ae2d/</dc:identifier>
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<item rdf:about="https://link.springer.com/chapter/10.1007/978-3-662-08546-2_12">
    <title>Stochastic Realization Theory | SpringerLink</title>
    <dc:date>2023-05-19T03:28:05+00:00</dc:date>
    <link>https://link.springer.com/chapter/10.1007/978-3-662-08546-2_12</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The use of state-space models for modelling and processing of random signals was introduced by Kalman at the very beginning of the history of System Theory. Although spectacular successes have emerged from the introduction of these models (Kalman filtering to name just one), until quite recently there has not been any serious effort of putting together in a logically consistent way a theory of modelling and model representation in the stochastic frame. Expanding applications to diverse fields like Econometrics etc. and a multitude of nonstandard estimation problems arising in engineering applications seem now to render the need for such a theory more urgent."]]></description>
<dc:subject>to:NB stochastic_processes modeling via:mraginsky</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
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    <title>[2301.13724] The passive symmetries of machine learning</title>
    <dc:date>2023-05-02T21:11:45+00:00</dc:date>
    <link>https://arxiv.org/abs/2301.13724</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Any representation of data involves arbitrary investigator choices. Because those choices are external to the data-generating process, each choice leads to an exact symmetry, corresponding to the group of transformations that takes one possible representation to another. These are the passive symmetries; they include coordinate freedom, gauge symmetry and units covariance, all of which have led to important results in physics. Our goal is to understand the implications of passive symmetries for machine learning: Which passive symmetries play a role (e.g., permutation symmetry in graph neural networks)? What are dos and don'ts in machine learning practice? We assay conditions under which passive symmetries can be implemented as group equivariances. We also discuss links to causal modeling, and argue that the implementation of passive symmetries is particularly valuable when the goal of the learning problem is to generalize out of sample. While this paper is purely conceptual, we believe that it can have a significant impact on helping machine learning make the transition that took place for modern physics in the first half of the Twentieth century."]]></description>
<dc:subject>symmetry machine_learning statistics via:rvenkat modeling scholkopf.bernhard in_NB</dc:subject>
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    <title>Use of ‘too hot’ climate models exaggerates impacts of global warming | Science | AAAS</title>
    <dc:date>2022-06-07T14:20:28+00:00</dc:date>
    <link>https://www.science.org/content/article/use-too-hot-climate-models-exaggerates-impacts-global-warming</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>have_read climate_change modeling simulation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:7ca9bdc3ecfb/</dc:identifier>
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<item rdf:about="https://www.journals.uchicago.edu/doi/10.1093/bjps/axy039">
    <title>Robustness and Idealizations in Agent-Based Models of Scientific Interaction | The British Journal for the Philosophy of Science: Vol 71, No 4</title>
    <dc:date>2022-03-19T23:02:38+00:00</dc:date>
    <link>https://www.journals.uchicago.edu/doi/10.1093/bjps/axy039</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The article presents an agent-based model (ABM) of scientific interaction aimed at examining how different degrees of connectedness of scientists impact their efficiency in knowledge acquisition. The model is built on the basis of Zollman’s ([2010]) ABM by changing some of its idealizing assumptions that concern the representation of the central notions underlying the model: epistemic success of the rivalling scientific theories, scientific interaction and the assessment in view of which scientists choose theories to work on. Our results suggest that whether and to what extent the degree of connectedness of a scientific community impacts its efficiency is a highly context-dependent matter since different conditions deem strikingly different results. More generally, we argue that simplicity of ABMs may come at a price: the requirement to run extensive robustness analysis before we can specify the adequate target phenomenon of the model.1"]]></description>
<dc:subject>agent-based_models model_checking science_as_a_social_process modeling via:? in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:27e2f1980d16/</dc:identifier>
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<item rdf:about="https://onlinelibrary.wiley.com/doi/10.1111/jiec.13084">
    <title>Update to limits to growth: Comparing the World3 model with empirical data - Herrington - 2021 - Journal of Industrial Ecology - Wiley Online Library</title>
    <dc:date>2022-03-16T15:16:53+00:00</dc:date>
    <link>https://onlinelibrary.wiley.com/doi/10.1111/jiec.13084</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In the 1972 bestseller Limits to Growth (LtG), the authors concluded that, if global society kept pursuing economic growth, it would experience a decline in food production, industrial output, and ultimately population, within this century. The LtG authors used a system dynamics model to study interactions between global variables, varying model assumptions to generate different scenarios. Previous empirical-data comparisons since then by Turner showed closest alignment with a scenario that ended in collapse. This research constitutes a data update to LtG, by examining to what extent empirical data aligned with four LtG scenarios spanning a range of technological, resource, and societal assumptions. The research benefited from improved data availability since the previous updates and included a scenario and two variables that had not been part of previous comparisons. The two scenarios aligning most closely with observed data indicate a halt in welfare, food, and industrial production over the next decade or so, which puts into question the suitability of continuous economic growth as humanity's goal in the twenty-first century. Both scenarios also indicate subsequent declines in these variables, but only one—where declines are caused by pollution—depicts a collapse. The scenario that aligned most closely in earlier comparisons was not amongst the two closest aligning scenarios in this research. The scenario with the smallest declines aligned least with empirical data; however, absolute differences were often not yet large. The four scenarios diverge significantly more after 2020, suggesting that the window to align with this last scenario is closing."

--- The last tag does not do justice to my degree of disbelief in this sort of modeling, nor in efforts to pretend that it was accurate all along.]]></description>
<dc:subject>to:NB modeling environmental_management via:? color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:af8819de54fc/</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:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:environmental_management"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://link.springer.com/book/10.1007/978-3-030-45153-0">
    <title>Modelling Nature: An Opinionated Introduction to Scientific Representation | SpringerLink</title>
    <dc:date>2021-12-18T05:56:16+00:00</dc:date>
    <link>https://link.springer.com/book/10.1007/978-3-030-45153-0</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This monograph offers a critical introduction to current theories of how scientific models represent their target systems. Representation is important because it allows scientists to study a model to discover features of reality. The authors provide a map of the conceptual landscape surrounding the issue of scientific representation, arguing that it consists of multiple intertwined problems. They provide an encyclopaedic overview of existing attempts to answer these questions, and they assess their strengths and weaknesses. The book also presents a comprehensive statement of their alternative proposal, the DEKI account of representation, which they have developed over the last few years. They show how the account works in the case of material as well as non-material models; how it accommodates the use of mathematics in scientific modelling; and how it sheds light on the relation between representation in science and art. The issue of representation has generated a sizeable literature, which has been growing fast in particular over the last decade. This makes it hard for novices to get a handle on the topic because so far there is no book-length introduction that would guide them through the discussion. Likewise, researchers may require a comprehensive review that they can refer to for critical evaluations. This book meets the needs of both groups."]]></description>
<dc:subject>to:NB books:noted philosophy_of_science modeling representation books:suggest_to_library</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:9f5fbeffa09b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:suggest_to_library"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.pnas.org/content/118/15/e2022886118">
    <title>Addressing partial identification in climate modeling and policy analysis | PNAS</title>
    <dc:date>2021-04-14T14:30:38+00:00</dc:date>
    <link>https://www.pnas.org/content/118/15/e2022886118</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Numerical simulations of the global climate system provide inputs to integrated assessment modeling for estimating the impacts of greenhouse gas mitigation and other policies to address global climate change. While essential tools for this purpose, computational climate models are subject to considerable uncertainty, including intermodel “structural” uncertainty. Structural uncertainty analysis has emphasized simple or weighted averaging of the outputs of multimodel ensembles, sometimes with subjective Bayesian assignment of probabilities across models. However, choosing appropriate weights is problematic. To use climate simulations in integrated assessment, we propose, instead, framing climate model uncertainty as a problem of partial identification, or “deep” uncertainty. This terminology refers to situations in which the underlying mechanisms, dynamics, or laws governing a system are not completely known and cannot be credibly modeled definitively even in the absence of data limitations in a statistical sense. We propose the min−max regret (MMR) decision criterion to account for deep climate uncertainty in integrated assessment without weighting climate model forecasts. We develop a theoretical framework for cost−benefit analysis of climate policy based on MMR, and apply it computationally with a simple integrated assessment model. We suggest avenues for further research."

--- On the one hand, Manski on partial identification is self-recommending.  On the other hand, it's a contributed paper in PNAS.

--- ETA after reading: Even Homer nods sometimes.  There's no actual need for, or use of, Manski's (pathbreaking) work on partial identification here.  Rather, the core of this is two quite simple, but sensible, recommendations: (i) if there are multiple good models, one way to represent uncertainty is simply to show the spread of different models, rather than trying to come up with weights that will force some sort of average; (ii) for each policy, find the optimal course of action, and then select the model-optimal policy which minimizes the maximum regret.  No connection to low-regret learning, which of course _does_ use weights.

