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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="https://arxiv.org/abs/2605.21535"/>
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	<rdf:li rdf:resource="https://blog.arxiv.org/2026/10/01/updated-rate-limit-policy/"/>
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	<rdf:li rdf:resource="https://arxiv.org/abs/2609.38107"/>
	<rdf:li rdf:resource="https://buttondown.com/apperceptive/archive/tracing-the-rogue-ideology-of-the-frontier-labs/"/>
	<rdf:li rdf:resource="https://www.programmablemutter.com/p/what-ai-debates-have-to-do-with-alchemy"/>
	<rdf:li rdf:resource="https://sourceforge.net/projects/djvu/"/>
	<rdf:li rdf:resource="https://www.cambridge.org/core/books/tragedy-of-soviet-market-economists/4931D444A881184CE31E83E1397EBB44#fndtn-information"/>
	<rdf:li rdf:resource="https://github.com/szhan/CSSR-R"/>
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	<rdf:li rdf:resource="https://ieeexplore.ieee.org/document/4767596"/>
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	<rdf:li rdf:resource="https://direct-mit-edu.cmu.idm.oclc.org/books/oa-monograph/6166/SimPoliticsAmerica-s-Quest-to-Solve-Politics-with"/>
	<rdf:li rdf:resource="https://direct-mit-edu.cmu.idm.oclc.org/books/oa-edited-volume/6161/Towards-a-Biosemiotic-Theoretical-BiologySign"/>
	<rdf:li rdf:resource="https://www.stat.berkeley.edu/~aldous/Papers/weak-gtp.pdf"/>
	<rdf:li rdf:resource="https://terrytao.wordpress.com/2026/09/13/happy-those-able-to-know-the-causes-of-things/"/>
	<rdf:li rdf:resource="https://eehh-stanford.github.io/monkeys_uncle/posts/culture_wars/"/>
	<rdf:li rdf:resource="https://www.nytimes.com/2026/09/13/business/guidepost-montessori-higher-ground-education-ray-girn.html"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2007.07623"/>
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	<rdf:li rdf:resource="https://arxiv.org/abs/2608.13567"/>
	<rdf:li rdf:resource="https://aiguide.substack.com/p/misleading-metaphors-and-real-risks"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2607.01693"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2608.27310"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2603.20973"/>
	<rdf:li rdf:resource="https://people.kernel.org/monsieuricon/creepy-crawlies"/>
	<rdf:li rdf:resource="https://www.newyorker.com/magazine/2026/08/03/food-justice-undone-hanna-garth-book-review"/>
	<rdf:li rdf:resource="https://www.nature.com/articles/s41586-026-10098-2"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1604.02603"/>
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	<rdf:li rdf:resource="https://arxiv.org/abs/1010.3913"/>
	<rdf:li rdf:resource="https://thesphinxblog.com/2026/08/11/take-a-chance-on-me/"/>
	<rdf:li rdf:resource="https://www.science.org/doi/10.1126/science.aef8874"/>
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	<rdf:li rdf:resource="https://www.nature.com/articles/s41586-026-10953-2"/>
	<rdf:li rdf:resource="https://www.science.org/doi/full/10.1126/sciadv.adz6502"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2607.17397"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2604.16653"/>
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	<rdf:li rdf:resource="https://arxiv.org/abs/2607.06145"/>
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	<rdf:li rdf:resource="https://arxiv.org/abs/2607.25139"/>
	<rdf:li rdf:resource="https://journals.aps.org/prx/abstract/10.1103/tmkr-9kl2"/>
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	<rdf:li rdf:resource="https://www.quantamagazine.org/is-ai-reasoning-right-for-the-wrong-reasons-20260731/"/>
	<rdf:li rdf:resource="https://en.wikipedia.org/wiki/Congressional_Airport"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2605.09294"/>
	<rdf:li rdf:resource="https://sociologicalscience.com/articles-v13-31-802/"/>
	<rdf:li rdf:resource="https://link.springer.com/article/10.1007/s13171-026-00452-x"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2608.01326"/>
	<rdf:li rdf:resource="https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/subjective-selection-superattractors-and-the-origins-of-the-cultural-manifold/2D71AA44CFDB34F171A3D2E2F4410173"/>
	<rdf:li rdf:resource="https://iai.tv/articles/puzzles-reveal-the-limits-of-ai-auid-3638"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2411.10939"/>
	<rdf:li rdf:resource="https://www.nature.com/articles/s41593-025-02031-z"/>
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  </channel><item rdf:about="https://proceedings.mlr.press/v108/weber20a.html">
    <title>Neighborhood Growth Determines Geometric Priors for Relational Representation Learning</title>
    <dc:date>2026-10-09T19:09:55+00:00</dc:date>
    <link>https://proceedings.mlr.press/v108/weber20a.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The problem of identifying geometric structure in heterogeneous, high-dimensional data is a cornerstone of representation learning. While there exists a large body of literature on the embeddability of canonical graphs, such as lattices or trees, the heterogeneity of the relational data typically encountered in practice limits the applicability of these classical methods. In this paper, we propose a combinatorial approach to evaluating embeddability, i.e., to decide whether a data set is best represented in Euclidean, Hyperbolic or Spherical space. Our method analyzes nearest-neighbor structures and local neighborhood growth rates to identify the geometric priors of suitable embedding spaces. For canonical graphs, the algorithm’s prediction provably matches classical results. As for large, heterogeneous graphs, we introduce an efficiently computable statistic that approximates the algorithm’s decision rule. We validate our method over a range of benchmark data sets and compare with recently published optimization-based embeddability methods."]]></description>
<dc:subject>to:NB network_data_analysis relational_learning graph_theory hyperbolic_geometry re:hyperbolic_networks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8091b246f5dc/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:relational_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graph_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hyperbolic_geometry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:hyperbolic_networks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2605.21535">
    <title>[2605.21535] An Old Look at Empirical Bayes</title>
    <dc:date>2026-10-06T16:41:32+00:00</dc:date>
    <link>https://arxiv.org/abs/2605.21535</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Dennis Lindley once said that there is only one thing worse than a frequentist, and that is an empirical Bayesian. The quip has the air of caricature, but its technical content is serious: empirical Bayes uses the same data twice, conflates levels of a hierarchy, and produces posterior-shaped summaries whose uncertainty quantification differs from what a fully hierarchical model delivers. David Blei's 2026 IMS Medallion Lecture, "A Fresh Look at Empirical Bayes," revives the program under three new banners: empirical Bayes via probabilistic symmetries (rebranded "Bayesian empirical Bayes"), empirical Bayes with implicit likelihoods through simulation-based inference, and empirical Bayes for combining experimental and observational data through calibration studies. This is a continuation of Blei and Kucukelbir's earlier "population empirical Bayes" (PopEB, 2015). We argue, in the spirit of Lindley, I. J. Good, William DuMouchel, Thomas Louis, and our own recent work with Datta, that Blei's machinery targets inferential objects distinct from the posterior conditional on the realized data, and that the cost of maintaining the full hierarchical discipline has fallen low enough that the computational trade-off no longer favors the shortcut. The case study is the Tweedie formula. Efron's f-modeling empirical Bayes plugs an estimated score function into a posterior-mean identity, but a smoothed score need not arise from any prior. The horseshoe Tweedie formula does. We conclude by recommending that the impressive computational machinery of modern empirical Bayes (variational inference, neural amortization, simulation-based inference) be redeployed in service of properly hierarchical Bayes."

--- I am irrationally and disproportionately amused by the idea that David (of all people) has such dedicated haters.]]></description>
<dc:subject>to:NB bayesianism empirical_bayes blei.david polson.nicholas_g.</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6545cafc791a/</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:bayesianism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:empirical_bayes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:blei.david"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:polson.nicholas_g."/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2607.21843">
    <title>[2607.21843] Simulation-Based Empirical Bayes</title>
    <dc:date>2026-10-06T16:38:58+00:00</dc:date>
    <link>https://arxiv.org/abs/2607.21843</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Empirical Bayes (EB) performs simultaneous inference across many related latent variables. Classical EB assumes that the likelihood p(x | z) is tractable. In many scientific applications, however, the likelihood is available only through a simulator. This paper develops EB for such implicit likelihoods. We introduce simulation-based empirical Bayes (SBEB), which connects nonparametric EB to simulation-based inference (SBI). SBEB computes EB estimates without an explicit density by using the observed data, simulator samples, and an amortized inference network. SBEB iteratively refines the fitted EB prior toward the population prior. With several scientific simulators and real-world data, we demonstrate that SBEB improves accuracy over SBI with a fixed prior."]]></description>
<dc:subject>to:NB simulation-based_inference blei.david missing_the_talk to_read empirical_bayes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4b2cab7d1366/</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-based_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:blei.david"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:missing_the_talk"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:empirical_bayes"/>
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</item>
<item rdf:about="https://blog.arxiv.org/2026/10/01/updated-rate-limit-policy/">
    <title>arXiv has updated its rate limit policy for all submitters.</title>
    <dc:date>2026-10-06T16:24:28+00:00</dc:date>
    <link>https://blog.arxiv.org/2026/10/01/updated-rate-limit-policy/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>large_language_models_(so_called) arxiv the_web_we_have_lost</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b3510b90e663/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
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</item>
<item rdf:about="https://www.nature.com/articles/s41586-026-11036-y">
    <title>Scalable decision-making for games of imperfect information | Nature</title>
    <dc:date>2026-10-06T16:19:28+00:00</dc:date>
    <link>https://www.nature.com/articles/s41586-026-11036-y</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Real-world decision-making generally involves hidden information, that is, information that is unknown to one agent but possessed by another. Unfortunately, the presence of large amounts of hidden information renders established reinforcement learning and search approaches ineffective. Even with multimillion-dollar industrial research efforts1, top-human-level play at Stratego—a board wargame with hidden information on a massive scale—has remained beyond the reach of artificial intelligence (AI). Here we introduce Ataraxos, an AI for Stratego based on general techniques that we developed for both self-play reinforcement learning and test-time search under hidden information. Ataraxos defeated the most decorated human Stratego player of all time by a large margin—achieving, to our knowledge, the first superhuman result in the game’s history—while consuming orders of magnitude less compute and data than previous efforts. Using the same techniques, we built a superhuman AI for Barrage Stratego and state-of-the-art AIs for Hanabi and dou dizhu, all with low cost and high sample efficiency. The success of this approach across adversarial, cooperative and team games establishes a design pattern for reinforcement learning and search that is effective under large amounts of hidden information, a longstanding desideratum of the field of strategic decision-making."]]></description>
<dc:subject>to:NB to_read learning_in_games reinforcement_learning artificial_intelligence kolter.zico</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:fb4d5c5b1c3b/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:reinforcement_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:artificial_intelligence"/>
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</item>
<item rdf:about="https://arxiv.org/abs/2609.38107">
    <title>[2609.38107] Correct Answers, Invalid Traces: What Verifiable Grade-School Math Reveals About Chain-of-Thought Traces</title>
    <dc:date>2026-10-06T16:17:36+00:00</dc:date>
    <link>https://arxiv.org/abs/2609.38107</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Chain-of-thought traces are widely read as records of how models reach their answers, informing debugging, agent auditing, and claims about reasoning. Testing this interpretation is difficult because natural-language thinking traces are rarely mechanically verifiable. We revisit it in iGSM, a synthetic grade-school mathematics benchmark designed to study thinking traces and used to support claims of learned reasoning and planning. Crucially, iGSM exposes the exact quantities and dependencies that a correct solution should use, allowing generated traces to be checked programmatically step by step and enabling us to test whether correct answers are reliably accompanied by valid traces. We first evaluate models trained exclusively on valid, minimal traces. Answer correctness and trace validity nearly coincide in distribution but decouple out of distribution: on the hardest instances, 31.6% of correct answers have invalid traces, over half of which pass all syntactic and arithmetic checks but fail semantic dependency checks. We then intervene on trace supervision. Non-minimal training traces induce non-minimal outputs, while re-asking the same problem with a different query reveals computations inherited from the original query, weakening minimality as evidence of selective planning. Shuffling tokens in 10% of training trace sentences preserves near-clean accuracy even out of distribution despite no trace passing verification. Swapped training traces likewise retain high in-distribution accuracy. We discuss the implications of these findings for chain-of-thought monitoring and interpretation in the context of AI safety."]]></description>
<dc:subject>to:NB to_read large_language_models_(so_called) chain-of-thought_(so_called)</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1238f97fc661/</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:large_language_models_(so_called)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:chain-of-thought_(so_called)"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://buttondown.com/apperceptive/archive/tracing-the-rogue-ideology-of-the-frontier-labs/">
    <title>Tracing the rogue ideology of the frontier labs to their product choices • Buttondown</title>
    <dc:date>2026-10-06T15:53:54+00:00</dc:date>
    <link>https://buttondown.com/apperceptive/archive/tracing-the-rogue-ideology-of-the-frontier-labs/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- Strongly recommended.]]></description>
<dc:subject>artificial_intelligence large_language_models_(so_called) have_read to:NB kith_and_kin</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:91f75a7bbd51/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:artificial_intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.programmablemutter.com/p/what-ai-debates-have-to-do-with-alchemy">
    <title>What AI debates have to do with alchemy - by Henry Farrell</title>
    <dc:date>2026-10-06T14:40:45+00:00</dc:date>
    <link>https://www.programmablemutter.com/p/what-ai-debates-have-to-do-with-alchemy</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>have_read large_language_models_(so_called) artificial_intelligence farrell.henry kith_and_kin alchemy</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:eff7b87d8640/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:artificial_intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:farrell.henry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:alchemy"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://sourceforge.net/projects/djvu/">
    <title>DjVuLibre download | SourceForge.net</title>
    <dc:date>2026-09-28T14:02:41+00:00</dc:date>
    <link>https://sourceforge.net/projects/djvu/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["DjVu is a web-centric format for distributing documents and images. DjVu was created at AT&T Labs-Research and later sold to LizardTech Inc. DjVuLibre is a GPL implementation of DjVu maintained by the original inventors of DjVu."

