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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://www.mariangoodman.com/exhibitions/agnieszka-kurant-recursion-new-york/"/>
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  </channel><item rdf:about="https://www.annualreviews.org/content/journals/10.1146/annurev-polisci-032624-025000">
    <title>Conceptualizing Academic Freedom | Annual Reviews</title>
    <dc:date>2026-06-18T14:27:59+00:00</dc:date>
    <link>https://www.annualreviews.org/content/journals/10.1146/annurev-polisci-032624-025000</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Academic freedom is an unusual and complex set of norms and practices. It arises out of the combination of the corporate self-governance of medieval universities and the spirit of disciplinary scientific inquiry in modern research universities. It combines a principle of antiorthodoxy as to conclusions with the robust associational self-governance of scholarly communities whose members evaluate one another as participants in that shared enterprise. It has never been easily or wholly embraced by wider societies; today it is under wholesale attack. This article combines conceptual, normative, and historical analyses of academic freedom as a general norm with attention to conflicts over it in the mid-to-late 2010s and early 2020s. Some genuinely hard cases and questions tested the meaning of academic freedom and university values well before the current crisis."]]></description>
<dc:subject>to:NB to_read academic_freedom via:henry_farrell</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:01dff0f0ccd5/</dc:identifier>
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<item rdf:about="https://direct.mit.edu/books/monograph/6064/Wired-for-WordsThe-Neural-Architecture-of-Language">
    <title>Wired for Words: The Neural Architecture of Language | Books Gateway | MIT Press</title>
    <dc:date>2026-06-18T14:10:56+00:00</dc:date>
    <link>https://direct.mit.edu/books/monograph/6064/Wired-for-WordsThe-Neural-Architecture-of-Language</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A critical synthesis of over 150 years of research on the brain’s networks that enable us to communicate through language.
"The neural architecture of language has been a hotly debated topic in neurology, cognitive neuroscience, linguistics, and philosophy since the early 1800s. Is language separable from intelligence? Is it enabled by dedicated and localizable neural networks? Do we speak and understand with our left hemisphere? How did language emerge? Is language grounded in sensorimotor systems, or is it abstract and amodal? Will we ever have a clear picture of how syntax, the pinnacle of human linguistic prowess, is organized neurologically?
"Wired for Words answers these questions and more. Gregory Hickok tells the stories behind the big ideas, revealing the source of both modern progress and persistent myths. Drawing on decades of research using tools and insights from neurology, functional imaging, neurosurgery, linguistics, psychology, and engineering, Hickok builds a new understanding of the neural architecture—the components and connection patterns—of the brain’s language system from sound to meaning to speech."]]></description>
<dc:subject>in_NB books:noted linguistics neuropsychology to_download</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:295cd5ae0a19/</dc:identifier>
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<item rdf:about="https://www.youtube.com/watch?v=fO9iRDPXvT4">
    <title>The Most Arrogant Science Book Ever Written - YouTube</title>
    <dc:date>2026-06-18T13:57:09+00:00</dc:date>
    <link>https://www.youtube.com/watch?v=fO9iRDPXvT4</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- As the person who alerted me to this put it, the sincerest form of flattery.  (I am credited with authoring the review at the beginning, so I suppose I don't have _too_ much to complain about, and sending a take-down notice would just be churlish.)  But the idea that it's worth someone's while to narrate a book review I wrote in 2002, because it gets hundreds of thousands of views, is very strange to me (to put it mildly).]]></description>
<dc:subject>self-centered not_exactly_self-promotion networked_life</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8409e874e8d6/</dc:identifier>
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<item rdf:about="https://nathan.rs/posts/gzip-lm/">
    <title>Can gzip be a language model?</title>
    <dc:date>2026-06-17T16:28:26+00:00</dc:date>
    <link>https://nathan.rs/posts/gzip-lm/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- Cf. [https://bactra.org/notebooks/nn-attention-and-transformers.html#gllz], but the beam-search trick is a good one.]]></description>
<dc:subject>to:NB re:gllz via:kjhealy to_teach:statistics_and_generative_ai have_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:da162a9639cd/</dc:identifier>
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    <title>[2606.13280] Generalization Bounds for Transformer-Based Next-Token Prediction in a Language Model</title>
    <dc:date>2026-06-17T16:26:17+00:00</dc:date>
    <link>https://arxiv.org/abs/2606.13280</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A refined statistical understanding of LLM pre-training requires the analysis of the transformer architecture for data distributions that encapsulate key characteristics of text data. To address this, we propose a text data distribution based on an extension of the log-bilinear language model from the natural language processing literature. For this data generating process, we derive generalization bounds for deep transformer architectures, highlighting the dependence on the network architecture, the vocabulary size, the number of documents and the document length."]]></description>
<dc:subject>to:NB to_read learning_theory natural_language_processing large_language_models_(so_called) via:mraginsky</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
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<item rdf:about="https://cacm.acm.org/opinion/artificial-intelligence-for-software-engineering-from-probable-to-provable/">
    <title>Artificial Intelligence for Software Engineering: From Probable to Provable – Communications of the ACM</title>
    <dc:date>2026-06-17T16:06:58+00:00</dc:date>
    <link>https://cacm.acm.org/opinion/artificial-intelligence-for-software-engineering-from-probable-to-provable/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Here we go again: No programmers will be needed anymore! AI will generate the code! If you have been around for a while, you may feel a sense of déjà vu. That same line advertised COBOL in the 1960s, 4GLs in the 1970s, CASE tools in the 1980s, component-based development in the 1990s, model-driven architecture in the 2000s, and low-code/no-code in the 2010s. Some of these approaches did improve programming, but they did not replace programming, let alone programmers. They simply introduced higher levels of abstraction or new tools, sometimes taking advantage of a restricted application domain. Is it the same this time, or do artificial intelligence (AI) and vibe coding upend the game? More generally, can AI and software engineering enter into a successful marriage?
"Warning and spoiler alert: Even though the following discussion starts out by examining limitations of AI for software construction, do not just expect a critique. Its aim is positive, in support of AI-supported software engineering. Its core thesis (here I am really spilling the beans) is that a successful solution requires combining AI with formal verification. (End of spoiler.)"

--- This makes sense, but I keep getting hung up on why we didn't do all of this with "genetic programming" in the 1990s.  "Prompt engineering is requirements specification", yes, but then you define a fitness function (*) and let the code evolve.  If you have good verification tools, you include that in the fitness function.  Maybe code is _so_ repetitive that training things to semi-memorize large chunks of all the code on the Internet is better than evolving from scratch, but I'd really like to see the cost-benefit on that...

*: Or a vector of fitness functions and drive to the Pareto frontier.  (I think I finally get why Bill T. was so into multi-objective optimization for genetic programming.)]]></description>
<dc:subject>to:NB large_language_models_(so_called) programming via:mraginsky have_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
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</item>
<item rdf:about="https://www.trainjazz.com/">
    <title>Every train, a note.</title>
    <dc:date>2026-06-17T16:01:41+00:00</dc:date>
    <link>https://www.trainjazz.com/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Every dot is a real subway train. Eight hundred of them, give or take, form a small jazz combo (walking bass, piano, sax, vibes, brushes) that has been playing without pause for over a hundred years. On the platforms they are hot, screaming, full of complaint. This is the music inside the noise.
"The harmony moves through a slow chorus. A note is placed precisely where the train happens to be along its route. Rush hour fills the band with held tones; at 3 a.m. the silences grow longer. Whatever is playing now has not played before and will not play again.
"Share your location and the trains nearest you grow louder. The piece rearranges itself around your body. You are listening to a portrait of where you stand, played by the city you are standing in."

--- Actually sounds decent, unlike most such stunts.]]></description>
<dc:subject>music new_york_city have_listened</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5585d2bf4f76/</dc:identifier>
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<item rdf:about="https://www.reuters.com/commentary/breakingviews/physical-shocks-are-shrinking-power-money-2026-06-04/">
    <title>Physical shocks are shrinking the power of money | Reuters</title>
    <dc:date>2026-06-17T16:00:46+00:00</dc:date>
    <link>https://www.reuters.com/commentary/breakingviews/physical-shocks-are-shrinking-power-money-2026-06-04/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>book_reviews economics money track_down_references</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:fc12e76a2400/</dc:identifier>
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<item rdf:about="https://aclanthology.org/2020.cl-2.7/">
    <title>Fair Is Better than Sensational: Man Is to Doctor as Woman Is to Doctor - ACL Anthology</title>
    <dc:date>2026-06-17T16:00:06+00:00</dc:date>
    <link>https://aclanthology.org/2020.cl-2.7/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Analogies such as man is to king as woman is to X are often used to illustrate the amazing power of word embeddings. Concurrently, they have also been used to expose how strongly human biases are encoded in vector spaces trained on natural language, with examples like man is to computer programmer as woman is to homemaker. Recent work has shown that analogies are in fact not an accurate diagnostic for bias, but this does not mean that they are not used anymore, or that their legacy is fading. Instead of focusing on the intrinsic problems of the analogy task as a bias detection tool, we discuss a series of issues involving implementation as well as subjective choices that might have yielded a distorted picture of bias in word embeddings. We stand by the truth that human biases are present in word embeddings, and, of course, the need to address them. But analogies are not an accurate tool to do so, and the way they have been most often used has exacerbated some possibly non-existing biases and perhaps hidden others. Because they are still widely popular, and some of them have become classics within and outside the NLP community, we deem it important to provide a series of clarifications that should put well-known, and potentially new analogies, into the right perspective."