--- It'd probably be good for the planet if we could leave Manski and Claire Monteleoni alone together on an island for a month.]]></description>
<dc:subject>climate_change decision_theory partial_identification modeling have_read to_blog in_NB manski.charles_f.</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:48bbbb0df653/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:climate_change"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:decision_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:partial_identification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_blog"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:manski.charles_f."/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://psyarxiv.com/rybh9/">
    <title>PsyArXiv Preprints | How computational modeling can force theory building in psychological science</title>
    <dc:date>2020-12-03T03:01:39+00:00</dc:date>
    <link>https://psyarxiv.com/rybh9/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Psychology endeavors to develop theories of human capacities and behaviors based on a variety of methodologies and dependent measures. We argue that one of the most divisive factors in our field is whether researchers choose to employ computational modeling of theories (over and above data) during the scientific inference process. Modeling is undervalued, yet holds promise for advancing psychological science. The inherent demands of computational modeling guide us towards better science by forcing us to conceptually analyze, specify, and formalise intuitions which otherwise remain unexamined — what we dub “open theory”. Constraining our inference process through modeling enables us to build explanatory and predictive theories. Herein, we present scientific inference in psychology as a path function, where each step shapes the next. Computational modeling can constrain these steps, thus advancing scientific inference over and above stewardship of experimental practice (e.g., preregistration). If psychology continues to eschew computational modeling, we predict more replicability “crises” and persistent failure at coherent theory-building. This is because without formal modelling we lack open and transparent theorising. We also explain how to formalise, specify, and implement a computational model, emphasizing that the advantages of modeling can be achieved by anyone with benefit to all."]]></description>
<dc:subject>to:NB psychology cognitive_science modeling</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:dcccb0192551/</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:psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://mitpress.mit.edu/books/computational-psychiatry-1">
    <title>Computational Psychiatry | The MIT Press</title>
    <dc:date>2020-09-21T03:56:32+00:00</dc:date>
    <link>https://mitpress.mit.edu/books/computational-psychiatry-1</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Computational psychiatry applies computational modeling and theoretical approaches to psychiatric questions, focusing on building mathematical models of neural or cognitive phenomena relevant to psychiatric diseases. It is a young and rapidly growing field, drawing on concepts from psychiatry, psychology, computer science, neuroscience, electrical and chemical engineering, mathematics, and physics. This book, accessible to nonspecialists, offers the first introductory textbook in computational psychiatry.
"After more than 100 years of psychological theories, psychopharmacological research, and clinical experience, the challenges of understanding and treating mental illness remain. Computational psychiatry seeks to explain how psychiatric dysfunction may emerge mechanistically, and how it may be classified, predicted, and clinically addressed. It has the potential to bridge advances in neuroscience and clinical applications, connecting low-level biological features with high-level cognitive features. After a survey of computational psychiatry methods, the book covers biologically detailed models of working memory and decision making and computational models of cognitive control. It then describes the application of computational approaches to schizophrenia, depression, anxiety, addiction, and Tourette's syndrome. Finally, the book briefly discusses additional disorders and offers guidelines for future research. Chapters also offer discussions of related issues, chapter summaries, and suggestions for further study. The book can be used as a textbook by students and as a reference for scientists and clinicians interested in applying computational models to diagnosis and treatment strategies."]]></description>
<dc:subject>to:NB books:noted psychiatry psychology modeling books:suggest_to_library</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:837ed6c0dc6f/</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:psychiatry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:suggest_to_library"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.journals.uchicago.edu/doi/abs/10.1086/699021">
    <title>Modeling: Neutral, Null, and Baseline | Philosophy of Science: Vol 85, No 4</title>
    <dc:date>2019-10-24T18:36:47+00:00</dc:date>
    <link>https://www.journals.uchicago.edu/doi/abs/10.1086/699021</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Two strategies for using a model as “null” are distinguished. Null modeling evaluates whether a process is causally responsible for a pattern by testing it against a null model. Baseline modeling measures the relative significance of various processes responsible for a pattern by detecting deviations from a baseline model. When these strategies are conflated, models are illegitimately privileged as accepted until rejected. I illustrate this using the neutral theory of ecology and draw general lessons from this case. First, scientists cannot draw certain conclusions using null modeling. Second, these conclusions follow using baseline modeling, but doing so requires more evidence."]]></description>
<dc:subject>to:NB neutral_models philosophy_of_science modeling</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c86cebf41e00/</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:neutral_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.annualreviews.org/doi/abs/10.1146/annurev-ecolsys-110617-062249">
    <title>Evaluating Model Performance in Evolutionary Biology | Annual Review of Ecology, Evolution, and Systematics</title>
    <dc:date>2019-05-26T18:10:58+00:00</dc:date>
    <link>https://www.annualreviews.org/doi/abs/10.1146/annurev-ecolsys-110617-062249</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Many fields of evolutionary biology now depend on stochastic mathematical models. These models are valuable for their ability to formalize predictions in the face of uncertainty and provide a quantitative framework for testing hypotheses. However, no mathematical model will fully capture biological complexity. Instead, these models attempt to capture the important features of biological systems using relatively simple mathematical principles. These simplifications can allow us to focus on differences that are meaningful, while ignoring those that are not. However, simplification also requires assumptions, and to the extent that these are wrong, so is our ability to predict or compare. Here, we discuss approaches for evaluating the performance of evolutionary models in light of their assumptions by comparing them against reality. We highlight general approaches, how they are applied, and remaining opportunities. Absolute tests of fit, even when not explicitly framed as such, are fundamental to progress in understanding evolution."]]></description>
<dc:subject>to:NB evolutionary_biology stochastic_models modeling model_checking statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:cb030510dd10/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evolutionary_biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:model_checking"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://link.springer.com/article/10.1007/s11023-018-9484-3">
    <title>Peeking Inside the Black Box: A New Kind of Scientific Visualization | SpringerLink</title>
    <dc:date>2019-05-14T16:11:42+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/s11023-018-9484-3</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Computational systems biologists create and manipulate computational models of biological systems, but they do not always have straightforward epistemic access to the content and behavioural profile of such models because of their length, coding idiosyncrasies, and formal complexity. This creates difficulties both for modellers in their research groups and for their bioscience collaborators who rely on these models. In this paper we introduce a new kind of visualization (observed in a qualitative study of a systems biology laboratory) that was developed to address just this sort of epistemic opacity. The visualization is unusual in that it depicts the dynamics and structure of a computer model instead of that model’s target system, and because it is generated algorithmically. Using considerations from epistemology and aesthetics, we explore how this new kind of visualization increases scientific understanding of the content and function of computer models in systems biology to reduce epistemic opacity."]]></description>
<dc:subject>to:NB visual_display_of_quantitative_information modeling simulation sociology_of_science</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:16794a8e7e35/</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:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sociology_of_science"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/us/academic/subjects/mathematics/mathematical-modelling-and-methods/mathematical-modelling-human-cardiovascular-system-data-numerical-approximation-clinical-applications?format=HB&amp;WT.mc_id=LFA-MAT-CL-ComingSoon%2BApplied-April-2019">
    <title>Mathematical modelling of the human cardiovascular system</title>
    <dc:date>2019-05-14T15:56:45+00:00</dc:date>
    <link>https://www.cambridge.org/us/academic/subjects/mathematics/mathematical-modelling-and-methods/mathematical-modelling-human-cardiovascular-system-data-numerical-approximation-clinical-applications?format=HB&amp;WT.mc_id=LFA-MAT-CL-ComingSoon%2BApplied-April-2019</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Mathematical and numerical modelling of the human cardiovascular system has attracted remarkable research interest due to its intrinsic mathematical difficulty and the increasing impact of cardiovascular diseases worldwide. This book addresses the two principal components of the cardiovascular system: arterial circulation and heart function. It systematically describes all aspects of the problem, stating the basic physical principles, analysing the associated mathematical models that comprise PDE and ODE systems, reviewing sound and efficient numerical methods for their approximation, and simulating both benchmark problems and clinically inspired problems. Mathematical modelling itself imposes tremendous challenges, due to the amazing complexity of the cardiovascular system and the need for computational methods that are stable, reliable and efficient. The final part is devoted to control and inverse problems, including parameter estimation, uncertainty quantiﬁcation and the development of reduced-order models that are important when solving problems with high complexity, which would otherwise be out of reach."]]></description>
<dc:subject>to:NB books:noted modeling biology excitable_media</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f3f16102d4a0/</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:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:excitable_media"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://onlinelibrary.wiley.com/doi/book/10.1002/9781118109656">
    <title>Empirical Model Building | Wiley Series in Probability and Statistics</title>
    <dc:date>2019-01-07T17:52:08+00:00</dc:date>
    <link>https://onlinelibrary.wiley.com/doi/book/10.1002/9781118109656</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Successful empirical model building is founded on the relationship between data and approximate representations of the real systems that generated that data. As a result, it is essential for researchers who construct these models to possess the special skills and techniques for producing results that are insightful, reliable, and useful. Empirical Model Building: Data, Models, and Reality, Second Edition presents a hands-on approach to the basic principles of empirical model building through a shrewd mixture of differential equations, computer-intensive methods, and data. The book outlines both classical and new approaches and incorporates numerous real-world statistical problems that illustrate modeling approaches that are applicable to a broad range of audiences, including applied statisticians and practicing engineers and scientists.