--- And thank you, document archaeology, for making it necessary for me to find this.]]></description>
<dc:subject>djvu</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:aa2e6d1ca18a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:djvu"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/core/books/tragedy-of-soviet-market-economists/4931D444A881184CE31E83E1397EBB44#fndtn-information">
    <title>The Tragedy of Soviet Market Economists</title>
    <dc:date>2026-09-24T13:50:49+00:00</dc:date>
    <link>https://www.cambridge.org/core/books/tragedy-of-soviet-market-economists/4931D444A881184CE31E83E1397EBB44#fndtn-information</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this path-breaking history, Tobias Rupprecht offers a revisionist account of Russia's post-Soviet marketisation from the perspective of the advisors and ministers who oversaw this transformation. Based on extensive interviews with economists and research in state and private archives, he uncovers a significant minority of economic liberals from late Soviet academic and dissident circles who sought to chart a new path, believing free prices and private property were the foundations of a 'civilised country'. This provides a vital challenge to the dominant narrative that neoliberal advisors and organisations imposed harmful reforms on Russia after the collapse of Communism. Liberal reformers faced a profound dilemma – one for which Western advisors had no solution either: should they commit to democratic political activism and risk irrelevance, or align themselves with those in power and be co-opted by an authoritarian state determined to reassert its imperial strength?"]]></description>
<dc:subject>to:NB books:noted downloaded post-soviet_politics USSR re:in_soviet_union_optimization_problem_solves_you</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e7f95407983c/</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:post-soviet_politics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:USSR"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:in_soviet_union_optimization_problem_solves_you"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/szhan/CSSR-R">
    <title>GitHub - szhan/CSSR-R: R interface to CSSR program, authored by https://github.com/stites/CSSR · GitHub</title>
    <dc:date>2026-09-24T13:41:23+00:00</dc:date>
    <link>https://github.com/szhan/CSSR-R</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- I had no idea!  And, it seems to work!]]></description>
<dc:subject>to:blog self-centered to_teach:data_over_space_and_time to_teach:complexity-and-inference markov_models inference_to_latent_objects</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6a5c9031c772/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:blog"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:self-centered"/>
	<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:to_teach:complexity-and-inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:markov_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:inference_to_latent_objects"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/core/books/geography-of-power/3C28A6B96DC0A558C41D4BA86C6E7F41#fndtn-information">
    <title>The Geography of Power</title>
    <dc:date>2026-09-23T17:53:42+00:00</dc:date>
    <link>https://www.cambridge.org/core/books/geography-of-power/3C28A6B96DC0A558C41D4BA86C6E7F41#fndtn-information</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Why are most contemporary autocracies concentrated between Siberia and Central Africa, while other regions remain largely democratic? This book uncovers the deep historical forces behind that divide, tracing how geography – particularly the vast steppe grasslands – and political–economic conflicts between nomadic and sedentary societies shaped enduring patterns of power. These structured conflicts reinforced authoritarian persistence across half the globe, creating a binary world with starkly different opportunities and threats. The result is a long-standing geopolitical fault line that continues to shape global politics today, exemplified by the autocratic axis of China, Russia, Iran, and North Korea. Combining insights from geography, history, and political economy, this book offers a compelling explanation of why authoritarianism thrives – and why democracy prevails elsewhere."]]></description>
<dc:subject>to:NB books:noted downloaded comparative_history central_asia color_me_skeptical re:huns_and_bolsheviks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ede7b4b06b64/</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:comparative_history"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_asia"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:huns_and_bolsheviks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://ieeexplore.ieee.org/document/4767596">
    <title>Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images | IEEE Journals &amp; Magazine | IEEE Xplore</title>
    <dc:date>2026-09-23T15:45:39+00:00</dc:date>
    <link>https://ieeexplore.ieee.org/document/4767596</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We make an analogy between images and statistical mechanics systems. Pixel gray levels and the presence and orientation of edges are viewed as states of atoms or molecules in a lattice-like physical system. The assignment of an energy function in the physical system determines its Gibbs distribution. Because of the Gibbs distribution, Markov random field (MRF) equivalence, this assignment also determines an MRF image model. The energy function is a more convenient and natural mechanism for embodying picture attributes than are the local characteristics of the MRF. For a range of degradation mechanisms, including blurring, nonlinear deformations, and multiplicative or additive noise, the posterior distribution is an MRF with a structure akin to the image model. By the analogy, the posterior distribution defines another (imaginary) physical system. Gradual temperature reduction in the physical system isolates low energy states (``annealing''), or what is the same thing, the most probable states under the Gibbs distribution. The analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations. The result is a highly parallel ``relaxation'' algorithm for MAP estimation. We establish convergence properties of the algorithm and we experiment with some simple pictures, for which good restorations are obtained at low signal-to-noise ratios."]]></description>
<dc:subject>to:NB have_read geman.stuart random_fields spatial_statistics simulated_annealing statistical_mechanics statistical_inference_for_stochastic_processes monte_carlo to_teach:data_over_space_and_time</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:07cb66739350/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:geman.stuart"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:random_fields"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:spatial_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:simulated_annealing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_mechanics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_inference_for_stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:monte_carlo"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.jstor.org/stable/2345426?searchText=ti%3A%28%22on+the+statistical+analysis+of+dirty+pictures%22%29&amp;searchUri=%2Faction%2FdoBasicSearch%3FQuery%3Dti%253A%2528%2522on%2Bthe%2Bstatistical%2Banalysis%2Bof%2Bdirty%2Bpictures%2522%2529%26so%3Drel&amp;ab_segments=0%2Fbasic_phrase_search%2Fcontrol&amp;refreqid=fastly-default%3A8efa537998468b0f004656389abbba94&amp;seq=1">
    <title>On the Statistical Analysis of Dirty Pictures | JSTOR</title>
    <dc:date>2026-09-23T15:43:51+00:00</dc:date>
    <link>https://www.jstor.org/stable/2345426?searchText=ti%3A%28%22on+the+statistical+analysis+of+dirty+pictures%22%29&amp;searchUri=%2Faction%2FdoBasicSearch%3FQuery%3Dti%253A%2528%2522on%2Bthe%2Bstatistical%2Banalysis%2Bof%2Bdirty%2Bpictures%2522%2529%26so%3Drel&amp;ab_segments=0%2Fbasic_phrase_search%2Fcontrol&amp;refreqid=fastly-default%3A8efa537998468b0f004656389abbba94&amp;seq=1</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>to:NB have_read random_fields spatial_statistics besag.julian statistical_inference_for_stochastic_processes inference_to_latent_objects to_teach:data_over_space_and_time</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a38a48739f94/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:random_fields"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:spatial_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:besag.julian"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_inference_for_stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:inference_to_latent_objects"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://projecteuclid.org/journals/annals-of-applied-probability/volume-5/issue-3/Hidden-Markov-Random-Fields/10.1214/aoap/1177004696.full">
    <title>Hidden Markov Random Fields</title>
    <dc:date>2026-09-23T15:27:10+00:00</dc:date>
    <link>https://projecteuclid.org/journals/annals-of-applied-probability/volume-5/issue-3/Hidden-Markov-Random-Fields/10.1214/aoap/1177004696.full</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A noninvertible function of a first-order Markov process or of a nearest-neighbor Markov random field is called a hidden Markov model. Hidden Markov models are generally not Markovian. In fact, they may have complex and long range interactions, which is largely the reason for their utility. Applications include signal and image processing, speech recognition and biological modeling. We show that hidden Markov models are dense among essentially all finite-state discrete-time stationary processes and finite-state lattice-based stationary random fields. This leads to a nearly universal parameterization of stationary processes and stationary random fields, and to a consistent nonparametric estimator. We show the results of attempts to fit simple speech and texture patterns."