--- The most astonishing thing to me here is realizing that in the usual "A is to B as C is to D" protocols, lots of experiments _prohibited_ D from being the same as B, so e.g. in "Man is to doctor as Woman is to ?", the answer _could not_ be "doctor".  (This of course connects to the authors' point that it's often really unclear what an acceptable, un-biased answer might possibly be.)]]></description>
<dc:subject>to:NB have_read analogy algorithmic_fairness to_teach:data-mining to_teach:statistics_of_inequality_and_discrimination</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ff3f7072cb0a/</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:analogy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:algorithmic_fairness"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
	<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://cooking.nytimes.com/recipes/780676171-cucumber-and-onion-salad">
    <title>Cucumber and Onion Salad Recipe</title>
    <dc:date>2026-06-17T15:57:16+00:00</dc:date>
    <link>https://cooking.nytimes.com/recipes/780676171-cucumber-and-onion-salad</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[INGREDIENTS
Yield: 6 servings
4 English cucumbers, sliced into 1/8-inch-thick rounds (about 10 cups)
2 tablespoons kosher salt (such as Diamond Crystal)
1.5 cups white vinegar
1/4 cup sugar
2 teaspoons fresh ground black pepper
1 small white onion, thinly sliced (about 1½ cups)

PREPARATION
Step 1
Toss cucumbers and salt together in a large bowl. Transfer cucumbers to a strainer, then place the strainer in the sink. Let cucumbers sit for 30 minutes as they release their water, stirring occasionally to help them drain. 

Step 2
Meanwhile, whisk the vinegar, sugar and pepper in a large bowl until the sugar dissolves. 

Step 3
Add the drained cucumbers and sliced onion to the bowl of vinegar marinade. Use your hands or tongs to toss well—really get in there and make sure the marinade is distributed. Cover and refrigerate for at least 1 hour before serving, stirring once halfway through to ensure everything gets evenly marinated.


--- AEO says it reminds her of her grandparents (in central PA, not the South...)]]></description>
<dc:subject>food recipes have_made</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:59b740127f14/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:food"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:recipes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_made"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://link.springer.com/book/10.1007/978-3-031-97239-3">
    <title>Signature Methods in Finance: An Introduction with Computational Applications | Springer Nature Link</title>
    <dc:date>2026-06-13T04:04:14+00:00</dc:date>
    <link>https://link.springer.com/book/10.1007/978-3-031-97239-3</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This Open Access volume offers an accessible entry point into the fast-growing field of signature methods in finance. It is written for early-career researchers and quantitatively minded practitioners—quant analysts and applied researchers—seeking a clear, practical introduction. It highlights recent developments and includes coding examples to help readers apply signature methods in practice.
"The advantages of modeling financial markets from a path-wise perspective, rather than as a traditional series of returns, are increasingly gaining recognition. Signature methods provide a parsimonious description of paths of stochastic processes and, through the signature kernel, open a rich and compelling framework at the interface between machine learning and mathematical finance."]]></description>
<dc:subject>to:NB books:noted path_signatures time_series stochastic_processes finance kernel_methods</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a5cf1e7772a2/</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:path_signatures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:finance"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kernel_methods"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2601.05444">
    <title>[2601.05444] What Functions Does XGBoost Learn?</title>
    <dc:date>2026-06-04T18:10:45+00:00</dc:date>
    <link>https://arxiv.org/abs/2601.05444</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper establishes a rigorous theoretical foundation for the function class implicitly learned by XGBoost, bridging the gap between its empirical success and our theoretical understanding. We introduce an infinite-dimensional function class d,s∞−ST that extends finite ensembles of bounded-depth regression trees, together with a complexity measure Vd,s∞−XGB(⋅) that generalizes the L1 regularization penalty used in XGBoost. We show that every optimizer of the XGBoost objective is also an optimizer of an equivalent penalized regression problem over d,s∞−ST with penalty Vd,s∞−XGB(⋅), providing an interpretation of XGBoost as implicitly targeting a broader function class. We also develop a smoothness-based interpretation of d,s∞−ST and Vd,s∞−XGB(⋅) in terms of Hardy--Krause variation. We prove that the least squares estimator over {f∈d,s∞−ST:Vd,s∞−XGB(f)≤V} achieves a nearly minimax-optimal rate of convergence n−2/3(logn)4(min(s,d)−1)/3, thereby avoiding the curse of dimensionality. Our results provide the first rigorous characterization of the function space underlying XGBoost, clarify its connection to classical notions of variation, and identify an important open problem: whether the XGBoost algorithm itself achieves minimax optimality over this class."]]></description>
<dc:subject>to:NB functional_analysis boosting ensemble_methods decision_trees via:msw</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:85d5009a07b7/</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:functional_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:boosting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ensemble_methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:decision_trees"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:msw"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.science.org/doi/10.1126/sciadv.aao3580">
    <title>Trends and fluctuations in the severity of interstate wars | Science Advances</title>
    <dc:date>2026-06-04T15:54:36+00:00</dc:date>
    <link>https://www.science.org/doi/10.1126/sciadv.aao3580</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Since 1945, there have been relatively few large interstate wars, especially compared to the preceding 30 years, which included both World Wars. This pattern, sometimes called the long peace, is highly controversial. Does it represent an enduring trend caused by a genuine change in the underlying conflict-generating processes? Or is it consistent with a highly variable but otherwise stable system of conflict? Using the empirical distributions of interstate war sizes and onset times from 1823 to 2003, we parameterize stationary models of conflict generation that can distinguish trends from statistical fluctuations in the statistics of war. These models indicate that both the long peace and the period of great violence that preceded it are not statistically uncommon patterns in realistic but stationary conflict time series. This fact does not detract from the importance of the long peace or the proposed mechanisms that explain it. However, the models indicate that the postwar pattern of peace would need to endure at least another 100 to 140 years to become a statistically significant trend. This fact places an implicit upper bound on the magnitude of any change in the true likelihood of a large war after the end of the Second World War. The historical patterns of war thus seem to imply that the long peace may be substantially more fragile than proponents believe, despite recent efforts to identify mechanisms that reduce the likelihood of interstate wars."]]></description>
<dc:subject>to:NB war kith_and_kin clauset.aaron social_measurement time_series</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e174e28da7ca/</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:war"/>
	<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:social_measurement"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://dl.acm.org/doi/full/10.1145/3722548">
    <title>Concerning the Responsible Use of AI in the U.S. Criminal Justice System | Communications of the ACM</title>
    <dc:date>2026-06-04T15:53:42+00:00</dc:date>
    <link>https://dl.acm.org/doi/full/10.1145/3722548</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>to:NB kith_and_kin algorithmic_fairness moore.cris to_teach:data-mining to_teach:statistics_of_inequality_and_discrimination</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a1ce478cb8c7/</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:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:algorithmic_fairness"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:moore.cris"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
	<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://archive.ph/Obx04">
    <title>Tom Stevenson · Beyond Mesopotamia: Linear Elamite Deciphered</title>
    <dc:date>2026-06-02T13:56:04+00:00</dc:date>
    <link>https://archive.ph/Obx04</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[https://www.lrb.co.uk/the-paper/v47/n04/tom-stevenson/beyond-mesopotamia]]></description>
<dc:subject>in_NB mesopotamia iran elamite have_read ancient_history</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c1749ea60fc7/</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:mesopotamia"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:iran"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:elamite"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ancient_history"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.jstor.org/stable/1403785?seq=1">
    <title>Markov and the Birth of Chain Dependence Theory on JSTOR</title>
    <dc:date>2026-06-01T13:58:12+00:00</dc:date>
    <link>https://www.jstor.org/stable/1403785?seq=1</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>in_NB markov_models history_of_mathematics markov.a.a. ergodic_theory mixing free_will</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b76b51010206/</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:markov_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_mathematics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:markov.a.a."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ergodic_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mixing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:free_will"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2511.05733">
    <title>[2511.05733] Nonparametric Block Bootstrap Kolmogorov-Smirnov Goodness-of-Fit Test</title>
    <dc:date>2026-05-28T13:18:20+00:00</dc:date>
    <link>https://arxiv.org/abs/2511.05733</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The Kolmogorov--Smirnov (KS) test is a widely used statistical test that assesses the conformity of a sample to a specified distribution. Its efficacy, however, diminishes with serially dependent data and when parameters within the hypothesized distribution are unknown. For independent data, parametric and nonparametric bootstrap procedures are available to adjust for estimated parameters. For serially dependent stationary data, parametric bootstrap has been developed with a working serial dependence structure. A counterpart for the nonparametric bootstrap approach, which needs a bias correction, has not been studied. Addressing this gap, our study introduces a bias correction method employing a nonparametric block bootstrap, which approximates the distribution of the KS statistic in assessing the goodness-of-fit of the marginal distribution of a stationary series, accounting for unspecified serial dependence and unspecified parameters. We assess its effectiveness through simulations, scrutinizing both its size and power. The practicality of our method is further illustrated with an examination of stock returns from the S\&P 500 index, showcasing its utility in real-world applications."

--- Gated: [https://doi.org/10.1080/00031305.2025.2588131]]]></description>
<dc:subject>in_NB goodness-of-fit time_series bootstrap</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:568f300920e5/</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:goodness-of-fit"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bootstrap"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/core/books/emergence-of-social-complexity/ABA823DDCB40AA9EB6D9D5B6916CE89B#fndtn-information">
    <title>The Emergence of Social Complexity</title>
    <dc:date>2026-05-23T01:26:48+00:00</dc:date>
    <link>https://www.cambridge.org/core/books/emergence-of-social-complexity/ABA823DDCB40AA9EB6D9D5B6916CE89B#fndtn-information</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The emergence of social complexity is at the heart of archaeological inquiry, but to date, there has been insufficient global comparative analysis of this phenomenon. This volume offers archaeologists and other social scientists reconstructions of past societies in all parts of the world, some of which challenge currently popular accounts. Using recently developed analytical approaches robust enough to yield compatible results from disparate datasets, the reconstructions presented here rest on fresh comparative analysis of archaeological data from 57 regions. They reveal the highly varied pathways to social complexity in ways that make it possible to see previously conflicting ideas as complementary. The analytical approaches and the full datasets are presented in detail in the book as well as an online data base. Offering new insights into the forces that have shaped human societies for millennia, this study provides a deeper understanding of the ways in which archaeology uses the material remains of past societies to reconstruct how they were organized."]]></description>
<dc:subject>to:NB books:noted downloaded archaeology comparative_history</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:79940266e96a/</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:archaeology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:comparative_history"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://kucharski.substack.com/p/real-signals-or-artificial-stereotypes">
    <title>Real signals or artificial stereotypes? - by Adam Kucharski</title>
    <dc:date>2026-05-21T16:09:03+00:00</dc:date>
    <link>https://kucharski.substack.com/p/real-signals-or-artificial-stereotypes</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- Very nice.  I can see getting a lot of use out of this example in many classes.  (Of course it would also be best complemented by subjecting _human_ content-analysts to the same protocol.)]]></description>
<dc:subject>via:kjhealy social_measurement large_language_models_(so_called) have_read to_teach to_teach:statistics_and_generative_ai</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c3e8ce793921/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:kjhealy"/>
	<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:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_and_generative_ai"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://hdl.handle.net/2027/heb01869.0001.001">
    <title>Empires (Doyle, 1986)</title>
    <dc:date>2026-05-20T16:50:03+00:00</dc:date>
    <link>https://hdl.handle.net/2027/heb01869.0001.001</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- Tried explaining the paper to AEO and she immediately gave me more to read (in particular chapter 3).