"The book continues to review models of growth and decay, systems where competition and interaction add to the complextiy of the model while discussing both classical and non-classical data analysis methods. This Second Edition now features further coverage of momentum based investing practices and resampling techniques, showcasing their importance and expediency in the real world. The author provides applications of empirical modeling, such as computer modeling of the AIDS epidemic to explain why North America has most of the AIDS cases in the First World and data-based strategies that allow individual investors to build their own investment portfolios. Throughout the book, computer-based analysis is emphasized and newly added and updated exercises allow readers to test their comprehension of the presented material."]]></description>
<dc:subject>to:NB books:noted modeling simulation time_series statistics downloaded color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:63eb65e51c65/</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:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:downloaded"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://onlinelibrary.wiley.com/doi/book/10.1002/0470870958">
    <title>Sensitivity Analysis in Practice | Wiley Online Books</title>
    <dc:date>2019-01-07T17:40:06+00:00</dc:date>
    <link>https://onlinelibrary.wiley.com/doi/book/10.1002/0470870958</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Sensitivity analysis should be considered a pre-requisite for statistical model building in any scientific discipline where modelling takes place. For a non-expert, choosing the method of analysis for their model is complex, and depends on a number of factors. This  book guides the non-expert through their problem in order to enable them to choose and apply the most appropriate method. It offers a review of the state-of-the-art in sensitivity analysis, and is suitable for a wide range of practitioners. It is focussed on the use of SIMLAB – a widely distributed freely-available sensitivity analysis software package developed by the authors – for solving problems in sensitivity analysis of statistical models."]]></description>
<dc:subject>to:NB books:noted downloaded sensitivity_analysis modeling simulation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:99227a97c37f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:downloaded"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sensitivity_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:simulation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://link.springer.com/book/10.1007/978-3-319-65433-1#about">
    <title>Model-Based Demography | SpringerLink</title>
    <dc:date>2019-01-06T15:33:15+00:00</dc:date>
    <link>https://link.springer.com/book/10.1007/978-3-319-65433-1#about</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Late in a career of more than sixty years, Thomas Burch, an internationally known social demographer, undertook a wide-ranging methodological critique of demography. This open access volume contains  a selection of resulting papers, some previously unpublished, some published but not readily accessible [from past meetings of The International Union for the Scientific Study of Population and its research committees, or from other small conferences and seminars]. Rejecting the idea that demography is simply a branch of applied statistics, his work views it as an autonomous and complete scientific discipline. When viewed from the perspective of modern philosophy of science, specifically the semantic or model-based school, demography is a balanced discipline, with a rich body of techniques and data, but also with more and better theories than generally recognized. As demonstrated in this book, some demographic techniques can also be seen as theoretical models, and some substantive/behavioral models, commonly rejected as theory because of inconsistent observations, are now seen as valuable theoretical models, for example demographic transition theory.  This book shows how demography can build a strong theoretical edifice on its broad and deep empirical foundation by adoption of the model-based approach to science. But the full-fruits of this approach will require demographers to make greater use of computer modeling [both macro- and micro-simulation], in the statement and manipulation of theoretical ideas, as well as for numerical computation.
"This book is open access under a CC BY license."]]></description>
<dc:subject>books:noted demography downloaded philosophy_of_science modeling in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:da6346056bfc/</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:demography"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:downloaded"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/us/academic/subjects/economics/history-economic-thought-and-methodology/models-mathematics-and-methodology-economic-explanation?format=HB&amp;isbn=9781108418775">
    <title>Models mathematics and methodology in economic explanation | History of economic thought and methodology | Cambridge University Press</title>
    <dc:date>2018-12-14T17:06:50+00:00</dc:date>
    <link>https://www.cambridge.org/us/academic/subjects/economics/history-economic-thought-and-methodology/models-mathematics-and-methodology-economic-explanation?format=HB&amp;isbn=9781108418775</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This book provides a practitioner's foundation for the process of explanatory model building, breaking down that process into five stages. Donald W. Katzner presents a concrete example with unquantified variable values to show how the five-stage procedure works. He describes what is involved in explanatory model building for those interested in this practice, while simultaneously providing a guide for those actually engaged in it. The combination of Katzner's focus on modeling and on mathematics, along with his focus on the explanatory performance of modeling, promises to become an important contribution to the field."]]></description>
<dc:subject>to:NB books:noted social_science_methodology modeling economics philosophy_of_science</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:9ac21bde945b/</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:social_science_methodology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://onlinelibrary.wiley.com/doi/book/10.1002/9781118527085">
    <title>Spatial Simulation | Wiley Online Books</title>
    <dc:date>2018-08-07T16:00:18+00:00</dc:date>
    <link>https://onlinelibrary.wiley.com/doi/book/10.1002/9781118527085</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Across broad areas of the environmental and social sciences, simulation models are  an important way to study systems inaccessible to scientific experimental and observational methods, and also an essential complement to those more conventional approaches.  The contemporary research literature is teeming with abstract simulation models whose presentation is mathematically demanding and requires a high level of knowledge of quantitative and computational methods and approaches.  Furthermore, simulation models designed to represent specific systems and phenomena are often complicated, and, as a result, difficult to reconstruct from their descriptions in the literature.  This book aims to provide a practical and accessible account of dynamic spatial modelling, while also equipping readers with a sound conceptual foundation in the subject, and a useful introduction to the wide-ranging literature.
"Spatial Simulation: Exploring Pattern and Process is organised around the idea that a small number of spatial processes underlie the wide variety of dynamic spatial models. Its central focus on three ‘building-blocks’ of dynamic spatial models – forces of attraction and segregation, individual mobile entities, and processes of spread – guides the reader to an understanding of the basis of many of the complicated models found in the research literature. The three building block models are presented in their simplest form and are progressively elaborated and related to real world process that can be represented using them.  Introductory chapters cover essential background topics, particularly the relationships between pattern, process and spatiotemporal scale.  Additional chapters consider how time and space can be represented in more complicated models, and methods for the analysis and evaluation of models. Finally, the three building block models are woven together in a more elaborate example to show how a complicated model can be assembled from relatively simple components.
"To aid understanding, more than 50 specific models described in the book are available online at patternandprocess.org for exploration in the freely available Netlogo platform.  This book encourages readers to develop intuition for the abstract types of model that are likely to be appropriate for application in any specific context.  Spatial Simulation: Exploring Pattern and Process will be of interest to undergraduate and graduate students taking courses in environmental, social, ecological and geographical disciplines.  Researchers and professionals who require a non-specialist introduction will also find this book an invaluable guide to dynamic spatial simulation."