--- Somehow missed this one in my education...]]></description>
<dc:subject>to:NB random_fields markov_models state-space_models statistical_inference_for_stochastic_processes have_skimmed to_teach:data_over_space_and_time</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e6db4184e1bb/</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:random_fields"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:markov_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:state-space_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_inference_for_stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_skimmed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/core/journals/perspectives-on-politics/article/symbolic-politics-of-status-in-the-maga-movement/A22AC624B4D1FF7367D9912F23875F4B">
    <title>The Symbolic Politics of Status in the MAGA Movement | Perspectives on Politics | Cambridge Core</title>
    <dc:date>2026-09-23T14:37:57+00:00</dc:date>
    <link>https://www.cambridge.org/core/journals/perspectives-on-politics/article/symbolic-politics-of-status-in-the-maga-movement/A22AC624B4D1FF7367D9912F23875F4B</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Drawing on ethnographic fieldwork among Make America Great Again (MAGA) activists during the 2020 presidential campaign, we explore the status dynamics behind the appeal of Donald Trump’s right-wing populism. While existing explanations emphasize partisanship, economic anxiety, racial resentment, rural identity, and media polarization, we underscore a less-explored explanation for Trump’s core support: it is a status-based social movement. We find that Trump’s activists are not simply voters responding to policy preferences or culture-war appeals but are also participants in a grassroots social movement organized around a shared perception of lost honor, declining esteem, and institutional disrespect. To make this argument we use the concept of the symbolic politics of status to explain how political conflict extends beyond contests over material distribution or moral values to include battles over whose values and lifestyles are considered worthy. For MAGA activists, reclaiming lost status means seeking public affirmation for identities they feel have been unfairly denigrated. The MAGA movement blends grievance with joy, cultivating pride, belonging, and celebration alongside anger at elites. By centering status in our analysis, we offer an integrative framework that connects material, cultural, and emotional motivations into a broader account of MAGA as a right-wing social movement grounded in grassroots populism."]]></description>
<dc:subject>to:NB ethnography us_politics running_dogs_of_reaction political_science social_movements sociology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e15809eeca50/</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:ethnography"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:us_politics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:running_dogs_of_reaction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:political_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_movements"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sociology"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2609.13009">
    <title>[2609.13009] How Good Are Frontier Models at Physics? Expert Re-Grading Reveals Broken Evaluations and Near-Saturation of Leading Benchmarks</title>
    <dc:date>2026-09-18T04:03:17+00:00</dc:date>
    <link>https://arxiv.org/abs/2609.13009</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Low reported scores on leading physics benchmarks, including those featured in the Artificial Analysis Intelligence Index (2026), suggest that frontier language models still struggle with advanced physics, a demanding test of their scientific reasoning and quantitative problem-solving abilities. Yet this impression does not always align with domain experts' experiences using these models in their work. We revisit these reported findings by evaluating frontier models on six widely used physics benchmarks and auditing them with experts, focusing on text-only problems with verifiable final answers. For each subfield of physics, faculty and graduate researchers with relevant expertise carefully review problem statements, reference solutions, and model responses to distinguish genuine model errors from grader errors, incorrect reference solutions, and ambiguous or underspecified questions. Most audited cases initially evaluated as incorrect reflect these benchmarking issues rather than errors in the models' physics reasoning. We then ask experts to address these benchmarking issues by correcting erroneous reference solutions and repairing or excluding flawed questions. We find that GPT-5.6-Sol's measured mean@4 rises from 47.3% to 78.7% on HLE-Physics and from 61.0% to 87.2% on CMT-Benchmark, while its corrected pass@4 reaches 94.4% on the 54 retained CritPt challenges. Corrected scores are computed on the retained evaluation subsets following expert review. Scores on the audited subsets of UGPhysics, PRISM-Physics, and PHYBench also rise substantially after correction. These findings suggest that current benchmarks substantially understate frontier models' ability to solve well-posed physics problems. Near-saturation on these closed-ended tasks highlights the need for more demanding, expert-validated evaluations."]]></description>
<dc:subject>to:NB large_language_models_(so_called)</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:adef5be6feb4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://cran.r-project.org/web/packages/dfms/index.html">
    <title>CRAN: Package dfms</title>
    <dc:date>2026-09-18T03:08:19+00:00</dc:date>
    <link>https://cran.r-project.org/web/packages/dfms/index.html</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>factor_analysis state-space_models R to_teach:data_over_space_and_time have_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:9bd79eb814cb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:factor_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:state-space_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<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:have_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://direct-mit-edu.cmu.idm.oclc.org/books/oa-monograph/6152/Reasoning-with-ConceptsConceptual-Spaces-as-a">
    <title>Reasoning with Concepts: Conceptual Spaces as a Framework | Books Gateway | MIT Press</title>
    <dc:date>2026-09-17T03:01:37+00:00</dc:date>
    <link>https://direct-mit-edu.cmu.idm.oclc.org/books/oa-monograph/6152/Reasoning-with-ConceptsConceptual-Spaces-as-a</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Psychologists and philosophers have worked on topics such as category-based induction, nonmonotonic reasoning, analogies, and generics, but these problems have largely been investigated independently. In Reasoning with Concepts, Peter Gärdenfors and Matías Osta-Vélez bring them all together by presenting models built on the theory of conceptual spaces. This theory offers a rich framework for modeling many aspects of the structure of concepts. In particular, it allows the definition of measures for similarity, typicality, diagnosticity, and coherence of concepts, notions long employed informally by psychologists and philosophers.
"While probabilistic models exist for some of these notions, no comprehensive formal framework has previously encompassed them all. The proposed measures here, based on distances in conceptual space and prototypes, generate novel testable predictions while unifying previously disparate theoretical territories. Furthermore, the models can be implemented in artificial systems that deal with different forms of reasoning."]]></description>
<dc:subject>to:NB books:noted downloaded cognitive_science philosophy_of_mind</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f9a9934f3263/</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:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_mind"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://direct-mit-edu.cmu.idm.oclc.org/books/oa-monograph/6171/Inventing-ELIZAHow-the-First-Chatbot-Shaped-the">
    <title>Inventing ELIZA: How the First Chatbot Shaped the Future of AI | Books Gateway | MIT Press</title>
    <dc:date>2026-09-17T02:56:48+00:00</dc:date>
    <link>https://direct-mit-edu.cmu.idm.oclc.org/books/oa-monograph/6171/Inventing-ELIZAHow-the-First-Chatbot-Shaped-the</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["As we reach the 60th anniversary of ELIZA’s public debut, Inventing ELIZA offers the first comprehensive critical analysis of Joseph Weizenbaum’s groundbreaking chatbot system through the lens of critical code studies. Drawing on extensive archival research at MIT, Stanford, and UCLA, this book presents the rediscovered original source code of ELIZA alongside previously unseen scripts that had been missing for decades, revealing a far more sophisticated system than previously documented. Sarah Ciston, David Berry, Anthony Hay, Mark Marino, Peter Millican, Arthur Schwarz, Jeff Shrager, and Peggy Weil trace ELIZA’s development (1965–1968), revealing that Weizenbaum created a chatbot within a conversational programming environment with previously unknown innovations well ahead of its time. Through close reading of both code and paratexts, the book reconstructs ELIZA’s conceptual evolution and situates it within the historical context of early AI development."]]></description>
<dc:subject>to:NB books:noted downloaded history_of_technology artificial_intelligence</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ab15d42df90a/</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:history_of_technology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:artificial_intelligence"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://direct-mit-edu.cmu.idm.oclc.org/books/oa-monograph/6166/SimPoliticsAmerica-s-Quest-to-Solve-Politics-with">
    <title>SimPolitics: America’s Quest to Solve Politics with Computers | Books Gateway | MIT Press</title>
    <dc:date>2026-09-17T02:55:01+00:00</dc:date>
    <link>https://direct-mit-edu.cmu.idm.oclc.org/books/oa-monograph/6166/SimPoliticsAmerica-s-Quest-to-Solve-Politics-with</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["For more than six decades, the public has been promised that computers will revolutionize politics, both nationally and internationally. In SimPolitics, Fenwick McKelvey traces the entwined history of politics and computers from the 1960s to the late 1980s. He shows how programmers, consultants, academics, political scientists, and peace activists all worked—sometimes in tandem, sometimes not—to build simulations to win campaigns, predict coups, forecast the future, and render politics as legible as a spreadsheet.
"Drawing on novel archival and historical research, McKelvey recounts the history of efforts to simulate politics by building models of elections, voters, and international relations. Comparing attempts in the United States to simulate domestic electoral politics and international affairs, he reveals the unexamined connections and conflicts between the two projects. His book provides a helpful guide to taking stock of exaggerated claims that AI and technology will fix politics, while presenting the long history of such promised technological fixes."]]></description>
<dc:subject>to:NB books:noted history_of_science history_of_ideas simulation us_politics political_science</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a5710e09674d/</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:history_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_ideas"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:us_politics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:political_science"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://direct-mit-edu.cmu.idm.oclc.org/books/oa-edited-volume/6161/Towards-a-Biosemiotic-Theoretical-BiologySign">
    <title>Towards a Biosemiotic Theoretical Biology: Sign Processes and Meaning-Making in Living Systems | Books Gateway | MIT Press</title>
    <dc:date>2026-09-17T02:53:32+00:00</dc:date>
    <link>https://direct-mit-edu.cmu.idm.oclc.org/books/oa-edited-volume/6161/Towards-a-Biosemiotic-Theoretical-BiologySign</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In the tradition of the field-changing four-volume essay collection Towards a Theoretical Biology issued by developmental biologist Conrad Hal Waddington from 1968 to 1972, this volume brings together many of today’s leading scientists to discuss what they consider to be the most important and pressing problems in our current understandings of the biological world—and how best to advance our understandings of such life processes scientifically.
"Contributors: Denis Noble, Terrance Deacon, Scott F. Gilbert, Stuart Kaufmann, Tom Froese, Erik L. Peterson, Richard I Vane-Wright, Charles Wolfe, Raymond Noble, Claus Emmeche, Alexei Sharov, Kalevi Kull, Donald Favareau, Arantza Etxeberria, Anton Markoš, Jana Švorcová, Daniel C. Mayer-Foulkes, Federico Vega, Henrik Nielsen, Karel Kleisner, David Cortés-García, Matt Kalkman, Georgii Karelin, Takashi Ikegami, and Mariana Vitti Rodrigues."]]></description>
<dc:subject>to:NB books:noted downloaded biology semiotics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4155eb0e9424/</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:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:semiotics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.stat.berkeley.edu/~aldous/Papers/weak-gtp.pdf">
    <title>Weak Convergence and the General Theory of Processes (Aldous, 1981)</title>
    <dc:date>2026-09-16T01:33:13+00:00</dc:date>
    <link>https://www.stat.berkeley.edu/~aldous/Papers/weak-gtp.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- Why had I never heard of this?]]></description>
<dc:subject>probability stochastic_processes convergence_of_stochastic_processes prediction aldous.david via:mw-s in_NB books:noted downloaded</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:79bdea7bc8d7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:convergence_of_stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:aldous.david"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:mw-s"/>
	<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:downloaded"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://terrytao.wordpress.com/2026/09/13/happy-those-able-to-know-the-causes-of-things/">
    <title>Happy, those able to know the causes of things | What's new</title>
    <dc:date>2026-09-15T14:31:32+00:00</dc:date>
    <link>https://terrytao.wordpress.com/2026/09/13/happy-those-able-to-know-the-causes-of-things/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- The notion of filling in the convex hull of existing knowledge / approaches is a very nice one.]]></description>
<dc:subject>mathematics large_language_models_(so_called) re:gopnikism have_read via:henry_farrell to:NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a00d8a676e2f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mathematics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:gopnikism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:henry_farrell"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://eehh-stanford.github.io/monkeys_uncle/posts/culture_wars/">
    <title>The Misunderstanding of the Anthropology Culture Wars – Monkey’s Uncle</title>
    <dc:date>2026-09-14T14:02:06+00:00</dc:date>
    <link>https://eehh-stanford.github.io/monkeys_uncle/posts/culture_wars/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>anthropology social_networks us_culture_wars academia via:? have_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5cd5b28627a4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:anthropology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:us_culture_wars"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:academia"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.nytimes.com/2026/09/13/business/guidepost-montessori-higher-ground-education-ray-girn.html">
    <title>Inside the $440 Million Collapse of Guidepost Montessori - The New York Times</title>
    <dc:date>2026-09-14T02:12:14+00:00</dc:date>
    <link>https://www.nytimes.com/2026/09/13/business/guidepost-montessori-higher-ground-education-ray-girn.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- This is hilarious and incredible.
--- Objectivism + Montessori is... not a connection I would have made, but in retrospect I guess I can see how it'd work.  (One teacher at one of my Montessori schools assigned me to read Ayn Rand, but that was the year he assigned me a whole series of books he knew I'd hate, to force me to argue with them rather than merely reject.)
--- I got an incredible douse of Proustian nostalgia from the photo of the bead chains for forming squares and cubes. [https://static01.nyt.com/images/2026/09/13/multimedia/13biz-sun-preschool-ponzi-04-lcmt/13biz-sun-preschool-ponzi-04-lcmt-superJumbo.jpg?quality=75&auto=webp]
--- The bit about "nasal voice" was unnecessary and only weakens the satirical effect.
]]></description>
<dc:subject>education montessori business_disasters have_read via:aeo</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b90ae8ee0a67/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:education"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:montessori"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:business_disasters"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:aeo"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2007.07623">
    <title>[2007.07623] Stationarity and ergodic properties for some observation-driven models in random environments</title>
    <dc:date>2026-09-13T01:40:36+00:00</dc:date>
    <link>https://arxiv.org/abs/2007.07623</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The first motivation of this paper is to study stationarity and ergodic properties for a general class of time series models defined conditional on an exogenous covariates process. The dynamic of these models is given by an autoregressive latent process which forms a Markov chain in random environments. Contrarily to existing contributions in the field of Markov chains in random environments, the state space is not discrete and we do not use small set type assumptions or uniform contraction conditions for the random Markov kernels. Our assumptions are quite general and allows to deal with models that are not fully contractive, such as threshold autoregressive processes. Using a coupling approach, we study the existence of a limit, in Wasserstein metric, for the backward iterations of the chain. We also derive ergodic properties for the corresponding skew-product Markov chain. Our results are illustrated with many examples of autoregressive processes widely used in statistics or in econometrics, including GARCH type processes, count autoregressions and categorical time series."]]></description>
<dc:subject>to:NB markov_models ergodic_theory chains_with_complete_connections doukhan.paul via:mw-s</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c2dd565fb596/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:markov_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ergodic_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:chains_with_complete_connections"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:doukhan.paul"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:mw-s"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6116366">
    <title>Recurrent Neural Networks for Nonlinear Time Series  by Xiao Chen, Yu Chen, Zhouyu Shen, Dacheng Xiu :: SSRN</title>
    <dc:date>2026-09-13T01:35:55+00:00</dc:date>
    <link>https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6116366</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Bridging classical time-series econometrics with modern machine-learning tools, we establish theoretical guarantees for recurrent neural networks trained on time series generated by nonlinear vector autoregressive moving-average models with exogenous variables. We derive upper bounds on predictive risk that decompose into approximation and estimation errors. Approximation error depends on smoothness and effective dimension, while estimation error depends on architecture; both vanish as network complexity grows with sample size. Under an invertibility condition, recurrence yields parsimonious representations of temporal dependence and faster convergence than nonparametric regressions based on high-order autoregressive truncations."