CMU access link: https://www-fulcrum-org.cmu.idm.oclc.org/concern/monographs/5999n353w]]></description>
<dc:subject>to:NB books:noted to_read imperialism comparative_history re:do-institutions-evolve via:aeo</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e53f31fe26c3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:imperialism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:comparative_history"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:aeo"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://doi.org/10.1108/FTCIT-09-2025-0149">
    <title>Volume 23 Issue 3-4 | Foundations and Trends in Communications and Information Theory | Emerald Publishing</title>
    <dc:date>2026-05-19T15:46:33+00:00</dc:date>
    <link>https://doi.org/10.1108/FTCIT-09-2025-0149</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Online learning is a foundational paradigm underlying applications from recommendation systems to the continual learning of modern AI models. Yet much of its theory centers on either fully adversarial or purely stochastic settings. However, real-world environments typically fall between these extremes, making classical models inadequate for describing practical behavior. This monograph develops a unified perspective for analyzing online learning under more nuanced and realistic environments. The authors approach the problem through the lens of universality from information theory and extend tools such as the Shtarkov sum, covering numbers and packing arguments to the online setting, revealing deeper structural connections between these two fields. Building on this viewpoint, they characterize minimax regret for logarithmic and Lipschitz losses, analyze expected regret under i.i.d. and more general stochastic processes and study hybrid adversarial–stochastic scenarios. The authors further develop constructive algorithms that achieve near-optimal regret guarantees, yielding a coherent and fine-grained information-theoretic framework of online universal learning."]]></description>
<dc:subject>to_read information_theory learning_theory learning_under_dependence in_NB low-regret_learning online_learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:eb2f88390f74/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_under_dependence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:low-regret_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:online_learning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://substack.com/home/post/p-183753276">
    <title>The fall of the theorem economy - David Bessis</title>
    <dc:date>2026-05-06T17:58:46+00:00</dc:date>
    <link>https://substack.com/home/post/p-183753276</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- This is mostly sensible and right-hearted, but it should be cut by at least 50%, maybe 66%, maybe 75%.
--- The shorter and punchier version of this is that one of the very first AI programs was Newell, Simon & Shaw's Logic Theorist of 1956, which really did come up with proofs for _Principia Mathematica_.  At least one of them, IIRC, was shorter than the Whitehead&Russell proof.  Everything since then has been a matter of how much, when, and through what exact technology. ]]></description>
<dc:subject>via:rvenkat mathematics science_as_a_social_process philosophy_of_science large_language_models_(so_called) artificial_intelligence we_have_established_what_you_are_now_we_are_haggling_over_the_price</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:10d34ea058a5/</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:mathematics"/>
	<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:philosophy_of_science"/>
	<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:we_have_established_what_you_are_now_we_are_haggling_over_the_price"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://marginalrevolution.com/marginalrevolution/2026/04/capitalism-and-modernity.html">
    <title>Capitalism and Modernity - Marginal REVOLUTION</title>
    <dc:date>2026-05-05T13:51:24+00:00</dc:date>
    <link>https://marginalrevolution.com/marginalrevolution/2026/04/capitalism-and-modernity.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[absfac's comments [https://pinboard.in/u:absfac/b:43e7451be4b3] endorsed in their entirety.]]></description>
<dc:subject>have_read modernity capitalism alienation via:absfac twitter_threads_that_should_be_blog_posts</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2d9fef8421fd/</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:modernity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:capitalism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:alienation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:absfac"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:twitter_threads_that_should_be_blog_posts"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://stackingthebricks.com/how-blogs-broke-the-web/">
    <title>How the Blog Broke the Web - Stacking the Bricks</title>
    <dc:date>2026-05-04T14:22:35+00:00</dc:date>
    <link>https://stackingthebricks.com/how-blogs-broke-the-web/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- On the one hand, probably true.  On the other hand, how much of my life, cumulatively, has been spent typing '<a href="'?
]]></description>
<dc:subject>the_web_we_have_lost blogging we_shape_our_tools_and_our_tools_shape_us the_present_before_it_was_widely_distributed via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a4d5cd0b780a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:the_web_we_have_lost"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:blogging"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:we_shape_our_tools_and_our_tools_shape_us"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:the_present_before_it_was_widely_distributed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2502.09192">
    <title>[2502.09192] Thinking beyond the anthropomorphic paradigm benefits LLM research</title>
    <dc:date>2026-05-01T13:43:24+00:00</dc:date>
    <link>https://arxiv.org/abs/2502.09192</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Anthropomorphism, or the attribution of human traits to technology, is an automatic and unconscious response that occurs even in those with advanced technical expertise. In this position paper, we analyze hundreds of thousands of research articles to present empirical evidence of the prevalence and growth of anthropomorphic terminology in research on large language models (LLMs). We argue for challenging the deeper assumptions reflected in this terminology -- which, though often useful, may inadvertently constrain LLM development -- and broadening beyond them to open new pathways for understanding and improving LLMs. Specifically, we identify and examine five anthropomorphic assumptions that shape research across the LLM development lifecycle. For each assumption (e.g., that LLMs must use natural language for reasoning, or that they should be evaluated on benchmarks originally meant for humans), we demonstrate empirical, non-anthropomorphic alternatives that remain under-explored yet offer promising directions for LLM research and development."]]></description>
<dc:subject>to:NB via:henry_farrell 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:663d8f4e0529/</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:via:henry_farrell"/>
	<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://arxiv.org/abs/2504.09762">
    <title>[2504.09762] Position: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!</title>
    <dc:date>2026-05-01T13:41:17+00:00</dc:date>
    <link>https://arxiv.org/abs/2504.09762</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Intermediate token generation (ITG), where a model produces output before the solution, has become a standard method to improve the performance of language models on reasoning tasks. These intermediate tokens have been called \say{reasoning traces} or even \say{thoughts} -- implicitly anthropomorphizing the traces, and implying that these traces resemble steps a human might take when solving a challenging problem, and as such can provide an interpretable window into the operation of the model's thinking process to the end user. In this position paper, we present evidence that this anthropomorphization isn't a harmless metaphor, and instead is quite dangerous -- it confuses the nature of these models and how to use them effectively, and leads to questionable research. We call on the community to avoid such anthropomorphization of intermediate tokens."]]></description>
<dc:subject>via:rvenkat to_read to:NB 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:ade957324966/</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:to_read"/>
	<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:chain-of-thought_(so_called)"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://direct.mit.edu/books/monograph/6123/Artificial-ReligionOn-AI-Myth-and-Power">
    <title>Artificial Religion: On AI, Myth, and Power | Books Gateway | MIT Press</title>
    <dc:date>2026-04-30T19:58:33+00:00</dc:date>
    <link>https://direct.mit.edu/books/monograph/6123/Artificial-ReligionOn-AI-Myth-and-Power</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["How AI is shaped by Western religious culture and universal existential aspirations—and why we think we need it in the first place.
"Artificial Religion argues that to fully understand our puzzling relation to AI, we must first look at the religious and existential background of our thinking about machines. Mapping some surprising connections between our history of thought about machines and Western religious narratives to political issues and existential human needs and aspirations, Mark Coeckelbergh offers a better understanding of our relationship to machines and why we think we need them at all.
"The book is unique in not just discussing the myth of AI in terms of its technical limitations and the power of Big Tech but also revealing the deeper cultural “grammar” of AI—that is, the religious patterns of thinking and existential aspirations that are often not visible but still haunt Western thinking and shape its technological culture. Moreover, this is done in a way that sheds critical light on the power of AI."]]></description>
<dc:subject>to:NB books:noted artificial_intelligence books:suggest_to_library</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:59520017a464/</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:artificial_intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:suggest_to_library"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://faculty.washington.edu/yenchic/short_note/note_MoM.pdf">
    <title>A short note on the median-of-means estimator (Yen-Chi Chen, 2020)</title>
    <dc:date>2026-04-23T16:43:42+00:00</dc:date>
    <link>https://faculty.washington.edu/yenchic/short_note/note_MoM.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Very nice.]]></description>
<dc:subject>to:NB have_read statistics heavy_tails estimation empirical_processes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ee5135168ce3/</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:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:heavy_tails"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:estimation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:empirical_processes"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2604.19560">
    <title>[2604.19560] Separating Geometry from Probability in the Analysis of Generalization</title>
    <dc:date>2026-04-22T20:22:34+00:00</dc:date>
    <link>https://arxiv.org/abs/2604.19560</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The goal of machine learning is to find models that minimize prediction error on data that has not yet been seen. Its operational paradigm assumes access to a dataset S and articulates a scheme for evaluating how well a given model performs on an arbitrary sample. The sample can be S (in which case we speak of ``in-sample'' performance) or some entirely new S′ (in which case we speak of ``out-of-sample'' performance). Traditional analysis of generalization assumes that both in- and out-of-sample data are i.i.d.\ draws from an infinite population. However, these probabilistic assumptions cannot be verified even in principle. This paper presents an alternative view of generalization through the lens of sensitivity analysis of solutions of optimization problems to perturbations in the problem data. Under this framework, generalization bounds are obtained by purely deterministic means and take the form of variational principles that relate in-sample and out-of-sample evaluations through an error term that quantifies how close out-of-sample data are to in-sample data. Statistical assumptions can then be used \textit{ex post} to characterize the situations when this error term is small (either on average or with high probability)."]]></description>
<dc:subject>to:NB to_read recht.benjamin raginsky.maxim learning_theory optimization via:mraginsky to_teach:childs_garden_of_statistical_learning_theory straight_into_my_veins interpolation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:963cc7aaa897/</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:recht.benjamin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:raginsky.maxim"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:mraginsky"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:childs_garden_of_statistical_learning_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:straight_into_my_veins"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:interpolation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613(26)00052-5">
    <title>Dependency syntax as the simplest theory of grammar: Trends in Cognitive Sciences</title>
    <dc:date>2026-04-21T13:17:07+00:00</dc:date>
    <link>https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613(26)00052-5</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The syntax of human languages has long been argued to be complex and even unlearnable from the input alone. However, the success of large language models (LLMs) has challenged this idea. I argue for a simple view of syntax, where the syntax of a language is just the set of dependency rules, with no phrase structure or transformation rules—constructs central to Chomsky’s transformational grammar. This approach accounts for diverse phenomena in human language processing and explains crosslinguistic word order universals. Moreover, it better explains human data for cases that differentiate these accounts and eliminates the syntax learnability problem. I speculate that LLMs, similar to children, learn the dependency grammar from linguistic patterns, leading to their impressive syntactic competence."]]></description>
<dc:subject>to:NB linguistics grammar_induction via:rvenkat</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6de20ecece1a/</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:linguistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:grammar_induction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:rvenkat"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.journals.uchicago.edu/doi/10.1086/724447">
    <title>On the Ecological and Internal Rationality of Bayesian Conditionalization and Other Belief Updating Strategies | The British Journal for the Philosophy of Science: Vol 77, No 1</title>
    <dc:date>2026-04-18T22:13:36+00:00</dc:date>
    <link>https://www.journals.uchicago.edu/doi/10.1086/724447</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["According to Bayesians, agents should respond to evidence by conditionalizing their prior degrees of belief on what they learn. A major aim of this article is to demonstrate that there are common scenarios where Bayesian conditionalization is less rational—from both an ecological and an internal perspective—than other theoretically well-motivated belief updating strategies, even in simple situations and even for an ‘ideal’ agent who is computationally unbounded. The examples also serve to demarcate the conditions under which Bayesian conditionalization may be expected to be ecologically optimal. A second aim of the article is to argue for a broader notion of rationality than what is typically assumed in formal epistemology. On this broader understanding of rationality, classical decision theoretic principles such as expected utility maximization play a less important role."]]></description>
<dc:subject>to:NB epistemology bayesianism rationality</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c9a42dd78d1c/</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:epistemology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bayesianism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:rationality"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.journals.uchicago.edu/doi/10.1086/723623">
    <title>Cascade Versus Mechanism: The Diversity of Causal Structure in Science | The British Journal for the Philosophy of Science: Vol 77, No 1</title>
    <dc:date>2026-04-18T22:12:54+00:00</dc:date>
    <link>https://www.journals.uchicago.edu/doi/10.1086/723623</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["According to mainstream philosophical views causal explanation in biology and neuroscience is mechanistic. As the term ‘mechanism’ gets regular use in these fields it is unsurprising that philosophers consider it important to scientific explanation. What is surprising is that they consider it the only causal term of importance. This article provides an analysis of a new causal concept—it examines the cascade concept in science and the causal structure it refers to. I argue that this concept is importantly different from the notion of mechanism and that this difference matters for our understanding of causation and explanation in science."]]></description>
<dc:subject>to:NB philosophy_of_science explanation_by_mechanisms neuroscience</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:513d062dbd7b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:explanation_by_mechanisms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neuroscience"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.journals.uchicago.edu/doi/10.1086/724448">
    <title>When Is Similarity-Biased Social Learning Adaptive? | The British Journal for the Philosophy of Science: Vol 77, No 1</title>
    <dc:date>2026-04-18T22:12:08+00:00</dc:date>
    <link>https://www.journals.uchicago.edu/doi/10.1086/724448</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Some cultural evolution theorists claim that humans tend to imitate the traits of people who are similar to themselves—men tend to imitate men, women tend to imitate women, and so on. These theorists further suggest that selection has shaped human psychology to attend to similarities and weigh them when learning from others. The argument typically works like this: If similar people face similar problems, then learning from those people can ensure humans learn the most relevant information to solve problems they will face. Little formal evolutionary modelling has explored the conditions under which this argument is valid. This article develops a series of models to answer this question. The general insight is that the viability of the evolutionary argument depends largely on what we assume the function of social roles to be. If, as is the default view in the cultural evolution literature, social roles facilitate coordination, then the model is not very robust with respect to the initial conditions, parameter settings, or population structure. However, if social roles facilitate the division of labour, then similarity-biased learning evolves under a wide range of conditions. These results can improve our understanding of the origins of inequality. Some philosophers have proposed evolutionary bargaining models as potential explanations for inequality. These models make frequent use of similarity-biased learning assumptions. I suggest some ways to improve the research programme on bargaining models in light of these results."]]></description>
<dc:subject>to:NB cultural_evolution inequality</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:9e68f53b9a9f/</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:cultural_evolution"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:inequality"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.journals.uchicago.edu/doi/10.1086/724449">
    <title>In Defence of Science: Two Ways to Rehabilitate Reichenbach’s Vindication of Induction | The British Journal for the Philosophy of Science: Vol 77, No 1</title>
    <dc:date>2026-04-18T22:10:53+00:00</dc:date>
    <link>https://www.journals.uchicago.edu/doi/10.1086/724449</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Confronted with the problem of induction, Reichenbach accepts that we cannot justify that induction is reliable. He tries to solve the problem by proving a weaker proposition: that induction is an optimal method of prediction, because it is guaranteed not to be worse and may be better than any alternative. Regarding the most serious objection to his approach, Reichenbach himself hints at an answer without spelling it out. In this article, I will argue that there are two workable strategies to rehabilitate Reichenbach’s account. The first leads to the widely discussed method of meta-induction, as proposed by Schurz. The second strategy has not been suggested thus far. I will develop the second strategy and argue for it being, in some respects, superior to the first and closer to Reichenbach’s own position. The strategy is based on Reichenbach’s idea that the inductive straight rule is not only applicable on the object but also on the method level. He does not spell out how exactly this insight is supposed to save his account. But he seems to assume that nothing more than the straight rule and the different levels of its application are needed for this purpose. The strategy introduced in this article illustrates that this assumption is correct."]]></description>
<dc:subject>to:NB philosophy_of_science induction reichenbach.hans</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:14188e6b7edb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:induction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:reichenbach.hans"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/core/elements/boolean-networks-as-predictive-models-of-emergent-biological-behaviors/0D2383F0D64543A77892CEBD5C6A964B">
    <title>Boolean Networks as Predictive Models of Emergent Biological Behaviors</title>
    <dc:date>2026-04-17T03:18:43+00:00</dc:date>
    <link>https://www.cambridge.org/core/elements/boolean-networks-as-predictive-models-of-emergent-biological-behaviors/0D2383F0D64543A77892CEBD5C6A964B</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Interacting biological systems at all organizational levels display emergent behavior. Modeling these systems is made challenging by the number and variety of biological components and interactions – from molecules in gene regulatory networks to species in ecological networks – and the often-incomplete state of system knowledge, such as the unknown values of kinetic parameters for biochemical reactions. Boolean networks have emerged as a powerful tool for modeling these systems. This Element provides a methodological overview of Boolean network models of biological systems. After a brief introduction, the authors describe the process of building, analyzing, and validating a Boolean model. They then present the use of the model to make predictions about the system's response to perturbations and about how to control its behavior. The Element emphasizes the interplay between structural and dynamical properties of Boolean networks and illustrates them in three case studies from disparate levels of biological organization."]]></description>
<dc:subject>to:NB biochemical_networks of_course_its_really_a_spin_glass books:noted downloaded</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:98c166445d04/</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:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:of_course_its_really_a_spin_glass"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:downloaded"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://doi.org/10.1017/9781009029346">
    <title>Scientific Models and Decision Making</title>
    <dc:date>2026-04-17T02:56:41+00:00</dc:date>
    <link>https://doi.org/10.1017/9781009029346</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This Element introduces the philosophical literature on models, with an emphasis on normative considerations relevant to models for decision-making. Chapter 1 gives an overview of core questions in the philosophy of modeling. Chapter 2 examines the concept of model adequacy for purpose, using three examples of models from the atmospheric sciences to describe how this sort of adequacy is determined in practice. Chapter 3 explores the significance of using models that are not adequate for purpose, including the purpose of informing public decisions. Chapter 4 provides a basic framework for values in modelling, using a case study to highlight the ethical challenges in building models for decision making. It concludes by establishing the need for strategies to manage value judgments in modelling, including the potential for public participation in the process."]]></description>
<dc:subject>to:NB books:noted philosophy_of_science modeling political_philosophy science_as_a_social_process</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6cc19b65d04c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:political_philosophy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:science_as_a_social_process"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://digressionsimpressions.substack.com/p/scientific-models-and-political-decision">
    <title>Scientific Models and Political Decision-Making (on Winsberg &amp; Harvard)</title>
    <dc:date>2026-04-17T02:50:00+00:00</dc:date>
    <link>https://digressionsimpressions.substack.com/p/scientific-models-and-political-decision</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>have_read book_reviews science_as_a_social_process tracked_down_references</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5848fe2cc01a/</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:book_reviews"/>
	<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:tracked_down_references"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.pnas.org/doi/abs/10.1073/pnas.2021865119">
    <title>One model for the learning of language | PNAS</title>
    <dc:date>2026-04-17T02:05:27+00:00</dc:date>
    <link>https://www.pnas.org/doi/abs/10.1073/pnas.2021865119</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A major goal of linguistics and cognitive science is to understand what class of learning systems can acquire natural language. Until recently, the computational requirements of language have been used to argue that learning is impossible without a highly constrained hypothesis space. Here, we describe a learning system that is maximally unconstrained, operating over the space of all computations, and is able to acquire many of the key structures present in natural language from positive evidence alone. We demonstrate this by providing the same learning model with data from 74 distinct formal languages which have been argued to capture key features of language, have been studied in experimental work, or come from an interesting complexity class. The model is able to successfully induce the latent system generating the observed strings from small amounts of evidence in almost all cases, including for regular (e.g., an, , and 
 ), context-free (e.g., 
 , and 
 ), and context-sensitive (e.g., 
 , and xx) languages, as well as for many languages studied in learning experiments. These results show that relatively small amounts of positive evidence can support learning of rich classes of generative computations over structures. The model provides an idealized learning setup upon which additional cognitive constraints and biases can be formalized."]]></description>
<dc:subject>to:NB grammar_induction color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8107fdce20d9/</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:grammar_induction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://henrich.fas.harvard.edu/sites/g/files/omnuum5811/files/henrich/files/hong_henrich_-_2021_-_the_cultural_evolution_of_epistemic_practices.pdfd">
    <title>The Cultural Evolution of Epistemic Practices: The case of Diviniation</title>
    <dc:date>2026-04-16T17:38:56+00:00</dc:date>
    <link>https://henrich.fas.harvard.edu/sites/g/files/omnuum5811/files/henrich/files/hong_henrich_-_2021_-_the_cultural_evolution_of_epistemic_practices.pdfd</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Although a substantial literature in anthropology and comparative religion explores
divination across diverse societies and back into history, little research has integrated
the older ethnographic and historical work with recent insights on human learning,
cultural transmission, and cognitive science. Here we present evidence showing that
divination practices are often best viewed as an epistemic technology, and we formally model the scenarios under which individuals may overestimate the efficacy of
divination that contribute to its cultural omnipresence and historical persistence. We
found that strong prior belief, underreporting of negative evidence, and misinferring
belief from behavior can all contribute to biased and inaccurate beliefs about the
effectiveness of epistemic technologies. We finally suggest how scientific epistemology, as it emerged in Western societies over the past few centuries, has influenced
the importance and cultural centrality of divination practices."]]></description>
<dc:subject>to:NB divination superstition cultural_evolution epistemology via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:55ac0f0a6d48/</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:divination"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:superstition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cultural_evolution"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epistemology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.academia.edu/106616966/Landemore_Can_AI_bring_deliberation_to_the_masses">
    <title>Landemore: Can AI bring deliberation to the masses</title>
    <dc:date>2026-04-16T17:33:41+00:00</dc:date>
    <link>https://www.academia.edu/106616966/Landemore_Can_AI_bring_deliberation_to_the_masses</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A core problem in deliberative democracy is the tension between two seemingly equally important conditions of democratic legitimacy: deliberation, on the one hand, and mass participation, on the other. Might artificial intelligence help bring quality deliberation to the masses? The answer is a qualified yes. The chapter first examines the conundrum in deliberative democracy around the trade-off between deliberation and mass participation by returning to the seminal debate between Joshua Cohen and Jürgen Habermas. It then turns to an analysis of the 2019 French Great National Debate, a low-tech attempt to involve millions of French citizens in a two-month-long structured exercise of collective deliberation. Building on the shortcomings of this process, the chapter then considers two different visions for an algorithm-powered form of mass deliberation-Mass Online Deliberation (MOD), on the one hand, and Many Rotating Mini-publics (MRMs), on the other-theorizing various ways artificial intelligence could play a role in them. To the extent that artificial intelligence makes the possibility of either vision more likely to come to fruition, it carries with it the promise of deliberation at the very large scale."