--- This looks cool, but it'd kind of blow the kids minds, so the last tag is really more "to mine for examples" than "to teach".]]></description>
<dc:subject>books:noted simulation modeling cellular_automata to_teach:data_over_space_and_time in_NB books:owned</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:18fe52af0a69/</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:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cellular_automata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:owned"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.cambridge.org/us/academic/subjects/mathematics/differential-and-integral-equations-dynamical-systems-and-co/volterra-integral-equations-introduction-theory-and-applications?format=HB&amp;WT.mc_id=LFA-MAT-CL-CMACM%2Bseries#6F2r81CQjhxbvyFF.97">
    <title>Volterra integral equations introduction theory and applications | Differential and integral equations, dynamical systems and control | Cambridge University Press</title>
    <dc:date>2018-06-23T16:41:36+00:00</dc:date>
    <link>http://www.cambridge.org/us/academic/subjects/mathematics/differential-and-integral-equations-dynamical-systems-and-co/volterra-integral-equations-introduction-theory-and-applications?format=HB&amp;WT.mc_id=LFA-MAT-CL-CMACM%2Bseries#6F2r81CQjhxbvyFF.97</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This book offers a comprehensive introduction to the theory of linear and nonlinear Volterra integral equations (VIEs), ranging from Volterra's fundamental contributions and the resulting classical theory to more recent developments that include Volterra functional integral equations with various kinds of delays, VIEs with highly oscillatory kernels, and VIEs with non-compact operators. It will act as a 'stepping stone' to the literature on the advanced theory of VIEs, bringing the reader to the current state of the art in the theory. Each chapter contains a large number of exercises, extending from routine problems illustrating or complementing the theory to challenging open research problems. The increasingly important role of VIEs in the mathematical modelling of phenomena where memory effects play a key role is illustrated with some 30 concrete examples, and the notes at the end of each chapter feature complementary references as a guide to further reading."]]></description>
<dc:subject>to:NB mathematics modeling books:noted</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:73f9bef8caaa/</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:mathematics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.mitpressjournals.org/doi/abs/10.1162/POSC_a_00273">
    <title>Explaining with Simulations: Why Visual Representations Matter | Perspectives on Science | MIT Press Journals</title>
    <dc:date>2018-04-04T14:05:32+00:00</dc:date>
    <link>https://www.mitpressjournals.org/doi/abs/10.1162/POSC_a_00273</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Computer simulations are often expected to provide explanations about target phenomena. However there is a gap between the simulation outputs and the underlying model, which prevents users finding the relevant explanatory components within the model. I contend that visual representations which adequately display the simulation outputs can nevertheless be used to get explanations. In order to do so, I elaborate on the way graphs and pictures can help one to explain the behavior of a flow past a cylinder. I then specify the reasons that make more generally visual representations particularly suitable for explanatory tasks in a computer-assisted context."]]></description>
<dc:subject>to:NB simulation modeling explanation philosophy_of_science visual_display_of_quantitative_information</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2004b56035a6/</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:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:explanation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.hup.harvard.edu/catalog.php?isbn=9780674975002">
    <title>As If — Kwame Anthony Appiah | Harvard University Press</title>
    <dc:date>2017-09-07T16:03:33+00:00</dc:date>
    <link>http://www.hup.harvard.edu/catalog.php?isbn=9780674975002</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Idealization is a fundamental feature of human thought. We build simplified models in our scientific research and utopias in our political imaginations. Concepts like belief, desire, reason, and justice are bound up with idealizations and ideals. Life is a constant adjustment between the models we make and the realities we encounter. In idealizing, we proceed “as if” our representations were true, while knowing they are not. This is not a dangerous or distracting occupation, Kwame Anthony Appiah shows. Our best chance of understanding nature, society, and ourselves is to open our minds to a plurality of imperfect depictions that together allow us to manage and interpret our world.
"The philosopher Hans Vaihinger first delineated the “as if” impulse at the turn of the twentieth century, drawing on Kant, who argued that rational agency required us to act as if we were free. Appiah extends this strategy to examples across philosophy and the human and natural sciences. In a broad range of activities, we have some notion of the truth yet continue with theories that we recognize are, strictly speaking, false. From this vantage point, Appiah demonstrates that a picture one knows to be unreal can be a vehicle for accessing reality.
"As If explores how strategic untruth plays a critical role in far-flung areas of inquiry: decision theory, psychology, natural science, and political philosophy. A polymath who writes with mainstream clarity, Appiah defends the centrality of the imagination not just in the arts but in science, morality, and everyday life."]]></description>
<dc:subject>books:noted philosophy epistemology philosophy_of_science approximation modeling idealization in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d992f3c0970c/</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"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epistemology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:approximation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:idealization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://mitpress.mit.edu/books/true-enough">
    <title>True Enough | The MIT Press</title>
    <dc:date>2017-09-05T23:38:22+00:00</dc:date>
    <link>https://mitpress.mit.edu/books/true-enough</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Philosophy valorizes truth, holding that there can never be epistemically good reasons to accept a known falsehood, or to accept modes of justification that are not truth conducive. How can this stance account for the epistemic standing of science, which unabashedly relies on models, idealizations, and thought experiments that are known not to be true? In True Enough, Catherine Elgin argues that we should not assume that the inaccuracy of models and idealizations constitutes an inadequacy. To the contrary, their divergence from truth or representational accuracy fosters their epistemic functioning. When effective, models and idealizations are, Elgin contends, felicitous falsehoods that exemplify features of the phenomena they bear on. Because works of art deploy the same sorts of felicitous falsehoods, she argues, they also advance understanding.
"Elgin develops a holistic epistemology that focuses on the understanding of broad ranges of phenomena rather than knowledge of individual facts. Epistemic acceptability, she maintains, is a matter not of truth-conduciveness, but of what would be reflectively endorsed by the members of an idealized epistemic community—a quasi-Kantian realm of epistemic ends."

--- Well, the first part sounds interesting...]]></description>
<dc:subject>books:noted modeling epistemology philosophy_of_science in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:85574dc241ad/</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:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epistemology"/>
	<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="http://drodrik.scholar.harvard.edu/files/dani-rodrik/files/ariel_rubinstein_jel_2016.pdf">
    <title>Comments on Economic Models, Economics and Economists: Remarks on _Economics Rules_ by Dani Rodrik</title>
    <dc:date>2016-12-12T21:59:13+00:00</dc:date>
    <link>http://drodrik.scholar.harvard.edu/files/dani-rodrik/files/ariel_rubinstein_jel_2016.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[This is a very interesting review, which manages both to be generous and to expound on the reviewer's own notions.  (And it speaks well of Rodrik to host it!)  But it seems to me that if one accepts Rubinstein's views, one is left with no real reason to value economic theory, or even economic models in applications.  That might be the correct conclusion, but it's clearly not the one Rubinstein has drawn himself, so...  (Cf. http://bactra.org/reviews/modeling-bounded-rationality/)]]></description>
<dc:subject>social_science_methodology economics modeling philosophy_of_science rubinstein.ariel rodrik.dani via:henry_farrell book_reviews have_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c04b75d1efdb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_science_methodology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:rubinstein.ariel"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:rodrik.dani"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:henry_farrell"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:book_reviews"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.nap.edu/catalog/23650/from-maps-to-models-augmenting-the-nations-geospatial-intelligence-capabilities">
    <title>From Maps to Models: Augmenting the Nation's Geospatial Intelligence Capabilities | The National Academies Press</title>
    <dc:date>2016-11-08T01:25:43+00:00</dc:date>
    <link>https://www.nap.edu/catalog/23650/from-maps-to-models-augmenting-the-nations-geospatial-intelligence-capabilities</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The United States faces numerous, varied, and evolving threats to national security, including terrorism, scarcity and disruption of food and water supplies, extreme weather events, and regional conflicts around the world. Effectively managing these threats requires intelligence that not only assesses what is happening now, but that also anticipates potential future threats. The National Geospatial-Intelligence Agency (NGA) is responsible for providing geospatial intelligence on other countries—assessing where exactly something is, what it is, and why it is important—in support of national security, disaster response, and humanitarian assistance. NGA’s approach today relies heavily on imagery analysis and mapping, which provide an assessment of current and past conditions. However, augmenting that approach with a strong modeling capability would enable NGA to also anticipate and explore future outcomes.
"A model is a simplified representation of a real-world system that is used to extract explainable insights about the system, predict future outcomes, or explore what might happen under plausible what-if scenarios. Such models use data and/or theory to specify inputs (e.g., initial conditions, boundary conditions, and model parameters) to produce an output.