--- Can't help remarking that I knew people who were doing this literally 30 years ago...]]></description>
<dc:subject>to:NB neural_networks time_series learning_theory via:mw-s</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:190143e1cad2/</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:neural_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:mw-s"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2608.13567">
    <title>[2608.13567] Modular Cognitive Architecture Emerges in Large Language Models</title>
    <dc:date>2026-09-12T15:07:12+00:00</dc:date>
    <link>https://arxiv.org/abs/2608.13567</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The human brain exhibits a striking degree of functional specialization, with distinct networks supporting language, formal reasoning, reasoning about other minds, and reasoning about the physical world. Is this modular organization a fundamental principle of how intelligent systems must be built, or an evolutionary accident specific to biological brains? Here, we test whether a similar organization emerges in Large Language Models--another class of intelligent systems created through a very different optimization process. Using circuit analyses across N=46 tasks spanning four cognitive domains (language, formal reasoning, social reasoning, physical reasoning), we find that LLMs develop a modular architecture that mirrors the human brain: tasks drawing on the same network in humans recruit overlapping neurons in LLMs, whereas tasks drawing on different networks recruit distinct neurons. The convergent emergence of modularity in brains and neural networks suggests that it may be a fundamental property of intelligent systems."]]></description>
<dc:subject>to:NB large_language_models_(so_called) neuroscience functional_connectivity cognitive_science color_me_skeptical prophesying_upon_the_eigenvalues neural_networks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:80a7627004b4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neuroscience"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:functional_connectivity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prophesying_upon_the_eigenvalues"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neural_networks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://aiguide.substack.com/p/misleading-metaphors-and-real-risks">
    <title>Misleading Metaphors, Real Risks - by Melanie Mitchell</title>
    <dc:date>2026-09-11T02:59:01+00:00</dc:date>
    <link>https://aiguide.substack.com/p/misleading-metaphors-and-real-risks</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>have_read kith_and_kin mitchell.melanie large_language_models_(so_called) rhetoric to:NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:04af858a6e8e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mitchell.melanie"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:rhetoric"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2607.01693">
    <title>[2607.01693] A Mathematical Introduction to Diffusion Models</title>
    <dc:date>2026-09-04T03:43:37+00:00</dc:date>
    <link>https://arxiv.org/abs/2607.01693</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["These notes give a proof-oriented introduction to diffusion models from the viewpoint of sampling, tracing a single arc from classical sampling dynamics to modern diffusion samplers, their error analysis, and inference-time control. Throughout, the material is layered into core definitions and identities proved in full, representative estimates proved under simplifying assumptions, and research-level theorems stated with a proof roadmap. The intended audience is beginning graduate students with a background in probability but no prior exposure to stochastic differential equations, stochastic numerics, or diffusion models."]]></description>
<dc:subject>to:NB generative_diffusion_models to_teach:statistics_and_generative_ai via:rvenkat</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:afb1bde3daf4/</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:generative_diffusion_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_and_generative_ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:rvenkat"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2608.27310">
    <title>[2608.27310] Conformal Prediction Through the Lens of Hypothesis Testing: Universality, Impossibility, and Optimality</title>
    <dc:date>2026-09-02T13:51:22+00:00</dc:date>
    <link>https://arxiv.org/abs/2608.27310</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The connections between conformal prediction and permutation tests are already widely-known in the literature. Some authors motivate conformal prediction by saying that it computes a permutation p-value for the hypothesis H0:Yn+1=y, and then inverts this to form a prediction set for Yn+1 (i.e., accepts all values y into the prediction set for which the p-value is large). In this paper, we examine an alternative view, which is less well-known: we again cast conformal prediction via the inversion of a permutation test, but for the null of exchangeability of the joint distribution of the n+1 samples. This change in perspective, while simple, adheres more closely to traditional formalization in hypothesis testing, which offers several benefits. First, we use the duality between conformal sets and testing to show that foundational universality and impossibility results in the conformal prediction literature can be reproduced directly using classical hypothesis testing theory (due to Neyman, Lehmann, Scheffé, Kraft, Le Cam, and others). Furthermore, we show that an optimality result for conformal prediction can be derived using standard Neyman-Pearson theory: for any joint distribution of the covariates and response X,Y, and any sample size, the optimal method for prediction sets---which delivers the most efficient set among all methods with valid coverage for exchangeable distributions---is a conformal predictor whose score is the inverse conditional density of Y|X."]]></description>
<dc:subject>to:NB to_read conformal_prediction hypothesis_testing confidence_sets kith_and_kin tibshirani.ryan ramdas.aaditya foygel_barber.rina</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1964e1562f3a/</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:conformal_prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hypothesis_testing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:confidence_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:tibshirani.ryan"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ramdas.aaditya"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:foygel_barber.rina"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2603.20973">
    <title>[2603.20973] Scaling laws in empirical networks</title>
    <dc:date>2026-09-01T14:28:39+00:00</dc:date>
    <link>https://arxiv.org/abs/2603.20973</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["How does the shape of a network change as its size increases? Although random graph models provide some expectations for such "scaling behaviors" in the structure of networks, relatively little is known about how empirical network structure scales with network size or how well random graphs explain those empirical patterns. Using a large, structurally diverse corpus of networks from four scientific domains, we first characterize the empirical scaling laws of real-world networks, considering how mean degree, transitivity, mean geodesic distance, and degree assortativity vary with network size. We show that networks from all four scientific domains exhibit a consistent set of scaling laws on these measures of network structure, but with differing scaling rates. We then assess the extent to which these empirical scaling laws are explained by three random graph models with different structural assumptions, showing that configuration model random graphs are a remarkably good model of network scaling behavior, although null models with modular structure are slightly better. These findings identify a new set of common patterns in the network structure of complex systems, provide new validation targets for models of network structure, and shed new light on the role of randomness in shaping the large-scale structure of networks."]]></description>
<dc:subject>to:NB to_read network_data_analysis kith_and_kin clauset.aaron via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1e6de0b81a86/</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:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:clauset.aaron"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://people.kernel.org/monsieuricon/creepy-crawlies">
    <title>Creepy crawlies — Konstantin Ryabitsev</title>
    <dc:date>2026-09-01T14:27:22+00:00</dc:date>
    <link>https://people.kernel.org/monsieuricon/creepy-crawlies</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>via:rvenkat networked_life large_language_models_(so_called)</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:007a5664c989/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:rvenkat"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networked_life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.newyorker.com/magazine/2026/08/03/food-justice-undone-hanna-garth-book-review">
    <title>The Myth of the Food Desert | The New Yorker</title>
    <dc:date>2026-08-24T17:10:53+00:00</dc:date>
    <link>https://www.newyorker.com/magazine/2026/08/03/food-justice-undone-hanna-garth-book-review</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>debunking food_deserts have_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ce434ce86a2e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:debunking"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:food_deserts"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.nature.com/articles/s41586-026-10098-2">
    <title>The political effects of X’s feed algorithm | Nature</title>
    <dc:date>2026-08-19T17:28:40+00:00</dc:date>
    <link>https://www.nature.com/articles/s41586-026-10098-2</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Feed algorithms are widely suspected to influence political attitudes. However, previous evidence from switching off the algorithm on Meta platforms found no political effects1. Here we present results from a 2023 field experiment on Elon Musk’s platform X shedding light on this puzzle. We assigned active US-based users randomly to either an algorithmic or a chronological feed for 7 weeks, measuring political attitudes and online behaviour. Switching from a chronological to an algorithmic feed increased engagement and shifted political opinion towards more conservative positions, particularly regarding policy priorities, perceptions of criminal investigations into Donald Trump and views on the war in Ukraine. In contrast, switching from the algorithmic to the chronological feed had no comparable effects. Neither switching the algorithm on nor switching it off significantly affected affective polarization or self-reported partisanship. To investigate the mechanism, we analysed users’ feed content and behaviour. We found that the algorithm promotes conservative content and demotes posts by traditional media. Exposure to algorithmic content leads users to follow conservative political activist accounts, which they continue to follow even after switching off the algorithm, helping explain the asymmetry in effects. These results suggest that initial exposure to X’s algorithm has persistent effects on users’ current political attitudes and account-following behaviour, even in the absence of a detectable effect on partisanship."]]></description>
<dc:subject>to:NB social_media experimental_sociology twitter recommender_systems to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f9b130f0670c/</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:social_media"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:experimental_sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:twitter"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:recommender_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1604.02603">
    <title>[1604.02603] Information, Processes and Games</title>
    <dc:date>2026-08-19T17:27:14+00:00</dc:date>
    <link>https://arxiv.org/abs/1604.02603</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We survey the prospects for an Information Dynamics which can serve as the basis for a fundamental theory of information, incorporating qualitative and structural as well as quantitative aspects. We motivate our discussion with some basic conceptual puzzles: how can information increase in computation, and what is it that we are actually computing in general? Then we survey a number of the theories which have been developed within Computer Science, as partial exemplifications of the kind of fundamental theory which we seek: including Domain Theory, Dynamic Logic, and Process Algebra. We look at recent work showing new ways of combining quantitative and qualitative theories of information, as embodied respectively by Domain Theory and Shannon Information Theory. Then we look at Game Semantics and Geometry of Interaction, as examples of dynamic models of logic and computation in which information flow and interaction are made central and explicit. We conclude by looking briefly at some key issues for future progress."