--- Can't find this anywhere except this ridiculous parasitic site...]]></description>
<dc:subject>to:NB democracy large_language_models_(so_called) deliberative_democracy landemore.helene via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:046e2daa4d7c/</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:democracy"/>
	<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:deliberative_democracy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:landemore.helene"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://theoryandpractice.org/2024/10/Yes,%20we%20did%20discover%20the%20Higgs!/">
    <title>Yes, we did discover the Higgs! - Theory And Practice</title>
    <dc:date>2026-04-16T17:30:22+00:00</dc:date>
    <link>https://theoryandpractice.org/2024/10/Yes,%20we%20did%20discover%20the%20Higgs!/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>cranmer.kyle particle_physics hypothesis_testing statistics philosophy_of_science via:? sociology_of_science science_as_a_social_process have_read to:blog</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:86fa85118401/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cranmer.kyle"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:particle_physics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hypothesis_testing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sociology_of_science"/>
	<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:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:blog"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2410.18858">
    <title>[2410.18858] Bilinear Sequence Regression: A Model for Learning from Long Sequences of High-dimensional Tokens</title>
    <dc:date>2026-04-16T17:13:09+00:00</dc:date>
    <link>https://arxiv.org/abs/2410.18858</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Current progress in artificial intelligence is centered around so-called large language models that consist of neural networks processing long sequences of high-dimensional vectors called tokens. Statistical physics provides powerful tools to study the functioning of learning with neural networks and has played a recognized role in the development of modern machine learning. The statistical physics approach relies on simplified and analytically tractable models of data. However, simple tractable models for long sequences of high-dimensional tokens are largely underexplored. Inspired by the crucial role models such as the single-layer teacher-student perceptron (aka generalized linear regression) played in the theory of fully connected neural networks, in this paper, we introduce and study the bilinear sequence regression (BSR) as one of the most basic models for sequences of tokens. We note that modern architectures naturally subsume the BSR model due to the skip connections. Building on recent methodological progress, we compute the Bayes-optimal generalization error for the model in the limit of long sequences of high-dimensional tokens, and provide a message-passing algorithm that matches this performance. We quantify the improvement that optimal learning brings with respect to vectorizing the sequence of tokens and learning via simple linear regression. We also unveil surprising properties of the gradient descent algorithms in the BSR model."]]></description>
<dc:subject>to:NB large_language_models_(so_called) neural_networks of_course_its_really_a_spin_glass high-dimensional_statistics zeborova.lenka</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:01772ebb1064/</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:neural_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:of_course_its_really_a_spin_glass"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:high-dimensional_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:zeborova.lenka"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.mariangoodman.com/exhibitions/agnieszka-kurant-recursion-new-york/">
    <title>Agnieszka Kurant: Recursion, Marian Goodman Gallery New York</title>
    <dc:date>2026-04-16T16:52:33+00:00</dc:date>
    <link>https://www.mariangoodman.com/exhibitions/agnieszka-kurant-recursion-new-york/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[---- This actually looks cool.]]></description>
<dc:subject>art artificial_life artificial_intelligence via:mraginsky</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b21da58d1da4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:artificial_life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:artificial_intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:mraginsky"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://direct.mit.edu/books/monograph/5848/AI-amp-IAn-Intellectual-History-of-Artificial">
    <title>AI &amp; I: An Intellectual History of Artificial Intelligence | Books Gateway | MIT Press</title>
    <dc:date>2026-04-16T16:45:59+00:00</dc:date>
    <link>https://direct.mit.edu/books/monograph/5848/AI-amp-IAn-Intellectual-History-of-Artificial</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A concise and illuminating history of the field of artificial intelligence from one of its earliest and most respected pioneers.
"AI & I is an intellectual history of the field of artificial intelligence from the perspective of one of its first practitioners, Eugene Charniak. Charniak entered the field in 1967, roughly 12 years after AI's founding, and was involved in many of AI's formative milestones. In this book, he traces the trajectory of breakthroughs and disappointments of the discipline up to the current day, clearly and engagingly demystifying this oft revered and misunderstood technology. His argument is controversial but well supported: that classical AI has been almost uniformly unsuccessful and that the modern deep learning approach should be viewed as the foundation for all the exciting developments that are to come.
"Written for the scientifically educated layperson, this book chronicles the history of the field of AI, starting with its origin in 1956, as a topic for a small academic workshop held at Dartmouth University. From there, the author covers reasoning and knowledge representation, reasoning under uncertainty, chess, computer vision, speech recognition, language acquisition, deep learning, and learning writ large. Ultimately, Charniak takes issue with the controversy of AI—the fear that its invention means the end of jobs, creativity, and potentially even humans as a species—and explains why such concerns are unfounded. Instead, he believes that we should embrace the technology and all its potential to benefit society."