"From Maps to Models: Augmenting the Nation's Geospatial Intelligence Capabilities describes the types of models and analytical methods used to understand real-world systems, discusses what would be required to make these models and methods useful for geospatial intelligence, and identifies supporting research and development for NGA. This report provides examples of models that have been used to help answer the sorts of questions NGA might ask, describes how to go about a model-based investigation, and discusses models and methods that are relevant to NGA’s mission."]]></description>
<dc:subject>to:NB books:noted cartography intelligence_(spying) modeling</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f4e482385d14/</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:cartography"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:intelligence_(spying)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.mitpressjournals.org/doi/abs/10.1162/POSC_a_00214">
    <title>Modeling the Heavens: Sphairopoiia and Ptolemy’s Planetary Hypotheses</title>
    <dc:date>2016-07-06T16:40:46+00:00</dc:date>
    <link>http://www.mitpressjournals.org/doi/abs/10.1162/POSC_a_00214</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This article investigates sphairopoiia, the art of making instruments that display the heavens, in Claudius Ptolemy’s Planetary Hypotheses. It takes up two questions: what kind of instrument does Ptolemy describe? And, could such an instrument have been constructed? I argue that Ptolemy did not propose one specific type of instrument, but instead he offered a range of possible designs, with the details to be worked out by the craftsman. Moreover, in addition to exhibiting his astronomical models and having the ability to estimate predictions, the instrument he proposed would have also shown the physical workings of the heavens. What emerges is both a clearer idea of what Ptolemy wanted the technician to build, and the purpose of such instruments."]]></description>
<dc:subject>to:NB history_of_science astronomy ptolemy modeling prediction history_of_technology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d5b53c243511/</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:history_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:astronomy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ptolemy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_technology"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1606.03490#">
    <title>[1606.03490] The Mythos of Model Interpretability</title>
    <dc:date>2016-07-05T14:02:28+00:00</dc:date>
    <link>https://arxiv.org/abs/1606.03490#</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Supervised machine learning models boast remarkable predictive capabilities. But can you trust your model? Will it work in deployment? What else can it tell you about the world? We want models to be not only good, but interpretable. And yet the task of interpretation appears underspecified. Papers provide diverse and sometimes non-overlapping motivations for interpretability, and offer myriad notions of what attributes render models interpretable. Despite this ambiguity, many papers proclaim interpretability axiomatically, absent further explanation. In this paper, we seek to refine the discourse on interpretability. First, we examine the motivations underlying interest in interpretability, finding them to be diverse and occasionally discordant. Then, we address model properties and techniques thought to confer interpretability, identifying transparency to humans and post-hoc explanations as competing notions. Throughout, we discuss the feasibility and desirability of different notions, and question the oft-made assertions that linear models are interpretable and that deep neural networks are not."]]></description>
<dc:subject>to:NB data_mining statistics modeling via:vaguery to_teach color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:536917692927/</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:data_mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:vaguery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://ndpr.nd.edu/news/59657-reconstructing-reality-models-mathematics-and-simulations/">
    <title>Reconstructing Reality: Models, Mathematics, and Simulations // Reviews // Notre Dame Philosophical Reviews // University of Notre Dame</title>
    <dc:date>2016-01-17T14:09:41+00:00</dc:date>
    <link>https://ndpr.nd.edu/news/59657-reconstructing-reality-models-mathematics-and-simulations/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>in_NB books:noted philosophy_of_science simulation modeling re:phil-of-bayes_paper</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:455c316ef510/</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: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:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://press.uchicago.edu/ucp/books/book/chicago/Q/bo23530231.html">
    <title>Quantifying Life: A Symbiosis of Computation, Mathematics, and Biology, Kondrashov</title>
    <dc:date>2016-01-06T06:40:17+00:00</dc:date>
    <link>http://press.uchicago.edu/ucp/books/book/chicago/Q/bo23530231.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Since the time of Isaac Newton, physicists have used mathematics to describe the behavior of matter of all sizes, from subatomic particles to galaxies. In the past three decades, as advances in molecular biology have produced an avalanche of data, computational and mathematical techniques have also become necessary tools in the arsenal of biologists. But while quantitative approaches are now providing fundamental insights into biological systems, the college curriculum for biologists has not caught up, and most biology majors are never exposed to the computational and probabilistic mathematical approaches that dominate in biological research.
"With Quantifying Life, Dmitry A. Kondrashov offers an accessible introduction to the breadth of mathematical modeling used in biology today. Assuming only a foundation in high school mathematics, Quantifying Life takes an innovative computational approach to developing mathematical skills and intuition. Through lessons illustrated with copious examples, mathematical and programming exercises, literature discussion questions, and computational projects of various degrees of difficulty, students build and analyze models based on current research papers and learn to implement them in the R programming language. This interplay of mathematical ideas, systematically developed programming skills, and a broad selection of biological research topics makes Quantifying Life an invaluable guide for seasoned life scientists and the next generation of biologists alike."

--- Mineable for examples?]]></description>
<dc:subject>books:noted biology programming modeling to_teach:statcomp to_teach:complexity-and-inference</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:20c907173f0c/</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:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statcomp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:complexity-and-inference"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://link.springer.com/article/10.1007/s11229-014-0524-0?wt_mc=alerts.TOCjournals">
    <title>Models, robustness, and non-causal explanation: a foray into cognitive science and biology - Springer</title>
    <dc:date>2016-01-01T19:44:27+00:00</dc:date>
    <link>http://link.springer.com/article/10.1007/s11229-014-0524-0?wt_mc=alerts.TOCjournals</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper is aimed at identifying how a model’s explanatory power is constructed and identified, particularly in the practice of template-based modeling (Humphreys, Philos Sci 69:1–11, 2002; Extending ourselves: computational science, empiricism, and scientific method, 2004), and what kinds of explanations models constructed in this way can provide. In particular, this paper offers an account of non-causal structural explanation that forms an alternative to causal–mechanical accounts of model explanation that are currently popular in philosophy of biology and cognitive science. Clearly, defences of non-causal explanation are far from new (e.g. Batterman, Br J Philos Sci 53:21–38, 2002a; The devil in the details: asymptotic reasoning in explanation, reduction, and emergence, 2002b; Pincock, Noûs 41:253–275, 2007; Mathematics and scientific representation 2012; Rice, Noûs. doi:10.​1111/​nous.​12042, 2013; Biol Philos 27:685–703, 2012), so the targets here are focused on a particular type of robust phenomenon and how strong invariance to interventions can block a range of causal explanations. By focusing on a common form of model construction, the paper also ties functional or computational style explanations found in cognitive science and biology more firmly with explanatory practices across model-based science in general."]]></description>
<dc:subject>to:NB philosophy_of_science explanation explanation_by_mechanisms modeling</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:7b1f36c46249/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:explanation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:explanation_by_mechanisms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://link.springer.com/article/10.1007/s11229-014-0538-7?wt_mc=alerts.TOCjournals">
    <title>Scientific understanding: truth or dare? - Springer</title>
    <dc:date>2016-01-01T19:43:29+00:00</dc:date>
    <link>http://link.springer.com/article/10.1007/s11229-014-0538-7?wt_mc=alerts.TOCjournals</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["It is often claimed—especially by scientific realists—that science provides understanding of the world only if its theories are (at least approximately) true descriptions of reality, in its observable as well as unobservable aspects. This paper critically examines this ‘realist thesis’ concerning understanding. A crucial problem for the realist thesis is that (as study of the history and practice of science reveals) understanding is frequently obtained via theories and models that appear to be highly unrealistic or even completely fictional. So we face the dilemma of either giving up the realist thesis that understanding requires truth, or allowing for the possibility that in many if not all practical cases we do not have scientific understanding. I will argue that the first horn is preferable: the link between understanding and truth can be severed. This becomes a live option if we abandon the traditional view that scientific understanding is a special type of knowledge. While this view implies that understanding must be factive, I avoid this implication by identifying understanding with a skill rather than with knowledge. I will develop the idea that understanding phenomena consists in the ability to use a theory to generate predictions of the target system’s behavior. This implies that the crucial condition for understanding is not truth but intelligibility of the theory, where intelligibility is defined as the value that scientists attribute to the theoretical virtues that facilitate the construction of models of the phenomena. I will show, first, that my account accords with the way practicing scientists conceive of understanding, and second, that it allows for the use of idealized or fictional models and theories in achieving understanding."]]></description>
<dc:subject>to:NB philosophy_of_science explanation modeling</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ae8a26839796/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:explanation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://smr.sagepub.com/content/44/2/186.abstract?rss=1">
    <title>Agent-Based Models in Empirical Social Research</title>
    <dc:date>2015-05-20T02:10:39+00:00</dc:date>
    <link>http://smr.sagepub.com/content/44/2/186.abstract?rss=1</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Agent-based modeling has become increasingly popular in recent years, but there is still no codified set of recommendations or practices for how to use these models within a program of empirical research. This article provides ideas and practical guidelines drawn from sociology, biology, computer science, epidemiology, and statistics. We first discuss the motivations for using agent-based models in both basic science and policy-oriented social research. Next, we provide an overview of methods and strategies for incorporating data on behavior and populations into agent-based models, and review techniques for validating and testing the sensitivity of agent-based models. We close with suggested directions for future research."]]></description>
<dc:subject>agent-based_models modeling in_NB social_science_methodology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c03816893a87/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:agent-based_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_science_methodology"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://press.princeton.edu/titles/10291.html">
    <title>Shiflet, A.B. and Shiflet, G.W.: Introduction to Computational Science: Modeling and Simulation for the Sciences (Second Edition). (Hardcover)</title>
    <dc:date>2014-04-10T17:12:03+00:00</dc:date>
    <link>http://press.princeton.edu/titles/10291.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Computational science is an exciting new field at the intersection of the sciences, computer science, and mathematics because much scientific investigation now involves computing as well as theory and experiment. This textbook provides students with a versatile and accessible introduction to the subject. It assumes only a background in high school algebra, enables instructors to follow tailored pathways through the material, and is the only textbook of its kind designed specifically for an introductory course in the computational science and engineering curriculum. While the text itself is generic, an accompanying website offers tutorials and files in a variety of software packages.