--- Can't tell from the abstract how seriously to take this.]]></description>
<dc:subject>to:NB game_theory information_theory via:? barely-comprehensible_metaphysics logic</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:dd15a952b368/</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:game_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:barely-comprehensible_metaphysics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:logic"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2608.13433">
    <title>[2608.13433] Algebraic Decomposition Theory for Transformer Length Generalization</title>
    <dc:date>2026-08-16T01:40:31+00:00</dc:date>
    <link>https://arxiv.org/abs/2608.13433</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Transformer-based language models are known to sometimes generalize to sequences longer than seen during training, but we lack a precise characterization of which tasks admit length generalization. It is not even known which regular languages transformers length-generalize on -- and this is a foundational class of languages. Our contributions are to establish the first complete characterization of which regular languages transformers length-generalize on and provide a decision algorithm running in polynomial time in the size of the language's syntactic monoid. These results rely on an effective characterization of the regular languages in C-RASP, a recently-established formalism that expresses which languages transformers length-generalize on. This characterization is challenging because classical tools like Krohn-Rhodes decomposition theory for finite semigroups are insufficient for C-RASP. Firstly, the basic building blocks of Krohn-Rhodes theory -- flip-flop and simple groups -- are not expressible in C-RASP. Secondly, the basic building block of C-RASP (unbounded counting) is not expressible by the finite semigroups of Krohn-Rhodes theory. Thus, length generalization on regular languages is controlled by an algebraic property that is invisible to classical finite decomposition theory. We generalize classical decomposition theory from finite semigroups to the infinite additive group on the integers, allowing us to characterize C-RASP in terms of iterated wreath products of the integers and derive a provable polynomial-time decision algorithm for regular language membership. Experiments across a broad test suite of regular languages confirm that our theory captures transformers' length-generalization behavior more accurately than existing classifications."]]></description>
<dc:subject>to:NB automata_theory algebra large_language_models_(so_called) via:rvenkat</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3f427dc4e596/</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:automata_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:algebra"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:rvenkat"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7269378">
    <title>&lt;p&gt;Swan Song: My Understanding of Democracy&lt;/p&gt; by Adam Przeworski :: SSRN</title>
    <dc:date>2026-08-15T14:06:46+00:00</dc:date>
    <link>https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7269378</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["I summarize my understanding of democracy: the theory which predicts when elections peacefully manage conflicts and the empirical evidence regarding the conditions under which it succeeds or fails. The current situation in the US is an anomaly in the light of both theory and evidence, and I speculate about its implications."]]></description>
<dc:subject>to:NB to_read przeworski.adam democracy political_science the_continuing_crises via:henry_farrell</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a7dc8661943b/</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:przeworski.adam"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:democracy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:political_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:the_continuing_crises"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:henry_farrell"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1010.3913">
    <title>[1010.3913] One more discussion of the replica trick: the examples of exact solutions</title>
    <dc:date>2026-08-14T15:24:01+00:00</dc:date>
    <link>https://arxiv.org/abs/1010.3913</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A systematic replica field theory calculations are analysed using the examples of two particular one-dimensional "toy" random models with Gaussian disorder. Due to apparent simplicity of the model the replica trick calculations can be followed here step by step from the very beginning till the very end. In this way it can be easily demonstrated that formally at certain stage of the calculations the implementation of the standard replica program is just impossible. On the other hand, following the usual "doublethink" traditions of the replica calculations (i.e. closing eyes on the fact that certain suggestions used in the calculations contradict to each other) one can easily fulfil the programme till the very end to obtain physically sensible result for the entire free energy distribution function."]]></description>
<dc:subject>to:NB spin_glasses statistical_mechanics replica_method</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b933d45427b9/</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:spin_glasses"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_mechanics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:replica_method"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://thesphinxblog.com/2026/08/11/take-a-chance-on-me/">
    <title>Take A Chance On Me | Sphinx</title>
    <dc:date>2026-08-13T16:21:26+00:00</dc:date>
    <link>https://thesphinxblog.com/2026/08/11/take-a-chance-on-me/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>scholarship tychetext have_read via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0b9023bbe51c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:scholarship"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:tychetext"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.science.org/doi/10.1126/science.aef8874">
    <title>Recovering signatures of archaic hominin introgression using ancestral recombination graphs | Science</title>
    <dc:date>2026-08-11T15:32:23+00:00</dc:date>
    <link>https://www.science.org/doi/10.1126/science.aef8874</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Admixture between modern humans and extinct hominins has shaped the genomes of present-day individuals, but reconstructing this history has been constrained by the scarcity of archaic samples and unadmixed outgroup populations. We introduce TRACE, a reference- and outgroup-free approach that uses features of ancestral recombination graphs to identify archaic ancestry. Simulations show TRACE has high precision and low false discovery rates. Applied to 1000 Genomes, TRACE recovers known Neanderthal and Denisovan introgression and uncovers ghost admixture from uncharacterized hominins in both Africans and non-Africans. Ghost ancestry persists in Neanderthal and Denisovan ancestry deserts, challenging their interpretation as Homo sapiens–specific regions. In Oceanians, TRACE finds deep lineages are enriched in Denisovan compared to Neanderthal regions, supporting super-archaic introgression. TRACE enables mapping archaic introgression without archaic genomes."

--- Maybe I'm being excessively skeptical, but if your new method proposes _two_ new lineages which are previously totally unknown and we have no evidence for, you really need super convincing proof in the reliability of the method, no?]]></description>
<dc:subject>to:NB historical_genetics human_evolution</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:9e7b6d281105/</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:historical_genetics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:human_evolution"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://en.wikipedia.org/wiki/Sofic_group">
    <title>Sofic group - Wikipedia</title>
    <dc:date>2026-08-11T15:28:58+00:00</dc:date>
    <link>https://en.wikipedia.org/wiki/Sofic_group</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- I'll be honest, I find the definition here incomprehensible, and I used to understand sofic shifts pretty well.]]></description>
<dc:subject>algebra</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c644022254be/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:algebra"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://dl.acm.org/doi/10.1145/359131.359136">
    <title>Algorithm = logic + control | Communications of the ACM</title>
    <dc:date>2026-08-11T15:06:12+00:00</dc:date>
    <link>https://dl.acm.org/doi/10.1145/359131.359136</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The notion that computation = controlled deduction was first proposed by Pay Hayes [19] and more recently by Bibel [2] and Vaughn-Pratt [31]. A similar thesis that database systems should be regarded as consisting of a relational component, which defines the logic of the data, and a control component, which stores and retrieves it, has been successfully argued by Codd [10]. Hewitt's argument [20] for the programming language PLANNER, though generally regarded as an argument against logic, can also be regarded as an argument for the thesis that algorithms be regarded as consisting of both logic and control components. In this paper we shall explore some of the useful consequences of that thesis."]]></description>
<dc:subject>in_NB computation algorithms re:thought_experiments via:? have_read logic</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:292ce720202f/</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:computation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:thought_experiments"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:logic"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://sociologicalscience.com/articles-v13-21-528/">
    <title>How a Seemingly Innocuous and Intuitive Methodological Choice Confused a Generation of Research on Policy Responsiveness</title>
    <dc:date>2026-08-11T13:46:47+00:00</dc:date>
    <link>https://sociologicalscience.com/articles-v13-21-528/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The finding that government policy is, “virtually unrelated to the desires of the low- and middle-income citizens” (Gilens 2005:789), is one of the most influential social science results of the last two decades. This article offers a new perspective on this finding. I show that the seemingly innocuous decision to restrict analyses to data where different income groups’ policy support differs (i.e., a preference gap exists) introduced Simpson’s paradox, leading to misleading conclusions about whose preferences policy reflects. The same concerns apply to analyses of responsiveness to men and women and to partisan groups. I also present evidence that other common approaches for evaluating policy responsiveness can produce equally misleading conclusions. These findings suggest a need to reconsider conventional wisdom about political influence. The conclusion offers methodological recommendations and discusses implications related to understanding social and economic inequality and support for populist candidates."