--- Charniak's statistical language processing book from the 1990s is great so I'm excited for this.]]></description>
<dc:subject>in_NB books:noted downloaded to_read artificial_intelligence machine_learning charniak.eugene</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ae501489213b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:downloaded"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:artificial_intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:machine_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:charniak.eugene"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/core/journals/philosophy-of-science/article/parsimony-and-overfitting/B6A58202B75D1099BB22A95EDE1F8F58?WT.mc_id=New%2520Cambridge%2520Alert%2520-%2520Articles">
    <title>Parsimony and Overfitting | Philosophy of Science | Cambridge Core</title>
    <dc:date>2026-04-16T16:06:31+00:00</dc:date>
    <link>https://www.cambridge.org/core/journals/philosophy-of-science/article/parsimony-and-overfitting/B6A58202B75D1099BB22A95EDE1F8F58?WT.mc_id=New%2520Cambridge%2520Alert%2520-%2520Articles</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Philosophers often defend appeals to parsimony by invoking its central role in science. I argue that this move fails once we distinguish between two uses of parsimony: non-ideal and ideal. Non-ideal parsimony enjoys strong inductive support in science, since complex models are prone to overfit to predictively irrelevant noise. But philosophical data aren’t significantly noisy in the relevant sense: when our intuitions are unreliable, their unreliability typically reflects systematic bias rather than noise, which parsimony doesn’t mitigate. Philosophers therefore need ideal parsimony, which finds only weak support from science. Thus, the scientific analogy cannot vindicate the philosopher’s use of parsimony."]]></description>
<dc:subject>occams_razor philosophy_of_science in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b7f72d5e442f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:occams_razor"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://direct.mit.edu/books/monograph/6116/The-Great-Energy-TransitionAmerica-from-1876-to">
    <title>The Great Energy Transition: America from 1876 to 1929 | Books Gateway | MIT Press</title>
    <dc:date>2026-04-16T13:16:40+00:00</dc:date>
    <link>https://direct.mit.edu/books/monograph/6116/The-Great-Energy-TransitionAmerica-from-1876-to</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["How new forms of energy transformed every aspect of American life in a span of 50 years, from 1876 to 1929—and how it seeded our current polarization.