"This fully updated and expanded edition features two new chapters on agent-based simulations and modeling with matrices, ten new project modules, and an additional module on diffusion. Besides increased treatment of high-performance computing and its applications, the book also includes additional quick review questions with answers, exercises, and individual and team projects."]]></description>
<dc:subject>to:NB books:noted to_teach:complexity-and-inference programming mathematics modeling</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:51808b715ad5/</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:to_teach:complexity-and-inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mathematics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.pnas.org/content/111/14/5076.abstract.html?etoc">
    <title>Mathematical approaches to modeling development and reprogramming</title>
    <dc:date>2014-04-09T13:49:03+00:00</dc:date>
    <link>http://www.pnas.org/content/111/14/5076.abstract.html?etoc</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Induced pluripotent stem cells (iPSCs) are created by the reprogramming of somatic cells via overexpression of certain transcription factors, such as the originally described Yamanaka factors: Oct4, Sox2, Klf4, and c-Myc (OSKM). Here we discuss recent advancements in iPSC reprogramming and introduce mathematical approaches to help map the landscape between cell states during reprogramming. Our modelization indicates that OSKM expression diminishes and/or changes potential barriers between cell states and that epigenetic remodeling facilitate these transitions. From a practical perspective, the modeling approaches outlined here allow us to predict the time necessary to create a given number of iPSC colonies or the number of reprogrammed cells generated in a given time. Additional investigations will help to further refine modeling strategies, rendering them applicable toward the study of the development and stability of cancer cells or even other reprogramming processes such as lineage conversion. Ultimately, a quantitative understanding of cell state transitions might facilitate the establishment of regenerative medicine strategies and enhance the translation of reprogramming technologies into the clinic."]]></description>
<dc:subject>molecular_biology developmental_biology modeling in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:39d8022a83f0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:molecular_biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:developmental_biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.ias.edu/about/publications/ias-letter/articles/2013-fall/economics-rodrik">
    <title>Economics: Science, Craft, or Snake Oil? | Institute for Advanced Study</title>
    <dc:date>2014-02-02T16:43:09+00:00</dc:date>
    <link>https://www.ias.edu/about/publications/ias-letter/articles/2013-fall/economics-rodrik</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>economics social_science_methodology rodrik.dani via:? modeling to:blog</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:38ba2b992de2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_science_methodology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:rodrik.dani"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:blog"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://ndpr.nd.edu/news/38637-simulation-and-similarity-using-models-to-understand-the-world/">
    <title>Simulation and Similarity: Using Models to Understand the World // Reviews // Notre Dame Philosophical Reviews // University of Notre Dame</title>
    <dc:date>2014-01-19T17:00:58+00:00</dc:date>
    <link>http://ndpr.nd.edu/news/38637-simulation-and-similarity-using-models-to-understand-the-world/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>to:NB books:noted book_reviews philosophy_of_science modeling simulation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:22e43e5e50d6/</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:book_reviews"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:simulation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://jliszka.github.io/2013/10/01/how-traffic-actually-works.html">
    <title>How traffic actually works</title>
    <dc:date>2013-10-09T18:44:24+00:00</dc:date>
    <link>http://jliszka.github.io/2013/10/01/how-traffic-actually-works.html</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>traffic via:? modeling</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6a54e2127669/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:traffic"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1306.0125">
    <title>[1306.0125] Understanding ACT-R - an Outsider's Perspective</title>
    <dc:date>2013-06-04T16:22:14+00:00</dc:date>
    <link>http://arxiv.org/abs/1306.0125</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The ACT-R theory of cognition developed by John Anderson and colleagues endeavors to explain how humans recall chunks of information and how they solve problems. ACT-R also serves as a theoretical basis for "cognitive tutors", i.e., automatic tutoring systems that help students learn mathematics, computer programming, and other subjects. The official ACT-R definition is distributed across a large body of literature spanning many articles and monographs, and hence it is difficult for an "outsider" to learn the most important aspects of the theory. This paper aims to provide a tutorial to the core components of the ACT-R theory."]]></description>
<dc:subject>to:NB cognitive_science modeling</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a4fcd8347547/</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:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://press.princeton.edu/titles/8624.html">
    <title>Bryant, J. and Sangwin, C.: How Round Is Your Circle? Where Engineering and Mathematics Meet.</title>
    <dc:date>2013-05-10T21:29:58+00:00</dc:date>
    <link>http://press.princeton.edu/titles/8624.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["How do you draw a straight line? How do you determine if a circle is really round? These may sound like simple or even trivial mathematical problems, but to an engineer the answers can mean the difference between success and failure. How Round Is Your Circle? invites readers to explore many of the same fundamental questions that working engineers deal with every day--it's challenging, hands-on, and fun.
"John Bryant and Chris Sangwin illustrate how physical models are created from abstract mathematical ones. Using elementary geometry and trigonometry, they guide readers through paper-and-pencil reconstructions of mathematical problems and show them how to construct actual physical models themselves--directions included. It's an effective and entertaining way to explain how applied mathematics and engineering work together to solve problems, everything from keeping a piston aligned in its cylinder to ensuring that automotive driveshafts rotate smoothly. Intriguingly, checking the roundness of a manufactured object is trickier than one might think. When does the width of a saw blade affect an engineer's calculations--or, for that matter, the width of a physical line? When does a measurement need to be exact and when will an approximation suffice? Bryant and Sangwin tackle questions like these and enliven their discussions with many fascinating highlights from engineering history. Generously illustrated, How Round Is Your Circle? reveals some of the hidden complexities in everyday things."]]></description>
<dc:subject>books:noted approximation geometry engineering modeling to_teach books:owned</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:9b2ded06b1c9/</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:approximation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:geometry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:owned"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.springer.com/education+%26+language/learning+%26+instruction/book/978-1-4614-1953-2?cm_mmc=NBA-_-Apr-13_WEST_12561625-_-product-_-978-1-4614-1953-2&amp;otherVersion=978-1-4614-1954-9">
    <title>Simulation and Learning - A Model-Centered Approach</title>
    <dc:date>2013-04-01T16:35:17+00:00</dc:date>
    <link>http://www.springer.com/education+%26+language/learning+%26+instruction/book/978-1-4614-1953-2?cm_mmc=NBA-_-Apr-13_WEST_12561625-_-product-_-978-1-4614-1953-2&amp;otherVersion=978-1-4614-1954-9</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This book conveys the incredible instructional potential of simulation as a modality of education and provides guidelines for the design of effective simulation-based learning environments.  The framework of the book consists of  model-centered learning---learning that requires a restructuring of individual mental models utilized by both students and teachers."

Simulation models extend our biological capacity to carry out simulative reasoning. Recent approaches to mental modeling, such as embodied cognition and the extended mind hypothesis are also considered in the book, which relies heavily on recent advances in cognitive science.

A conceptual model called the “epistemic simulation cycle” is proposed as a blueprint for the comprehension of the cognitive activities involved in simulation-based learning and for instructional design.]]></description>
<dc:subject>books:noted simulation education modeling cognitive_science in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2bbaa02b651b/</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:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:education"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://mathbabe.org/2013/03/17/modeling-in-plain-english/">
    <title>Modeling in Plain English | mathbabe</title>
    <dc:date>2013-03-20T01:43:07+00:00</dc:date>
    <link>http://mathbabe.org/2013/03/17/modeling-in-plain-english/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The result of this rule is that credit card companies must use simple models, probably decision trees, to make their rejection decisions.
"It’s a new way to think about modeling choice, to be sure. It doesn’t necessarily make for “better” decisions from the point of view of the credit card company: random forests, a generalization of decision trees, are known to be more accurate, but are arbitrarily more complicated to explain.
"So it matters what you’re optimizing for, and in this case the regulators have decided we’re optimizing for interpretability rather than accuracy. I think this is appropriate, given that consumers are at the mercy of these decisions and relatively powerless to act against them (although the FTC site above gives plenty of advice to people who have been rejected, mostly about how to raise their credit scores)."

- I'm not sure that this really does push towards something like decision trees (which after all include all sorts of interactions between variables) as opposed to random forests.]]></description>
<dc:subject>modeling credit_ratings to_teach:data-mining</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:477e282c0ab7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:credit_ratings"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.interfluidity.com/v2/4218.html">
    <title>interfluidity » K is not capital, L is not labor</title>
    <dc:date>2013-03-18T20:51:42+00:00</dc:date>
    <link>http://www.interfluidity.com/v2/4218.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[The fundamental point here is about the non-triviality of linking the theoretical variables in something like standard macroeconomic models to the quantities we actually measure.]]></description>
<dc:subject>economics economic_policy evisceration modeling chamley.christophe via:slaniel macroeconomics waldman.steven_randy</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2040be280318/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economic_policy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evisceration"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:chamley.christophe"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:slaniel"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:waldman.steven_randy"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://ftalphaville.ft.com/2013/01/17/1342082/a-tempest-in-a-spreadsheet/?">
    <title>A tempest in a spreadsheet | FT Alphaville</title>
    <dc:date>2013-01-17T22:13:43+00:00</dc:date>
    <link>http://ftalphaville.ft.com/2013/01/17/1342082/a-tempest-in-a-spreadsheet/?</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>utter_stupidity financial_markets modeling spreadsheets via:jbdelong to_teach:statcomp</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:53cb54e3083b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:utter_stupidity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:financial_markets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:spreadsheets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:jbdelong"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statcomp"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://press.princeton.edu/titles/9916.html">
    <title>Diekmann, O. and Heesterbeek, H., Britton, T.: Mathematical Tools for Understanding Infectious Disease Dynamics.</title>
    <dc:date>2012-12-21T04:50:19+00:00</dc:date>
    <link>http://press.princeton.edu/titles/9916.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Mathematical modeling is critical to our understanding of how infectious diseases spread at the individual and population levels. This book gives readers the necessary skills to correctly formulate and analyze mathematical models in infectious disease epidemiology, and is the first treatment of the subject to integrate deterministic and stochastic models and methods.