--- Should really write up that module about inequality in political participation & responsiveness.

--- ETA after reading: the Simpson's Paradox stuff is convincing, but the mind-blowing thing is the correlation in policy preferences between 10th and 90th percentiles is over 0.9!  As he says, this is just _asking_ for trouble with collinearity.
One could do a nonparametric regression here, with policy support at 10th and 90th percentiles as the two regressors, and because it'd only be 2D, the curse of dimensionality wouldn't really apply.  One would not need to explicitly encode a dummy for whether a policy was more supported by the affluent than the poor or not.  But with that much correlation between the two regressors, one would only be estimating the regression function in a narrow strip of the unit square (basically).
--- Might be worth making into an assignment for logistic regression in a regression class, or ADA? (Hence last tags.)]]></description>
<dc:subject>to:NB inequality political_economy to_teach:statistics_of_inequality_and_discrimination have_read social_science_methodology to_teach:linear_models to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:392c3f36ff71/</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:inequality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:political_economy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_of_inequality_and_discrimination"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_science_methodology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:linear_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://sociologicalscience.com/articles-v13-24-614/">
    <title>Declining Inequality and Persistent Inequality Structures</title>
    <dc:date>2026-08-11T13:45:45+00:00</dc:date>
    <link>https://sociologicalscience.com/articles-v13-24-614/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Prior research finds that rising labor market inequality in the United States was abetted by structural changes in the economy: a consolidation of occupation and organizational bases of advantage; rising within-job inequality; and declining pay and employment in middle-earning jobs. In this article, we revisit these structural changes by asking whether they have been reversed as labor market inequality fell over the last decade. Drawing on restricted-use microdata from the Occupational Employment and Wages Statistics, we find that declining inequality is due to declining inequality in occupation premiums. There has been only a small reversal of consolidation and no decrease in inequality within jobs. Low-wage jobs gained on shrinking middle-earning occupations, further eroding union, manufacturing, and public sector wage premiums. These findings demonstrate a novel configuration of labor market inequality, in which pay rose in low-wage jobs, but underlying inequality structures in the economy persisted."]]></description>
<dc:subject>to:NB to_teach:statistics_of_inequality_and_discrimination class_struggles_in_america inequality</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:271312f4fc4b/</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_teach:statistics_of_inequality_and_discrimination"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:class_struggles_in_america"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:inequality"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://sociologicalscience.com/articles-v13-32-825/">
    <title>A Roadmap for Inequality Research: Transparency, Intersectionality, and Multiple Measures of Race</title>
    <dc:date>2026-08-11T13:44:34+00:00</dc:date>
    <link>https://sociologicalscience.com/articles-v13-32-825/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Most quantitative studies of U.S. inequality rely on single measures of race and do not transparently describe them. However, inconsistencies between measures can yield conclusions that differ both substantively and statistically. We ask: when faced with multiple ways to categorize respondents, how should researchers choose? We conduct intersectional analyses of five inequality outcomes, using the 1979 National Longitudinal Survey of Youth, which offers several measures of self-identification and external classification. Strikingly, we find the survey’s screener race variable, ubiquitous in prior research, is never empirically preferred based on model fit across outcomes spanning the labor market (wages, salary, and unemployment), health (depression), and education (school discipline). Instead, the top-performing measure varies by gender, outcome, and fit statistic. The range of potential researcher decisions and the absence of a clear gold-standard highlights the need for greater transparency and more thoughtful decision-making when researchers operationalize race—whether racial categorization is central to the analysis or included primarily as a control variable. To that end, we offer a roadmap of key considerations inequality researchers can consult when designing their approach."

--- I'm not sure (without reading it) that "which measured feature gives the best prediction?" is really the right way to address "which is the better measurement of the underlying variable?"  But the basic point about caution and clarity being warranted when getting discordant results from different measures of (purportedly) the same thing is sound.]]></description>
<dc:subject>to:NB social_measurement race to_teach:statistics_of_inequality_and_discrimination</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:acbb92569074/</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:social_measurement"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:race"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_of_inequality_and_discrimination"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://sociologicalscience.com/articles-v13-33-864/">
    <title>Information Diets Are More Diverse in Attention Than in Engagement</title>
    <dc:date>2026-08-11T13:40:28+00:00</dc:date>
    <link>https://sociologicalscience.com/articles-v13-33-864/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["What political content do we pay attention to online? Diverse political information is essential for democratic competence, yet online media raises concerns about fragmented information diets. Research on selective exposure highlights how social media can foster ideological echo chambers, while other studies emphasize incidental exposure to diverse viewpoints. A critical measurement challenge is that public platform traces often observe engagement (e.g., likes or shares) more readily than lower-visibility forms of attention—i.e., what users notice or choose to read without necessarily interacting publicly. In this study, we address this gap with a social media clone platform that separately records attention and engagement. We ran the study in both the United Kingdom (once) and the United States (thrice). Across both contexts, we found that the ideology-engagement association is significantly larger than the ideology-attention association. This underscores the importance of measuring attention, rather than solely engagement, to accurately assess the diversity of online information diets."]]></description>
<dc:subject>to:NB social_media polarization preference_falsification</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5b541a1f8727/</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:social_media"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:polarization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:preference_falsification"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.nature.com/articles/s41586-026-10953-2">
    <title>Operational Tropical Cyclone Forecasting with AI | Nature</title>
    <dc:date>2026-08-10T18:40:33+00:00</dc:date>
    <link>https://www.nature.com/articles/s41586-026-10953-2</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Tropical cyclones are among the most dangerous and costly weather phenomena, yet forecasting them remains a profound scientific challenge. Here, we introduce WeatherNext Cyclones (WN-C), an AI operational weather model producing state-of-the-art ensemble forecasts for track, intensity, and size of tropical cyclones worldwide. Trained on a combination of global analysis data1 and a global database of historical tropical cyclones2,3, WN-C generates large ensembles of possible global weather and cyclone scenarios extending 15 days into the future. Evaluated on tropical cyclones from 2023–2025, the track, intensity and wind radii predictions from WN-C offer an average of a day or more of lead time advantage over leading operational models, an improvement in accuracy comparable to the progress seen over the last decade of operational development. We achieved these results using inputs orders of magnitude coarser than regional models, suggesting that high resolution is not a strict prerequisite for state-of-the-art intensity forecasting and that this coarser atmospheric data contains more intensity signal than previously recognised. Including predictions from WN-C in a weighted-average consensus ensemble substantially improves its skill. The scalability of WN-C enables up to 1,000-member ensembles which better capture rare events over conventional 50-member ensembles. By providing state-of-the-art operational ensemble guidance to human forecasters, this work represents a step-change towards more reliable and timely forecasts and warnings that can help protect lives and mitigate the devastating impacts of tropical cyclones."]]></description>
<dc:subject>to:NB prediction neural_networks meteorology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5512310d2847/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neural_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:meteorology"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.science.org/doi/full/10.1126/sciadv.adz6502">
    <title>Untrustworthy sources on Facebook and Instagram in 2020: Concentrated exposure but no attitudinal effects | Science Advances</title>
    <dc:date>2026-08-10T18:39:37+00:00</dc:date>
    <link>https://www.science.org/doi/full/10.1126/sciadv.adz6502</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Despite concern about exposure to content from untrustworthy sources on social media, little is known about the frequency or effects of exposure to their content. We examine 2020 data from all active US adults on Facebook and Instagram to measure exposure to content from Pages, groups, and web domains on Facebook and public accounts on Instagram that repeatedly publish misinformation. We find that average users saw relatively little content in their feeds from these untrustworthy sources; exposure was highly concentrated. A multimonth field experiment during the 2020 election among consenting users reduced feed-based exposure to content from untrustworthy sources by approximately 70% on both platforms but had no measurable effects on numerous preregistered outcomes, even among participants with high pretreatment exposure. Our results demonstrate that a feasible platform intervention can successfully reduce exposure to content from untrustworthy sources but suggest that these changes are unlikely to have immediate effects on attitudes and beliefs."]]></description>
<dc:subject>to:NB social_media epidemiology_of_representations</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:559f8f2ed2e7/</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:social_media"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epidemiology_of_representations"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2607.17397">
    <title>[2607.17397] The unintended consequences of large language models as a labor-augmenting technology in science</title>
    <dc:date>2026-08-10T18:20:38+00:00</dc:date>
    <link>https://arxiv.org/abs/2607.17397</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["As a labor-augmenting technology, large language models (LLMs) have the potential to accelerate scientific activity across the research pipeline. But even if LLMs perform on par with human experts at selected tasks, their use will bring unintended consequences as they alter the balance of frictions and inducements that steer the allocation of research effort across projects. Here we develop a simple mathematical model to illustrate. In fields where LLMs are useful primarily as tools for discovering promising projects, researchers will become more selective about what they publish; where they facilitate the process of publishing existing data, researchers will become less selective. By allowing scientists to work more quickly, LLMs raise the opportunity cost of researcher time, creating incentives to refine papers less thoroughly before moving on. Enticing as it is to imagine that, by saving us time on mundane tasks, LLMs will provide us with more time to think deeply and develop projects completely, our results temper such hopes."]]></description>
<dc:subject>to:NB large_language_models_(so_called) science_as_a_social_process bergstrom.carl</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2283b6baa54f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:science_as_a_social_process"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bergstrom.carl"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2604.16653">
    <title>[2604.16653] Continuous transformations of probability measures and their transport representations</title>
    <dc:date>2026-08-10T18:20:09+00:00</dc:date>
    <link>https://arxiv.org/abs/2604.16653</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Given a function F transforming a probability measure μ into another one F(μ), we study the existence and regularity of a transport representation of it. That is, we ask whether we can represent the image F(μ) of the input probability measure μ as the push-forward of μ by a map f(⋅,μ) which may depend on μ; and furthermore, how regular f can be chosen depending on F. Even if F is continuous and a transport representative exists, it cannot necessarily be chosen in a continuous way; however, if F is Lipschitz continuous with respect to the Wasserstein distance, then f can be chosen continuous. We provide several examples to illustrate the sharpness of our assumptions. This question is motivated by approximation results for transformations of probability distributions with transformers."]]></description>
<dc:subject>to:NB probability via:mraginsky</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3dd3c141c6d9/</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:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:mraginsky"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.aeaweb.org/articles?id=10.1257%2Faer.20231468">
    <title>When Product Markets Become Collective Traps: The Case of Social Media - American Economic Association</title>
    <dc:date>2026-08-10T18:18:39+00:00</dc:date>
    <link>https://www.aeaweb.org/articles?id=10.1257%2Faer.20231468</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Individuals might experience negative utility from not consuming a popular product. With such externalities to nonusers, standard consumer surplus measures, which take aggregate consumption as given, fail to appropriately capture consumer welfare. We propose an approach to account for these externalities and apply it to estimate consumer welfare from two social media platforms: TikTok and Instagram. Incentivized experiments with college students indicate positive welfare based on the standard measure but negative welfare when accounting for these nonuser externalities. Our findings highlight the existence of product market traps, where active users of a platform prefer it not to exist."