"The era of reform. The Gilded Age. The Progressive Era. What historians often divide into discrete eras was one period of profound change: a massive, multipronged energy transition. Oil, gas, and electricity were woven into a culture that had to heal sectional differences after the Civil War, absorb an enormous influx of immigrants, shift from a rural to an urban society, and adopt a scientific understanding of nature.
"Every job, business, house, and street underwent a transformation so rapid and radical that Americans simply could not grasp the larger pattern. The concepts of “technology” and an “energy transition” had yet to emerge, and observers struggled to understand their experiences using inadequate terms such as “kaleidoscopic change,” “applied science,” and “the machine age.” In The Great Energy Transition, David Nye documents this transformation—and explains our failure to see it for what it was.
"In this disorienting transformation, Nye locates the roots of today’s cultural polarization. The great energy transition accelerated demographic and economic trends, including higher wages, increasing longevity, the commodification of experience, engineering nature, corporatism, urbanization, resistance to science, and racial segregation. At the same time, the book points to the innovations and institutions that held the country together, from national parks and monuments to mass consumption and newly invented media events."]]></description>
<dc:subject>to:NB books:noted to_read american_history 19th_century_history 20th_century_history great_transformation the_present_before_it_was_widely_distributed re:the_singularity_in_our_past_light-cone books:suggest_to_library</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e461d24ea7cb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:american_history"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:19th_century_history"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:20th_century_history"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:great_transformation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:the_present_before_it_was_widely_distributed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:the_singularity_in_our_past_light-cone"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:suggest_to_library"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/core/journals/philosophy-of-science/article/abs/carnapian-inductive-logic-for-exponential-smoothing/84DD31142459DFD0289CCE8915E79952?WT.mc_id=New%2520Cambridge%2520Alert%2520-%2520Articles">
    <title>Carnapian Inductive Logic for Exponential Smoothing | Philosophy of Science | Cambridge Core</title>
    <dc:date>2026-04-15T13:16:02+00:00</dc:date>
    <link>https://www.cambridge.org/core/journals/philosophy-of-science/article/abs/carnapian-inductive-logic-for-exponential-smoothing/84DD31142459DFD0289CCE8915E79952?WT.mc_id=New%2520Cambridge%2520Alert%2520-%2520Articles</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper explores the inductive logic associated with exponential smoothing, the most widely used predictive rule that manifests the idea that more recent observations have a stronger influence on predictive probabilities than more remote ones. The main result shows that exponential smoothing can be derived from a set of plausible qualitative invariance assumptions about conditional probabilities. I discuss various aspects of the resulting inductive logic, including its connections to exchangeable processes, to Bayesian predictive inference and kernel methods in machine learning, as well as the philosophy of probabilistic invariance conditions and symmetries."]]></description>
<dc:subject>to:NB prediction statistics non-stationarity induction to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0b80819ed32c/</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:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:non-stationarity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:induction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://academic.oup.com/qje/article/141/2/1581/8435315?nbd=45497357721">
    <title>How Do You Identify a Good Manager?* | The Quarterly Journal of Economics | Oxford Academic</title>
    <dc:date>2026-04-13T19:40:03+00:00</dc:date>
    <link>https://academic.oup.com/qje/article/141/2/1581/8435315?nbd=45497357721</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We introduce and validate a novel approach to identifying good managers. In a preregistered lab experiment, we causally identify managerial contributions by randomly assigning managers to teams and controlling for individual skill. We find that manager contributions are crucial for team success, and that people who self-select into management roles perform worse than randomly assigned managers. Managerial performance is strongly predicted by economic decision-making skill but not by demographic characteristics. Two validation studies support our experimental results. Participants who succeed in the lab receive more real-world promotions and, in a separate study of retail store managers, skill measures strongly predict store sales. A one standard deviation increase in manager quality increases annual per store sales by US$4.1 million (25% increase). Selecting managers on skills rather than demographic characteristics or the desire to lead could substantially improve organizational performance."

--- I want to believe, so this needs to be treated with skepticism.]]></description>
<dc:subject>to:NB experimental_economics management economics to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a8ebab345ac7/</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:experimental_economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:management"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/xyz2tex/dot2tex">
    <title>GitHub - xyz2tex/dot2tex: Convert graphs generated by Graphviz to LaTeX friendly formats · GitHub</title>
    <dc:date>2026-04-13T02:10:05+00:00</dc:date>
    <link>https://github.com/xyz2tex/dot2tex</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- Works with modern python, unlike the version on CTAN.]]></description>
<dc:subject>latex</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c9722d3e683e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:latex"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://taggart-tech.com/reckoning/">
    <title>I used AI. It worked. I hated it.: Taggart Tech</title>
    <dc:date>2026-04-13T01:33:40+00:00</dc:date>
    <link>https://taggart-tech.com/reckoning/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>programming large_language_models_(so_called) via:absfac have_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8b2ce7afbe9a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:programming"/>
	<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:absfac"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://link.springer.com/article/10.1007/s11023-025-09755-8">
    <title>Decreasing Disruption and Increasing Concentration of Artificial Intelligence | Minds and Machines | Springer Nature Link</title>
    <dc:date>2026-04-09T13:32:20+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/s11023-025-09755-8</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper examines the development of artificial intelligence (AI) technologies from 1976 to 2020 and investigates the socio-economic factors driving its evolution. Using a large-scale dataset of AI patents and a novel measure called the pairwise disruption index (PDI), we trace the social drivers of AI disruption and investigate the underlying mechanisms. Our analysis focuses on three key dimensions of the knowledge base emphasized in innovation theories: government support, R&D capacity, and inventor human capital. Results reveal (1) a clear trend of AI technologies becoming concentrated within well-resourced institutions, consistent with the theory of intellectual monopoly capitalism; and (2) while both macro-level factors—such as government support and corporate R&D capabilities—and micro-level factors—such as R&D team size—contribute to this concentration, macro-level forces exert a stronger influence overall. Among them, government support has the most substantial impact, and organizational R&D capacity has become an increasingly dominant driver in recent years. This study provides a systematic assessment of the socio-economic forces shaping AI development, complements the intellectual monopoly theory, and highlights concerns over declining technological disruption and increasing concentration in the AI sector."

--- My skepticism starts with the fundamental measurement of "disruption" and goes on from there.  There is no reason this regressand should be linear in those regressor variables, and there is no comparison to other sectors / areas of technology.  Look for replication data and see if it might make a good problem set?]]></description>
<dc:subject>to:NB technological_change economics artificial_intelligence</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1b73bcd59027/</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:technological_change"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:artificial_intelligence"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://link.springer.com/article/10.1007/s11023-026-09767-y">
    <title>Using LLMs to Enhance Democracy | Minds and Machines | Springer Nature Link</title>
    <dc:date>2026-04-09T13:18:09+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/s11023-026-09767-y</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["LLMs are among the most advanced tools ever devised for understanding and generating natural language. Democratic deliberation and decision-making involve, at several distinct stages, the production and comprehension of language. So it is natural to ask whether our best linguistic tools might prove instrumental to one of our most important linguistic tasks involving language. Researchers and practitioners have recently asked whether LLMs can support democratic deliberation by leveraging abilities to summarise content, to aggregate opinions over summarised content, and to represent voters by predicting their preferences over unseen choices. In this paper, we assess whether using LLMs to perform these and related functions really advances the democratic values behind these experiments. We suggest that the record is mixed. In the presence of background inequality of power and resources, as well as deep moral and political disagreement, we should not use LLMs to automate non-instrumentally valuable components of the democratic process, nor should we be tempted to supplant fair and transparent decision-making procedures that are practically necessary to reconcile competing interests and values. However, while LLMs should be kept well clear of formal democratic decision-making processes, we think they can instead strengthen the informal public sphere—the arena that mediates between democratic governments and the polities that they serve, in which political communities seek information, form civic publics, and hold their leaders to account."]]></description>
<dc:subject>large_language_models_(so_called) democracy re:ai_as_a_social_technology in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d05ca1fd27a2/</dc:identifier>
<taxo:topics><rdf:Bag>	<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:democracy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:ai_as_a_social_technology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.aeaweb.org/articles?id=10.1257/aer.20240246">
    <title>Robust Misspecified Models - American Economic Association</title>
    <dc:date>2026-04-09T13:11:47+00:00</dc:date>
    <link>https://www.aeaweb.org/articles?id=10.1257/aer.20240246</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper studies which misspecified models are likely to persist when decision-makers compare them with competing models. The main result characterizes such models based on two features that can be derived from primitives: The model's asymptotic accuracy in predicting the equilibrium distribution of observed outcomes and the "tightness" of the prior around such equilibria. Misspecified models can be robust, persisting against any arbitrary competing model—including the true model—despite decision-makers observing an infinite amount of data. Moreover, simple misspecified models equipped with entrenched priors can be more robust than complex correctly specified models."]]></description>
<dc:subject>decision_theory misspecification re:bayes_as_evol statistics in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f59185400440/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:decision_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:misspecification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:bayes_as_evol"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.aeaweb.org/articles?id=10.1257/aer.20241056">
    <title>Similarity of Information and Collective Action - American Economic Association</title>
    <dc:date>2026-04-09T13:03:28+00:00</dc:date>
    <link>https://www.aeaweb.org/articles?id=10.1257/aer.20241056</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We study a canonical collective action game with incomplete information. Individuals attempt to coordinate to achieve a shared goal, while also facing a temptation to free-ride. More similar information can help them coordinate, but it can also exacerbate free-riding. Our main result shows that more similar information facilitates (impedes) achieving the common goal when it is sufficiently challenging (easy). We apply this insight to show why less powerful authoritarian governments may face larger protests if they restrict press freedom, when committee diversity is beneficial in costly voting, and when a more diverse community contributes more to public good provision."]]></description>
<dc:subject>collective_action collective_cognition re:democratic_cognition in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ab7e24fb22fd/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_action"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.ucs.org/resources/independent-science-initiative">
    <title>The Independent Science Initiative | Union of Concerned Scientists</title>
    <dc:date>2026-04-08T20:15:00+00:00</dc:date>
    <link>https://www.ucs.org/resources/independent-science-initiative</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Official-science in exile...]]></description>
<dc:subject>our_decrepit_institutions via:aeo</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3acd8e2f05a6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:our_decrepit_institutions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:aeo"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://direct.mit.edu/books/oa-edited-volume/6112/Dennett-s-Real-Patterns-in-Science-and-Nature">
    <title>Dennett's Real Patterns in Science and Nature | Books Gateway | MIT Press</title>
    <dc:date>2026-04-08T17:36:05+00:00</dc:date>
    <link>https://direct.mit.edu/books/oa-edited-volume/6112/Dennett-s-Real-Patterns-in-Science-and-Nature</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["How the concept of a pattern, as understood in information science and applied in contemporary AI, can address deep questions in science and philosophy.
"The explosive growth of AI and machine learning in recent decades is predicated on the recognition and exploitation of patterns in data. Of course, scientists have engaged in their own—less automated—processes of pattern recognition since the birth of science itself, and biological organisms evolved their own neural networks for pattern recognition long before people and their technology came along.
"In his seminal work, “Real Patterns,” philosopher and cognitive scientist Daniel Dennett laid out a road map for connecting the idea of “patterns” as understood by information theory to the practices of scientists and to our own cognitive capacity to model and predict the world around us. In this book—the first dedicated to the topic of real patterns—Tyler Millhouse, Steve Petersen, and Don Ross follow this road map. They explore the relevance of patterns to important aspects of both science and nature, including the emergence of high-level structure in physics, the nature of biological species, the measurement of welfare in economics, the evaluation of causal models, and the possibility of understanding in large neural networks."]]></description>
<dc:subject>books:noted philosophy_of_science emergence dennett.daniel_c. to_read downloaded in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3fd27bfc9cb4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:emergence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dennett.daniel_c."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:downloaded"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/core/journals/royal-institute-of-philosophy-supplements/article/abs/mind-as-a-control-system/501BF772FCAADCB00A1F576602E771F9">
    <title>The Mind as a Control System* | Royal Institute of Philosophy Supplements | Cambridge Core</title>
    <dc:date>2026-04-08T17:16:49+00:00</dc:date>
    <link>https://www.cambridge.org/core/journals/royal-institute-of-philosophy-supplements/article/abs/mind-as-a-control-system/501BF772FCAADCB00A1F576602E771F9</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This is not a scholarly research paper, but a ‘position paper’ outlining an approach to the study of mind which has been gradually evolving (at least in my mind) since about 1969 when I first become acquainted with work in Artificial Intelligence through Max Clowes. I shall try to show why it is more fruitful to construe the mind as a control system than as a computational system (although computation can play a role in control mechanisms)."