"Mathematical Tools for Understanding Infectious Disease Dynamics fully explains how to translate biological assumptions into mathematics to construct useful and consistent models, and how to use the biological interpretation and mathematical reasoning to analyze these models. It shows how to relate models to data through statistical inference, and how to gain important insights into infectious disease dynamics by translating mathematical results back to biology. This comprehensive and accessible book also features numerous detailed exercises throughout; full elaborations to all exercises are provided."]]></description>
<dc:subject>books:noted epidemic_models epidemiology modeling in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:7fa2d4359aee/</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:epidemic_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epidemiology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://mathbabe.org/2012/12/20/nate-silver-confuses-cause-and-effect-ends-up-defending-corruption/">
    <title>Nate Silver confuses cause and effect, ends up defending corruption « mathbabe</title>
    <dc:date>2012-12-20T14:43:28+00:00</dc:date>
    <link>http://mathbabe.org/2012/12/20/nate-silver-confuses-cause-and-effect-ends-up-defending-corruption/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[I see I am going to have to read this.  At the least, I'd like to see how he explains all the financial crises we had before (bad) financial models.]]></description>
<dc:subject>book_reviews silver.nate o'neil.cathy prediction modeling data_mining statistics the_objective_function_which_can_be_admitted_to_is_not_the_true_objective_function</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5604110f6a03/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:book_reviews"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:silver.nate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:o'neil.cathy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:the_objective_function_which_can_be_admitted_to_is_not_the_true_objective_function"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.jstor.org/action/showArticleInfo?doi=10.1086%2F667845">
    <title>Getting Serious about Similarity</title>
    <dc:date>2012-11-20T16:41:07+00:00</dc:date>
    <link>http://www.jstor.org/action/showArticleInfo?doi=10.1086%2F667845</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Although most philosophical accounts about model/world relations focus on structural mappings such as isomorphism, similarity has long been discussed as an alternative account. Despite its attractions, proponents of the similarity view have not provided detailed accounts of what it means that a model is similar to a real-world target system. This article gives the outlines of such an account, drawing on the work of Amos Tversky."]]></description>
<dc:subject>to:NB modeling philosophy_of_science analogy</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2062a714752e/</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:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:analogy"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.jstor.org/action/showArticleInfo?doi=10.1086%2F667846">
    <title>Forging Model/World Relations:Relevance and Reliability</title>
    <dc:date>2012-11-20T16:40:19+00:00</dc:date>
    <link>http://www.jstor.org/action/showArticleInfo?doi=10.1086%2F667846</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The relation between models and the world is mediated by experimental procedures generating data that are used as evidence to evaluate the model. Data can serve as empirical evidence, for or against, only if they result from reliable experimental procedures. The aim of this article is to discuss the role of relevance judgments in the evaluation of reliability and to clarify the conditions under which reliability can be a strictly empirical matter. It is argued that reliability is a strictly empirical issue only in the restricted case in which the claim under test/investigation is about a data-generating procedure."]]></description>
<dc:subject>to:NB philosophy_of_science modeling</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5e57b9bf3668/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1210.6278">
    <title>[1210.6278] Parameter space exploration of ecological models</title>
    <dc:date>2012-10-25T16:41:13+00:00</dc:date>
    <link>http://arxiv.org/abs/1210.6278</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In recent years, we are seeing the formulation and use of elaborate and complex models in ecological studies. The questions related to the efficient, systematic and error-proof exploration of parameter spaces are of great importance to better understand, estimate confidences and make use of the output from these models. In this work, we investigate some of the relevant questions related to parameter space exploration, in particular using the technique known as Latin Hypercube Sampling and focusing in quantitative output analysis. We present the analysis of a structured population growth model and contrast our findings with results from previously used techniques, known as sensitivity and elasticity analyses. We also assess how are the questions related to parameter space analysis being currently addressed in the ecological literature."]]></description>
<dc:subject>to:NB ecology modeling statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8f7c21145e14/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ecology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.cambridge.org/us/knowledge/isbn/item6793725/The%20World%20in%20the%20Model/?site_locale=en_US">
    <title>The World in the Model: How Economists Work and Think</title>
    <dc:date>2012-09-21T18:18:47+00:00</dc:date>
    <link>http://www.cambridge.org/us/knowledge/isbn/item6793725/The%20World%20in%20the%20Model/?site_locale=en_US</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["During the last two centuries, the way economic science is done has changed radically: it has become a social science based on mathematical models in place of words. This book describes and analyses that change – both historically and philosophically – using a series of case studies to illuminate the nature and the implications of these changes. In format, it offers a tourist guide to economics by focusing chapters on specific models, explaining how economists create them and how they reason with them. It is not a technical book; it is written for the intelligent person who wants to understand how economics works from the inside out. This book will be of interest to economists and science studies scholars (historians, sociologists and philosophers of science). But it also aims at a wider readership in the public intellectual sphere, building on the current interest in all things economic, and in the recent failure of the so-called economic model, which has shaped our beliefs and the world we live in."]]></description>
<dc:subject>books:noted economics modeling social_science_methodology philosophy_of_science in_NB books:owned</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c706e59ed123/</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:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_science_methodology"/>
	<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:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:owned"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.nap.edu/catalog.php?record_id=13395">
    <title>Assessing the Reliability of Complex Models: Mathematical and Statistical Foundations of Verification, Validation, and Uncertainty Quantification</title>
    <dc:date>2012-07-24T01:18:46+00:00</dc:date>
    <link>http://www.nap.edu/catalog.php?record_id=13395</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Advances in computing hardware and algorithms have dramatically improved the ability to simulate complex processes computationally. Today's simulation capabilities offer the prospect of addressing questions that in the past could be addressed only by resource-intensive experimentation, if at all. Assessing the Reliability of Complex Models recognizes the ubiquity of uncertainty in computational estimates of reality and the necessity for its quantification.
"As computational science and engineering have matured, the process of quantifying or bounding uncertainties in a computational estimate of a physical quality of interest has evolved into a small set of interdependent tasks: verification, validation, and uncertainty of quantification (VVUQ). In recognition of the increasing importance of computational simulation and the increasing need to assess uncertainties in computational results, the National Research Council was asked to study the mathematical foundations of VVUQ and to recommend steps that will ultimately lead to improved processes.