--- ETA after skimming: It's not perfect, but if you believe any willingness-to-pay results, these look legit.
--- I did not, in my skim, see the authors discuss appropriate economic remedies.  The usual economist's reaction to a negative externality is some sort of Pigouvian tax --- "make the polluter pay".  Here that'd mean that the companies running the services should be taxed for each additional user.  (Since the study puts the value of Instagram not existing at over $30/user, and there are 180 million Instagram users in the U.S., a ball-park figure would be over $5 billion dollars.  [Yes, yes, marginal user vs. average user, I know.])]]></description>
<dc:subject>to:NB collective_action social_media re:actually-dr-internet-is-the-name-of-the-monsters-creator</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b79be7e6abaa/</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:collective_action"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_media"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:actually-dr-internet-is-the-name-of-the-monsters-creator"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.tandfonline.com/doi/full/10.1080/01916599.2026.2697365">
    <title>The bee and the architect: scientists against scientism, a Transnational European Movement (1976–1978): History of European Ideas: Vol 0, No 0 - Get Access</title>
    <dc:date>2026-08-10T18:17:34+00:00</dc:date>
    <link>https://www.tandfonline.com/doi/full/10.1080/01916599.2026.2697365</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The Radical Science Movement emerged in the 1970s to challenge established scientific practices and the social consequences of technological modernity. This article investigates prominent radical scientists in Italy, Britain, West Germany, and France, such as Marcello Cini, Hilary and Steven Rose, the Max Planck researchers in Starnberg, and Jean-Marc Lévy-Leblond. Inspired by Marx, Kuhn, Marcuse and Heisenberg, radical scientists challenged the notion of scientific neutrality. They denounced the distortion of science by capitalism and advocated for a new science aligned with social goals. Their primary target was social democrats and communists who embraced productivism and mastery over nature and believed that growth of scientific power would lead to socialism. They criticised both the USA and the USSR while expressing admiration for China and Vietnam. Radical scientists revealed that, far from being a liberal profession, scientific work was as alienating and oppressive as any job under capitalism. However, their analysis had many weaknesses, such as being vague in depicting an alternative science, embracing pseudoscience, and exoticising distant countries. These intellectual contributions emerged at a turning point in Western culture, when the science-based optimism about the future and progress began to give way to scepticism about rationality and fear about technological risks."]]></description>
<dc:subject>to:NB history_of_science wisdom_of_the_east</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:14d0a20291aa/</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:wisdom_of_the_east"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2607.06145">
    <title>[2607.06145] Prompting Complexity: Shortest Prompts for Texts and Behaviors in LLMs</title>
    <dc:date>2026-08-10T18:16:28+00:00</dc:date>
    <link>https://arxiv.org/abs/2607.06145</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this paper, we define the quantity of prompting complexity: for a fixed instruction-tuned language model, what is the shortest plausible prompt that makes deterministic decoding produce a target text? It is an LM-relative analogue of resource-bounded Kolmogorov complexity: the prompt is a program, the model interface is the interpreter, and information omitted from the prompt is supplied by the model's weights, training distribution, tokenizer, template, and decoding rule. Unlike classical Kolmogorov complexity, this measure is intentionally non-universal. In the finite-context setting it is computable by enumeration, but there is no model-independent invariance theorem; the same text may be cheap for one model and inaccessible or expensive for another. To keep the search space aligned with prompt engineering, we restrict programs to plausible human-readable texts rather than arbitrary token strings. We extend the exact definition to soft prompting complexity for approximate outputs, yielding a lossy notion of model-relative text compression and a formal target for prompt optimization. We also define prompting distance by comparing shortest generating prompts, and behavioral prompting complexity for reaching any output satisfying a specification. Based on these formulations, we define a research agenda for empirically studying which texts and behaviors are accessible from short plausible prompts under a fixed LM interface."

--- Cf. [https://pinboard.in/u:cshalizi/b:0b7cc7c67740]]]></description>
<dc:subject>to:NB large_language_models_(so_called) no_relation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3c3d7acc876c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:no_relation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.annualreviews.org/content/journals/10.1146/annurev-genom-091416-035340">
    <title>On the Evolution of Lactase Persistence in Humans | Annual Reviews</title>
    <dc:date>2026-08-10T18:12:47+00:00</dc:date>
    <link>https://www.annualreviews.org/content/journals/10.1146/annurev-genom-091416-035340</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Lactase persistence—the ability of adults to digest the lactose in milk—varies widely in frequency across human populations. This trait represents an adaptation to the domestication of dairying animals and the subsequent consumption of their milk. Five variants are currently known to underlie this phenotype, which is monogenic in Eurasia but mostly polygenic in Africa. Despite being a textbook example of regulatory convergent evolution and gene-culture coevolution, the story of lactase persistence is far from clear: Why are lactase persistence frequencies low in Central Asian herders but high in some African hunter-gatherers? Why was lactase persistence strongly selected for even though milk processing can reduce the amount of lactose? Are there other factors, outside of an advantage of caloric intake, that contributed to the selective pressure for lactase persistence? It is time to revisit what we know and still do not know about lactase persistence in humans."]]></description>
<dc:subject>to:NB historical_genetics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6675c66a76b0/</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:historical_genetics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2607.25139">
    <title>[2607.25139] Emergent contagion complexity: Disentangling mechanistic complexity from correlated heterogeneity</title>
    <dc:date>2026-08-10T18:09:38+00:00</dc:date>
    <link>https://arxiv.org/abs/2607.25139</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Simple and complex contagions differ mechanistically; multiple exposures act synergistically in the latter but independently in the former. Yet correlated mixtures of simple contagions may appear complex when inferring global contagion rules, a phenomenon we call "emergent complexity." We present a measure of contagion complexity and an inferential framework for estimating mixtures of nonparametric contagion rules from time-series data. Our work reframes past studies on complex contagion by offering heterogeneous mixtures of simple contagions as an alternative explanation."]]></description>
<dc:subject>to:NB contagion epidemic_models statistical_inference_for_stochastic_processes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b6faa5cf9dc8/</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:contagion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epidemic_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_inference_for_stochastic_processes"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.aps.org/prx/abstract/10.1103/tmkr-9kl2">
    <title>Limits of Inference in Complex Systems: When Stochastic Models Become Indistinguishable | Phys. Rev. X</title>
    <dc:date>2026-08-10T18:05:50+00:00</dc:date>
    <link>https://journals.aps.org/prx/abstract/10.1103/tmkr-9kl2</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Robust inference for stochastic dynamical systems is often hampered by sparse sampling and the absence of closed-form likelihoods. We introduce a Monte Carlo path-inference framework that leverages full-path statistics and bridge processes to deliver reliable parameter estimation and model selection from coarsely sampled time series, without requiring analytical solutions. Crucially, we couple mechanistic stochastic models with their inference procedures to quantify how experimental design—specifically, sampling frequency and dataset size—governs estimator precision and model distinguishability. This analysis reveals optimal sampling regimes and sharp, resolution-dependent limits beyond which competing models become empirically indistinguishable. We validate the approach across four disparate systems—trajectories of optically trapped particles, human microbiome dynamics, social-media topic mentions, and forest population time series—recovering parameters and identifying when inference is fundamentally constrained by measurement resolution, thereby clarifying ongoing debates about dominant noise sources in these systems. Together, these results establish path-based Monte Carlo as a practical, general tool for inference and model discrimination in complex systems and provide principled guidelines for designing measurements that maximize information under real-world constraints."

--- Curious to see what this amounts to in my terms.]]></description>
<dc:subject>to:NB statistical_inference_for_stochastic_processes statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c7fa781757f0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_inference_for_stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://hdsr.mitpress.mit.edu/pub/op7v0ofq/release/1?readingCollection=78528461">
    <title>Shaping Data Science: Statistics and Geometry · Issue 8.3, Summer 2026</title>
    <dc:date>2026-08-10T18:03:51+00:00</dc:date>
    <link>https://hdsr.mitpress.mit.edu/pub/op7v0ofq/release/1?readingCollection=78528461</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>geometry statistics track_down_references</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f98cde1f69c3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:geometry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:track_down_references"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.quantamagazine.org/is-ai-reasoning-right-for-the-wrong-reasons-20260731/">
    <title>Is AI Reasoning Right for the Wrong Reasons? | Quanta Magazine</title>
    <dc:date>2026-08-10T18:03:23+00:00</dc:date>
    <link>https://www.quantamagazine.org/is-ai-reasoning-right-for-the-wrong-reasons-20260731/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>have_read large_language_models_(so_called)</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ca8402958002/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://en.wikipedia.org/wiki/Congressional_Airport">
    <title>Congressional Airport - Wikipedia</title>
    <dc:date>2026-08-10T16:47:55+00:00</dc:date>
    <link>https://en.wikipedia.org/wiki/Congressional_Airport</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- I'd always wondered why that was "Congressional Plaza".  The idea that it could've been the site of DCA, with tons of airplanes approaching over Rockville Pike, is... curious.]]></description>
<dc:subject>have_read maryland via:aeo</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d3504d0a0947/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:maryland"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:aeo"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2605.09294">
    <title>[2605.09294] Towards Effective Theory of LLMs: A Representation Learning Approach</title>
    <dc:date>2026-08-10T16:43:11+00:00</dc:date>
    <link>https://arxiv.org/abs/2605.09294</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We propose Representational Effective Theory (RET), a framework for describing large language model computation in terms of learned macrostates rather than microscopic details. RET learns these macrostates from hidden-state trajectories using a BYOL/JEPA-style self-supervised objective, coarse-graining activations into macrovariables that preserve higher-level structure relevant for prediction and interpretation. We evaluate whether these macrovariables are practically relevant for interpretability: RET yields temporally consistent states that reveal "mental-state" trajectories of reasoning, capture high-level semantic structure, support early prediction of behavioral outcomes such as sycophancy, and provide causal handles for steering generations toward interpretable computational phases. Together, these results suggest that LLM computation admits useful effective descriptions via RET: high-level, dynamically meaningful variables that support interpretation, prediction, and intervention."