--- Preprint version [https://cogaffarchive.org/Aaron.Sloman_Mind.as.controlsystem/Aaron.Sloman_Mind.as.controlsystem.pdf] gives the following abstract:

"Many people who favour the design-based approach to the study of mind, including the author previously, have thought of the mind as a computational system, though they don’t all agree regarding the forms of computation required for mentality. Because of ambiguities in the notion of ’computation’ and also because it tends to be too closely linked to the concept of an algorithm, it is suggested in this paper that we should rather construe the mind (or an agent with a mind) as a control system involving many interacting control loops of various kinds, most of them implemented in high level virtual machines, and many of them hierarchically organised. (Some of the sub-processes are clearly computational in character, though not necessarily all.) A number of implications are drawn out, including the implication that there are many informational substates, some incorporating factual information, some control information, using diverse forms of representation. The notion of architecture, i.e. functional differentiation into interacting components, is explained, and the conjecture put forward that in order to account for the main characteristics of the human mind it is more important to get the architecture right than to get the mechanisms right (e.g. symbolic vs neural mechanisms). Architecture dominates mechanism"]]></description>
<dc:subject>philosophy_of_mind control_theory_and_control_engineering via:mraginsky in_NB cognitive_science</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3e58021c932d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:control_theory_and_control_engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:mraginsky"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2601.10825">
    <title>[2601.10825] Reasoning Models Generate Societies of Thought</title>
    <dc:date>2026-04-08T16:58:19+00:00</dc:date>
    <link>https://arxiv.org/abs/2601.10825</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Large language models have achieved remarkable capabilities across domains, yet mechanisms underlying sophisticated reasoning remain elusive. Recent reasoning models outperform comparable instruction-tuned models on complex cognitive tasks, attributed to extended computation through longer chains of thought. Here we show that enhanced reasoning emerges not from extended computation alone, but from simulating multi-agent-like interactions -- a society of thought -- which enables diversification and debate among internal cognitive perspectives characterized by distinct personality traits and domain expertise. Through quantitative analysis and mechanistic interpretability methods applied to reasoning traces, we find that reasoning models like DeepSeek-R1 and QwQ-32B exhibit much greater perspective diversity than instruction-tuned models, activating broader conflict between heterogeneous personality- and expertise-related features during reasoning. This multi-agent structure manifests in conversational behaviors, including question-answering, perspective shifts, and the reconciliation of conflicting views, and in socio-emotional roles that characterize sharp back-and-forth conversations, together accounting for the accuracy advantage in reasoning tasks. Controlled reinforcement learning experiments reveal that base models increase conversational behaviors when rewarded solely for reasoning accuracy, and fine-tuning models with conversational scaffolding accelerates reasoning improvement over base models. These findings indicate that the social organization of thought enables effective exploration of solution spaces. We suggest that reasoning models establish a computational parallel to collective intelligence in human groups, where diversity enables superior problem-solving when systematically structured, which suggests new opportunities for agent organization to harness the wisdom of crowds."]]></description>
<dc:subject>to:NB to_read artificial_intelligence large_language_models_(so_called) ensemble_methods evans.james kith_and_kin</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f2cf63b3474b/</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: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:ensemble_methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evans.james"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/core/journals/philosophy-of-science/article/measured-inference-scales-statistics-and-scientific-inference/527F6793A1C954F01A321E72F780E931?WT.mc_id=New%2520Cambridge%2520Alert%2520-%2520Issues">
    <title>Measured Inference: Scales, Statistics, and Scientific Inference | Philosophy of Science | Cambridge Core</title>
    <dc:date>2026-04-08T16:56:58+00:00</dc:date>
    <link>https://www.cambridge.org/core/journals/philosophy-of-science/article/measured-inference-scales-statistics-and-scientific-inference/527F6793A1C954F01A321E72F780E931?WT.mc_id=New%2520Cambridge%2520Alert%2520-%2520Issues</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Despite the recent “epistemic turn” in the philosophy of measurement, philosophers have ignored a nearly 80-year controversy about the relationship between statistical inference and measurement theory. Some scholars maintain that measurement theory places no constraints on statistics, whereas others argue that the measurement scale (e.g., ordinal or interval) of one’s data determines which statistical methods are “permissible.” I defend an intermediate position: Even if existing measurement theory were irrelevant to statistical inference, it would be critical for scientific inference, which requires connecting statistical hypotheses to broader research hypotheses."]]></description>
<dc:subject>to:NB measurement philosophy_of_science statistics mayo-wilson.conor to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:cb7083aa4fd5/</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:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mayo-wilson.conor"/>
	<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/philosophy-of-science/article/we-should-not-align-quantitative-measures-with-stakeholder-values/1C7DBA5E3D5904AB023268C97EACB0F2?WT.mc_id=New%2520Cambridge%2520Alert%2520-%2520Issues">
    <title>We Should not Align Quantitative Measures with Stakeholder Values | Philosophy of Science | Cambridge Core</title>
    <dc:date>2026-04-08T16:56:11+00:00</dc:date>
    <link>https://www.cambridge.org/core/journals/philosophy-of-science/article/we-should-not-align-quantitative-measures-with-stakeholder-values/1C7DBA5E3D5904AB023268C97EACB0F2?WT.mc_id=New%2520Cambridge%2520Alert%2520-%2520Issues</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["There is a growing consensus among philosophers that quantifying value-laden concepts can be epistemically successful and politically legitimate if all value-laden choices in the process of quantification are aligned with stakeholder values. I argue that proponents of this alignment approach have failed to argue for its basic premise: Successful quantification is sufficiently unconstrained to be achievable along multiple, stakeholder-specific pathways. I then challenge this premise by considering a rare example of successful value-laden quantification in seismology, in which stakeholder values had to be disregarded from measure design and testing. The example motivates my contention that value alignment is not a workable source of political legitimacy for successful programs of quantification."

]]></description>
<dc:subject>to:NB measurement science_as_a_social_process philosophy_of_science</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0cab44853037/</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:science_as_a_social_process"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/core/journals/philosophy-of-science/article/science-without-trust/13A5FD15D5ADFF4C67DDC1DF8D8FEB6C?WT.mc_id=New%2520Cambridge%2520Alert%2520-%2520Issues">
    <title>Science Without Trust | Philosophy of Science | Cambridge Core</title>
    <dc:date>2026-04-08T16:55:29+00:00</dc:date>
    <link>https://www.cambridge.org/core/journals/philosophy-of-science/article/science-without-trust/13A5FD15D5ADFF4C67DDC1DF8D8FEB6C?WT.mc_id=New%2520Cambridge%2520Alert%2520-%2520Issues</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["It is often said that successful scientific research must be built on trust. Focusing on the alleged necessity of trust for successful scientific communication and thus for scientific cooperation (which underlies much of contemporary science), I argue that science mustn’t be built on trust. Appearances to the contrary come from a failure to distinguish different attitudes toward scientists’ testimony, in particular, trusting and relying on other scientists. This article proposes an account of scientific reliance and explains how it differs from scientific trust; it also shows why this distinction matters for science."]]></description>
<dc:subject>to:NB philosophy_of_science science_as_a_social_process trust machery.edouard</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:baa0d82bef89/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:science_as_a_social_process"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:trust"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:machery.edouard"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/math/0504472">
    <title>[math/0504472] Szemerédi's regularity lemma revisited</title>
    <dc:date>2026-04-08T02:30:02+00:00</dc:date>
    <link>https://arxiv.org/abs/math/0504472</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Szemerédi's regularity lemma is a basic tool in graph theory, and also plays an important role in additive combinatorics, most notably in proving Szemerédi's theorem on arithmetic progressions . In this note we revisit this lemma from the perspective of probability theory and information theory instead of graph theory, and observe a variant of this lemma which introduces a new parameter F. This stronger version of the regularity lemma was iterated in a recent paper of the author to reprove the analogous regularity lemma for hypergraphs."