"Assessing the Reliability of Complex Models discusses changes in education of professionals and dissemination of information that should enhance the ability of future VVUQ practitioners to improve and properly apply VVUQ methodologies to difficult problems, enhance the ability of VVUQ customers to understand VVUQ results and use them to make informed decisions, and enhance the ability of all VVUQ stakeholders to communicate with each other. This report is an essential resource for all decision and policy makers in the field, students, stakeholders, UQ experts, and VVUQ educators and practitioners."]]></description>
<dc:subject>to:NB books:noted statistics modeling simulation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8d280a2f6826/</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:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:simulation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://press.princeton.edu/titles/9741.html">
    <title>Bonacich, P. and Lu, P.: Introduction to Mathematical Sociology.</title>
    <dc:date>2012-04-11T03:46:52+00:00</dc:date>
    <link>http://press.princeton.edu/titles/9741.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Judging from the table of contents (which is unfair), a weird mix of reviewing truly elementary concepts and some actually interesting stuff.  (And yes, I know who Bonacich is.)]]></description>
<dc:subject>to:NB books:noted sociology networks modeling network_data_analysis color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2eefeef7d418/</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:sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.jstor.org/discover/10.1086/664746?uid=3739864&amp;uid=2&amp;uid=4&amp;uid=3739256&amp;sid=55943703643">
    <title>JSTOR: Philosophy of Science, Vol. 79, No. 2 (April 2012), pp. 207-232</title>
    <dc:date>2012-03-30T20:50:19+00:00</dc:date>
    <link>http://www.jstor.org/discover/10.1086/664746?uid=3739864&amp;uid=2&amp;uid=4&amp;uid=3739256&amp;sid=55943703643</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["It is proposed that we use the term “approximation” for inexact description of a target system and “idealization” for another system whose properties also provide an inexact description of the target system. Since systems generated by a limiting process can often have quite unexpected—even inconsistent—properties, familiar limit processes used in statistical physics can fail to provide idealizations but merely provide approximations."]]></description>
<dc:subject>to:NB modeling philosophy_of_science approximation norton.john_d.</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:37f9b210a09d/</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:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:approximation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:norton.john_d."/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://krugman.blogs.nytimes.com/2012/03/27/minksy-and-methodology-wonkish/">
    <title>Minksy and Methodology (Wonkish) - NYTimes.com</title>
    <dc:date>2012-03-27T17:28:12+00:00</dc:date>
    <link>http://krugman.blogs.nytimes.com/2012/03/27/minksy-and-methodology-wonkish/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>social_science_methodology krugman.paul economics modeling</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1a8e64db32ba/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_science_methodology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:krugman.paul"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://philsci-archive.pitt.edu/8885/">
    <title>Models as make-believe - PhilSci-Archive</title>
    <dc:date>2011-11-11T14:44:10+00:00</dc:date>
    <link>http://philsci-archive.pitt.edu/8885/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this paper I propose an account of representation for scientific models based on Kendall Walton’s ‘make-believe’ theory of representation in art. I first set out the problem of scientific representation and respond to a recent argument due to Craig Callender and Jonathan Cohen, which aims to show that the problem may be easily dismissed. I then introduce my account of models as props in games of make-believe and show how it offers a solution to the problem. Finally, I demonstrate an important advantage my account has over other theories of scientific representation. All existing theories analyse scientific representation in terms of relations, such as similarity or denotation. By contrast, my account does not take representation in modelling to be essentially relational. For this reason, it can accommodate a group of models often ignored in discussions of scientific representation, namely models which are representational but which represent no actual object."  --- Isn't this just "the philosophy of 'as-if' " from around 1900?]]></description>
<dc:subject>to:NB philosophy_of_science modeling</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:177bb75565c2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.springerlink.com/content/xk495k21p747k685/">
    <title>Science without (parametric) models: the case of bootstrap resampling: SpringerLink - Synthese, Volume 180, Number 1</title>
    <dc:date>2011-10-28T02:02:23+00:00</dc:date>
    <link>http://www.springerlink.com/content/xk495k21p747k685/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Scientific and statistical inferences build heavily on explicit, parametric models, and often with good reasons. However, the limited scope of parametric models and the increasing complexity of the studied systems in modern science raise the risk of model misspecification. Therefore, I examine alternative, data-based inference techniques, such as bootstrap resampling. I argue that their neglect in the philosophical literature is unjustified: they suit some contexts of inquiry much better and use a more direct approach to scientific inference. Moreover, they make more parsimonious assumptions and often replace theoretical understanding and knowledge about mechanisms by careful experimental design. Thus, it is worthwhile to study in detail how nonparametric models serve as inferential engines in science."]]></description>
<dc:subject>philosophy_of_science bootstrap statistics modeling nonparametrics in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:599db7a292ee/</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:bootstrap"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:nonparametrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://rochester.edu/college/psc/clarke/POPArticle.pdf">
    <title>Modernizing Political Science: A Model-Based Approach</title>
    <dc:date>2011-10-18T19:52:04+00:00</dc:date>
    <link>http://rochester.edu/college/psc/clarke/POPArticle.pdf</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>to:NB to_read political_science philosophy_of_science modeling re:phil-of-bayes_paper clarke.kevin primo.david</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:43873bec44a9/</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:political_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:clarke.kevin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:primo.david"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.powells.com/biblio?isbn=978-0195382204">
    <title>A Model Discipline: Political Science and the Logic of Representations by - Powell's Books</title>
    <dc:date>2011-10-18T17:53:23+00:00</dc:date>
    <link>http://www.powells.com/biblio?isbn=978-0195382204</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>to:NB books:noted philosophy_of_science political_science re:phil-of-bayes_paper clarke.kevin primo.david via:scotte modeling</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d20641798167/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:political_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:clarke.kevin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:primo.david"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:scotte"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://philsci-archive.pitt.edu/8717/">
    <title>The Productive:Tension: Mechanisms vs. Templates in Modeling the Phenomena - PhilSci-Archive</title>
    <dc:date>2011-07-15T23:38:36+00:00</dc:date>
    <link>http://philsci-archive.pitt.edu/8717/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We argue that there is a tension present in the modeling practice between the aim of capturing the specific mechanisms underlying the phenomena and the use of general cross-disciplinary computational templates to study them. To illuminate this tension we examine the Lotka-Volterra model, which has provided a powerful template for population biology and other areas of research. We will compare the respective approaches of Alfred Lotka and Vito Volterra. What makes this comparison especially interesting is that although they ended up presenting models that from the formal point of view looked identical – and were subsequently treated like that – they nevertheless followed different kinds of modeling strategies."
]]></description>
<dc:subject>philosophy_of_science modeling explanation_by_mechanisms lotka-volterra</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a030a3a3cbba/</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:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:explanation_by_mechanisms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:lotka-volterra"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://philsci-archive.pitt.edu/8720/">
    <title>Making Sense of Modeling: Beyond Representation - PhilSci-Archive</title>
    <dc:date>2011-07-15T23:38:05+00:00</dc:date>
    <link>http://philsci-archive.pitt.edu/8720/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>philosophy_of_science modeling to:NB</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:fb4b9493a9d1/</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:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://philsci-archive.pitt.edu/8622/">
    <title>Approximation and Idealization: Why the Difference Matters - PhilSci-Archive</title>
    <dc:date>2011-05-26T19:50:18+00:00</dc:date>
    <link>http://philsci-archive.pitt.edu/8622/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>philosophy_of_science approximation idealization modeling to:NB</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d295f1c35683/</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:approximation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:idealization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
</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://www.pnas.org/content/107/43/18243.abstract?etoc">
    <title>Mental models and human reasoning — PNAS</title>
    <dc:date>2010-11-16T17:34:49+00:00</dc:date>
    <link>http://www.pnas.org/content/107/43/18243.abstract?etoc</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>cognitive_science mental_models modeling to:NB johnson-laird.philip</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e1eda103150d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mental_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:johnson-laird.philip"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://philsci-archive.pitt.edu/8386/">
    <title>Representing with Physical Models - PhilSci-Archive</title>
    <dc:date>2010-11-11T12:00:27+00:00</dc:date>
    <link>http://philsci-archive.pitt.edu/8386/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>modeling simulation philosophy_of_science representation to:NB giere.ronald</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8d5f312ebb77/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<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:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:giere.ronald"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.modelingepistemology.pitt.edu/">
    <title>Home | Epistemology of Modeling &amp; Simulation: Conference, Pittsburgh, 1--3 April 2011</title>
    <dc:date>2010-11-09T15:42:23+00:00</dc:date>
    <link>http://www.modelingepistemology.pitt.edu/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[I should probably submit something, shouldn't I?
]]></description>
<dc:subject>conferences simulation philosophy_of_science modeling methodology</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f92c7d7cd479/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:conferences"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:methodology"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.people.fas.harvard.edu/~pgs/PGS-StrategyMBS-06.pdf">
    <title>The strategy of model-based science (Godfrey-Smith, 2006)</title>
    <dc:date>2010-11-08T01:20:32+00:00</dc:date>
    <link>http://www.people.fas.harvard.edu/~pgs/PGS-StrategyMBS-06.pdf</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>to_read modeling philosophy_of_science re:phil-of-bayes_paper godfrey-smith.peter</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:196ee032c04f/</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:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:godfrey-smith.peter"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.princeton.edu/~pkrugman/aag.pdf">
    <title>&quot;THE NEW ECONOMIC GEOGRAPHY, NOW MIDDLE-AGED&quot;</title>
    <dc:date>2010-04-16T12:30:17+00:00</dc:date>
    <link>http://www.princeton.edu/~pkrugman/aag.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Krugman looks back on his _Geography and Trade_ after 20 years, before an audience of actual geographers.  With how-I-model reflections.
]]></description>
<dc:subject>economics economic_geography geography increasing_returns imperfect_competition modeling krugman.paul economic_history</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:08db939de100/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economic_geography"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:geography"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:increasing_returns"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:imperfect_competition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:krugman.paul"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economic_history"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://philsci-archive.pitt.edu/archive/00005218/">
    <title>PhilSci Archive - Scientific Models as Information Carrying Artifacts</title>
    <dc:date>2010-03-30T13:43:19+00:00</dc:date>
    <link>http://philsci-archive.pitt.edu/archive/00005218/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We present an information theoretic account of models as scientific representations, where scientific models are understood as information carrying artifacts. We propose that the semantics of models should be based on this information coupling of the model to the world. The information theoretic account presents a way of avoiding the need to refer to agents' intentions as constitutive of the semantics of scientific representations, and it provides a naturalistic account of model semantics, which can deal with the problems of asymmetry, relevance and circularity that afflict other currently popular naturalistic proposals."
]]></description>
<dc:subject>philosophy_of_science information_theory semantics modeling</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e9e904ec4cf5/</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:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:semantics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>