--- Guess I should walk over to Gates and talk to them.]]></description>
<dc:subject>to:NB large_language_models_(so_called) macro_from_micro</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ab773838ea20/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://sociologicalscience.com/articles-v13-31-802/">
    <title>Leveraging Genomic Data to Document Within-Race Attractiveness Penalties Among Black Americans</title>
    <dc:date>2026-08-10T15:08:02+00:00</dc:date>
    <link>https://sociologicalscience.com/articles-v13-31-802/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In recent years, scholars of racial inequality have increasingly sought to move beyond simply quantifying discrete racial disparities and instead measure social stratification as a function of continuous racialized characteristics that vary both within and between racial groups. In this article, we draw on a sample of genotyped respondents from the Add Health study and construct genetic similarity proportions, individual-level measures that correlate with racialized physical features that vary across the expansive family tree of humanity (skin tone, facial structure, hair texture, etc.). We then investigate the relationship between these proportions and interviewer-rated physical attractiveness among self-identified Black Americans (N=2,087). Our findings highlight the existence of substantial attractiveness penalties related to having higher levels of Sub-Saharan African (as opposed to European) genetic similarity."

--- Last tag is really "to mention in the footnotes".
--- Notes after reading: I do not like using linear regression to predict a 5-level Likert scale.  Results for a logistic regression of predicting "Very Attractive" (vs. not) were similar, but they really should have done an ordinal regression.  Also, they neglect a simple and compelling explanation for why there might be small group differences in rated attractiveness (a finding they confirm) and yet large group disparities in outcomes like dating, viz., competition.  (Well, they _are_ sociologists.)
--- ETA on thinking about it later: The main figures & tables use %African ancestry as the regressor.  Do they check what things look like for %European?  (One presumes similar but with opposite slopes, but the two percentages do _not_ add up to 100 for obvious reasons.)]]></description>
<dc:subject>to:NB racism historical_genetics to_teach:statistics_of_inequality_and_discrimination</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d069d50e4b75/</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:racism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:historical_genetics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_of_inequality_and_discrimination"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://link.springer.com/article/10.1007/s13171-026-00452-x">
    <title>Bootstrapping Generalization Error Bounds for Time Series | Sankhya A | Springer Nature Link</title>
    <dc:date>2026-08-10T14:49:53+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/s13171-026-00452-x</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We consider the problem of constructing confidence intervals for the risk of forecasting the future of a stationary, ergodic stochastic process, using a model estimated from the past of the process. We show that a bootstrap procedure provides valid confidence intervals for the risk, when the data source is sufficiently mixing, and the loss function and the estimator are suitably smooth. Autoregressive (AR(d)) models estimated by least squares obey the necessary regularity conditions, even when mis-specified, and simulations show that the finite-sample coverage of our bounds quickly converges to the theoretical, asymptotic level. As an intermediate step, we derive sufficient conditions for asymptotic independence between empirical distribution functions formed by splitting a realization of a stochastic process, of independent interest."

--- Ungated: [http://arxiv.org/abs/1711.02834]

--- (This isn't my longest gap between a preprint and a journal version, but it's up there.)]]></description>
<dc:subject>in_NB time_series bootstrap prediction data_splitting self-promotion kith_and_kin lunde.robert</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:62d6e1157d0a/</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:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bootstrap"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_splitting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:self-promotion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:lunde.robert"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2608.01326">
    <title>[2608.01326] Context Compaction Theory</title>
    <dc:date>2026-08-10T14:42:28+00:00</dc:date>
    <link>https://arxiv.org/abs/2608.01326</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Large Language Models (LLMs) have a bounded context window. The context window is the maximum input size an LLM can consume for a single inference. AI agents rely on a process called context compaction to fit their state within the context window when calling an LLM. Despite its ubiquity, context compaction has received essentially no formal analysis. In this paper, we initiate a formal study of context compaction. We first introduce a framework consisting of two games that capture the two algorithmic strategies for context compaction used by contemporary AI agents in practice. The Context Selection Game models context compaction algorithms that select a subset of an agent's accumulated state to retain. The Context Generation Game models context compaction algorithms that summarize an agent's state by an arbitrary message of bounded length. We then prove an equivalence between the Context Generation Game and one-way communication complexity. The minimum context compaction budget for answering a set of queries within a target error is equal to the one-way communication complexity of the induced communication problem at the same error. Known bounds from communication complexity therefore transfer directly to context compaction. We also show that the Context Selection Game corresponds to a restricted class of one-way communication protocols. Any gap between selection and generation is therefore a gap between two classes of communication protocols. We prove that there exists a set of queries for which generation needs strictly less budget than selection. The equivalence between the Context Generation Game and one-way communication also lets us measure how well a deployed context compaction algorithm performs on a query relative to the optimal strategy. As an example, we present a case study that evaluates Anthropic's context compaction endpoint on set membership queries."]]></description>
<dc:subject>to:NB large_language_models_(so_called) information_theory mitzenmacher.michael to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:199b02f76217/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mitzenmacher.michael"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/subjective-selection-superattractors-and-the-origins-of-the-cultural-manifold/2D71AA44CFDB34F171A3D2E2F4410173">
    <title>Subjective selection, super-attractors, and the origins of the cultural manifold | Behavioral and Brain Sciences | Cambridge Core</title>
    <dc:date>2026-08-10T14:41:47+00:00</dc:date>
    <link>https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/subjective-selection-superattractors-and-the-origins-of-the-cultural-manifold/2D71AA44CFDB34F171A3D2E2F4410173</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Human societies consistently develop complex, near-universal traditions that exhibit striking similarities. These “super-attractors” span magic and religion (e.g., shamanism, supernatural punishment beliefs), esthetics (e.g., heroic tales, dance songs), and social institutions (e.g., justice, corporate groups), and collectively constitute the “cultural manifold.” Here, I argue that the components of the cultural manifold develop primarily through “subjective selection,” or the production and selective retention of variants evaluated as useful for satisfying goals. Whereas previous explanations emphasize objective individual or group-level benefits, I highlight how subjective selection drives complex cultural convergences worldwide, attesting to the importance of subjective selection in shaping human culture."

--- BBS target article, with plenty of commentaries.]]></description>
<dc:subject>to:NB to_read cultural_evolution re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:58e7633a7563/</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:cultural_evolution"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://iai.tv/articles/puzzles-reveal-the-limits-of-ai-auid-3638">
    <title>Puzzles reveal the limits of AI | Cristopher Moore » IAI TV</title>
    <dc:date>2026-08-10T14:40:56+00:00</dc:date>
    <link>https://iai.tv/articles/puzzles-reveal-the-limits-of-ai-auid-3638</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>have_read to:NB artificial_intelligence problem_solving kith_and_kin moore.cristopher</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0125d9affcb1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:artificial_intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:problem_solving"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:moore.cristopher"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2411.10939">
    <title>[2411.10939] Evaluating Generative AI Systems is a Social Science Measurement Challenge</title>
    <dc:date>2026-08-10T14:40:03+00:00</dc:date>
    <link>https://arxiv.org/abs/2411.10939</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult. We argue that these measurement tasks are highly reminiscent of measurement tasks found throughout the social sciences. With this in mind, we present a framework, grounded in measurement theory from the social sciences, for measuring concepts related to the capabilities, impacts, opportunities, and risks of GenAI systems. The framework distinguishes between four levels: the background concept, the systematized concept, the measurement instrument(s), and the instance-level measurements themselves. This four-level approach differs from the way measurement is typically done in ML, where researchers and practitioners appear to jump straight from background concepts to measurement instruments, with little to no explicit systematization in between. As well as surfacing assumptions, thereby making it easier to understand exactly what the resulting measurements do and do not mean, this framework has two important implications for evaluating evaluations: First, it can enable stakeholders from different worlds to participate in conceptual debates, broadening the expertise involved in evaluating GenAI systems. Second, it brings rigor to operational debates by offering a set of lenses for interrogating the validity of measurement instruments and their resulting measurements."]]></description>
<dc:subject>to:NB measurement social_measurement large_language_models_(so_called) kith_and_kin jacobs.abigail_z.</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c39bcf5cb31e/</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:measurement"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_measurement"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:jacobs.abigail_z."/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.nature.com/articles/s41593-025-02031-z">
    <title>A neural manifold view of the brain | Nature Neuroscience</title>
    <dc:date>2026-08-05T04:06:04+00:00</dc:date>
    <link>https://www.nature.com/articles/s41593-025-02031-z</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Animal behavior arises from the coordinated activity of neural populations that span the entire brain. The activity of large neural populations from an increasing number of brain regions, behaviors and species shows low-dimensional structure. We posit that this structure arises as a result of neural manifolds. Neural manifolds are mathematical descriptions of a meaningful biological entity: the possible collective states of a population of neurons given the constraints, both intrinsic (for example, connectivity) and extrinsic (for example, behavior), to the neural circuit. Here, we explore the link between neural manifolds and behavior, and discuss the insights that the neural manifold framework can provide into brain function. To conclude, we explore existing conceptual gaps in this framework and discuss their implications when building an integrative view of brain function. We thus position neural manifolds as a crucial framework with which to describe how the brain generates behavior."

--- The last tag is madly ambitious.]]></description>
<dc:subject>to:NB manifold_learning neuroscience neural_coding_and_decoding re:codename:catherine_wheel</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:03bb6504d5c8/</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:manifold_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neuroscience"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neural_coding_and_decoding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:codename:catherine_wheel"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1013130">
    <title>Information theoretic measures of neural and behavioural coupling predict representational drift | PLOS Computational Biology</title>
    <dc:date>2026-08-05T04:04:19+00:00</dc:date>
    <link>https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1013130</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In many parts of the brain, population tuning to stimuli and behaviour gradually changes over the course of days to weeks in a phenomenon known as representational drift. The tuning stability of individual cells varies over the population, and it remains unclear what drives this heterogeneity. We investigate how a neuron’s tuning stability relates to its shared variability with other neurons in the population using two published datasets from posterior parietal cortex and visual cortex. We quantified the contribution of pairwise interactions to behaviour or stimulus encoding by partial information decomposition, which breaks down the mutual information between the pairwise neural activity and the external variable into components uniquely provided by each neuron and by their interactions. Information shared by the two neurons is termed ‘redundant’, and information requiring knowledge of the state of both neurons is termed ‘synergistic’. We found that a neuron’s tuning stability is positively correlated with the strength of its average pairwise redundancy with the population. We hypothesize that subpopulations of neurons show greater stability because they are tuned to salient features common across multiple tasks. Regardless of the mechanistic implications of our work, the stability–redundancy relationship may support improved longitudinal neural decoding in technology that has to track population dynamics over time, such as brain–machine interfaces."]]></description>
<dc:subject>to:NB neural_coding_and_decoding information_theory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:be3a18a79790/</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:neural_coding_and_decoding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>