--- Re last tag, I ought to try to find time to think about this as a form of (approximate) statistical sufficiency, and/or the information bottleneck.]]></description>
<dc:subject>have_read tao.terence graph_theory information_theory probability coarse_graining sufficiency in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b71042d4baec/</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:tao.terence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graph_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:coarse_graining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sufficiency"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://direct.mit.edu/books/monograph/6030/Strange-AttractorThe-Hallucinatory-Life-of-Terence">
    <title>Strange Attractor: The Hallucinatory Life of Terence McKenna | Books Gateway | MIT Press</title>
    <dc:date>2026-04-04T03:34:28+00:00</dc:date>
    <link>https://direct.mit.edu/books/monograph/6030/Strange-AttractorThe-Hallucinatory-Life-of-Terence</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["An intellectual biography of one of the most celebrated and yet least understood figures of the late twentieth century, Terence McKenna.
"A stand-up philosopher who made a unique contribution to science, humanism, and the hidden arts, Terence McKenna (1946–2000) was the twentieth century’s psychedelic Renaissance man. Perfecting his rugged philosophy on the role of psychedelics in evolution, consciousness, and time, McKenna was a riotous charmer who stalked the shadows, but also sought the iridescence. More than twenty years since his untimely passing, McKenna has an enduring magnetism across the virtual pop stream, in pervasive digitization, and within social media networks. In Strange Attractor, the first biography of this enigmatic figure, Graham St John detects the signal behind the noise.
"This book is an engaging chronicle of the life, works, and legacy of this brazen adventurer of the inner and outer dimensions, whose weird intelligence has affected multitudes and whose spirit continues to haunt the present. It draws on original documents and letters, features fifty two rare photographs and artworks, and shares previously untold stories from over eighty people. Neither glorifying nor disparaging its subject, Strange Attractor will appeal to those interested in the evolution of a psychedelic intellectual, and to those for whom McKenna’s wisdom endures."]]></description>
<dc:subject>in_NB books:noted psychoceramica lives_of_the_scholars sort_of</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:70ad5101b1ce/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:psychoceramica"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:lives_of_the_scholars"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sort_of"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://ergosphere.blog/posts/the-machines-are-fine/">
    <title>The machines are fine. I'm worried about us.</title>
    <dc:date>2026-04-03T07:45:55+00:00</dc:date>
    <link>https://ergosphere.blog/posts/the-machines-are-fine/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>large_language_models_(so_called) science_as_a_social_process have_read via:kjhealy re:the_singer_of_tales_and_the_house_of_intellect</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0aa0e4be9ed7/</dc:identifier>
<taxo:topics><rdf:Bag>	<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:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:kjhealy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:the_singer_of_tales_and_the_house_of_intellect"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.aeaweb.org/articles?id=10.1257/aer.20240763">
    <title>Games on Multiplex Networks - American Economic Association</title>
    <dc:date>2026-03-31T18:08:27+00:00</dc:date>
    <link>https://www.aeaweb.org/articles?id=10.1257/aer.20240763</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We develop a simple multilayer network model in which agents allocate effort across layers with heterogeneous structures, subject to an aggregate effort constraint. Incentives are shaped by agents' network positions within each layer, and equilibrium behavior reflects both within- and cross-layer interactions. We analyze how shocks propagate through the network and characterize optimal targeting interventions. Our results show that effective policy design must account for effort allocation across layers. We also demonstrate that predictions from monolayer models can diverge sharply from those of multilayer models, underscoring the importance of accounting for network complexity in both empirical and policy analyses."]]></description>
<dc:subject>in_NB game_theory social_networks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2be7c9d94d49/</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:game_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_networks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2603.25568">
    <title>[2603.25568] Are LLMs Overkill for Databases?: A Study on the Finiteness of SQL</title>
    <dc:date>2026-03-31T13:10:48+00:00</dc:date>
    <link>https://arxiv.org/abs/2603.25568</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Translating natural language to SQL for data retrieval has become more accessible thanks to code generation LLMs. But how hard is it to generate SQL code? While databases can become unbounded in complexity, the complexity of queries is bounded by real life utility and human needs. With a sample of 376 databases, we show that SQL queries, as translations of natural language questions are finite in practical complexity. There is no clear monotonic relationship between increases in database table count and increases in complexity of SQL queries. In their template forms, SQL queries follow a Power Law-like distribution of frequency where 70% of our tested queries can be covered with just 13% of all template types, indicating that the high majority of SQL queries are predictable. This suggests that while LLMs for code generation can be useful, in the domain of database access, they may be operating in a narrow, highly formulaic space where templates could be safer, cheaper, and auditable."]]></description>
<dc:subject>to:NB large_language_models_(so_called) databases mimno.david re:gopnikism</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b8b2e634087e/</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:databases"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mimno.david"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:gopnikism"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2603.21687">
    <title>[2603.21687] MIRAGE: The Illusion of Visual Understanding</title>
    <dc:date>2026-03-30T18:05:57+00:00</dc:date>
    <link>https://arxiv.org/abs/2603.21687</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Multimodal AI systems have achieved remarkable performance across a broad range of real-world tasks, yet the mechanisms underlying visual-language reasoning remain surprisingly poorly understood. We report three findings that challenge prevailing assumptions about how these systems process and integrate visual information. First, Frontier models readily generate detailed image descriptions and elaborate reasoning traces, including pathology-biased clinical findings, for images never provided; we term this phenomenon mirage reasoning. Second, without any image input, models also attain strikingly high scores across general and medical multimodal benchmarks, bringing into question their utility and design. In the most extreme case, our model achieved the top rank on a standard chest X-ray question-answering benchmark without access to any images. Third, when models were explicitly instructed to guess answers without image access, rather than being implicitly prompted to assume images were present, performance declined markedly. Explicit guessing appears to engage a more conservative response regime, in contrast to the mirage regime in which models behave as though images have been provided. These findings expose fundamental vulnerabilities in how visual-language models reason and are evaluated, pointing to an urgent need for private benchmarks that eliminate textual cues enabling non-visual inference, particularly in medical contexts where miscalibrated AI carries the greatest consequence. We introduce B-Clean as a principled solution for fair, vision-grounded evaluation of multimodal AI systems."

!!!]]></description>
<dc:subject>to:NB large_language_models_(so_called) via:csantos chain-of-thought_(so_called)</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6967b297f42e/</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:via:csantos"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:chain-of-thought_(so_called)"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.tes.com/magazine/teaching-learning/general/uta-frith-interview-autism-not-spectrum">
    <title>Uta Frith interview: 'Autism is not a spectrum' | Tes</title>
    <dc:date>2026-03-30T01:56:50+00:00</dc:date>
    <link>https://www.tes.com/magazine/teaching-learning/general/uta-frith-interview-autism-not-spectrum</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>frith.uta autism interview neuropsychology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:258177d1aba9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:frith.uta"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:autism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:interview"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neuropsychology"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.sciencedirect.com/science/article/abs/pii/S0167268116301202?via%3Dihub">
    <title>OPEC, the Seven Sisters, and oil market dominance: An evolutionary game theory and agent-based modeling approach - ScienceDirect</title>
    <dc:date>2026-03-27T19:56:07+00:00</dc:date>
    <link>https://www.sciencedirect.com/science/article/abs/pii/S0167268116301202?via%3Dihub</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A methodological toolkit comprised of evolutionary game theory and agent-based modeling is used to study OPEC and the Seven Sisters as they struggled for control over global petroleum markets during the 1960s and 1970s. An evolutionary game theory model incorporates heterogeneous populations, energy-specific variables, and behavioral considerations to capture the fundamentals of the applied problem. An agent-based model is used to provide detailed results and demonstrate the importance of the natural resource to the outcome of the model."


]]></description>
<dc:subject>to:NB to_read economic_history agent-based_models evolutionary_game_theory via:aeo 20th_century_history</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ceba46bcf38f/</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:economic_history"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:agent-based_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evolutionary_game_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:aeo"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:20th_century_history"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://jbgruber.github.io/rollama/">
    <title>Communicate with Ollama to Run Large Language Models Locally • rollama</title>
    <dc:date>2026-03-26T16:16:04+00:00</dc:date>
    <link>https://jbgruber.github.io/rollama/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The goal of rollama is to wrap the Ollama API, which allows you to run different LLMs locally and create an experience similar to ChatGPT/OpenAI’s API. Ollama is very easy to deploy and handles a huge number of models. Checkout the project here: https://github.com/ollama/ollama.
"While there are several R packages for working with LLMs, rollama takes an opinionated approach centred on local, open-weight models: prioritising privacy, reproducibility, and ease of use for research tasks. The package and its learning materials are particularly focused on annotating text and images — making it a natural fit for (social) scientists who want to use LLMs without relying on proprietary APIs or sending sensitive data to third-party servers. It also offers deep integration with the Ollama ecosystem beyond just chat, including model management features like creating, copying, and pushing custom models."

--- Where "to_read" means "to install and play around with".  (Of course my 2017-vintage machine may groan and smoke...)]]></description>
<dc:subject>to:NB to_read large_language_models_(so_called) to_teach:statistics_and_generative_ai via:phnk</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:04a78f4bc198/</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:to_teach:statistics_and_generative_ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:phnk"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>