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Manikya Alister

@manikyaalister.bsky.social
471 followers 456 following 71 posts

Postdoc at UC Berkeley. Interested in all things social reasoning, cognition, modelling, and philosophy of science 🤓 manikyaalister.github.io

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Reposted by Manikya Alister
Joe Bak-Coleman @jbakcoleman.bsky.social · 07/10/2026
What truly grinds my gears is that climate scientist can spend 50 years building data-grounded models and need to frame their findings to avoid being called alarmist. At the same time x-risk cranks and CEOs can just pull P(doom) > 10% out of their ass and be taken seriously.
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Alejandro Fábregas-Tejeda @alejandrofabregastejeda.com · 11/08/2026
As simulations grow in complexity, a set of non-trivial issues emerge: diminished identifiability as free parameters become too numerous relative to exp. constraints & difficulties grasping causality. In our new 📄, we point to potential avenues to move #CellBio forward 👇 arxiv.org/abs/2608.06998
arxiv.org
Simulating is not always understanding: When model complexity obscures biology
In cell biology, computational models of biological systems range from minimal representations with a handful of parameters to whole-cell simulations tracking thousands of molecular species across a c...
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Manikya Alister @manikyaalister.bsky.social · 15/09/2026
Enjoyed working on this! Read below to learn how different types of consensus change in persuasiveness with increased repetitions 📈
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Kianté @kiante.bsky.social · 09/09/2026
A few years ago at MathPsych @singmann.bsky.social and I spoke about making an update. Well here it is new rtdists live on CRAN!
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William J. Brady @williambrady.bsky.social · 29/06/2026
In 2023 we showed how engagement-based algorithms exploit human social learning biases, distorting how we learn what's normal, common, or credible from each other ("Algorithm-mediated social learning," Trends in Cognitive Sciences). The obvious follow-up: where does generative AI fit in?
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Sam Gershman @gershbrain.bsky.social · 01/09/2026
If this makes you mad, submit your cognitive science research to @openmindjournal.bsky.social No APCs, no subscription fees. It's funded by university libraries, at low cost.
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Gaia Molinaro @gaiamolinaro.bsky.social · 28/08/2026
Why do some goals spread across people and generations, while others emerge only transiently? We argue that human goals are both drivers and products of cultural evolution: our individual pursuits are shaped by our cultural environments, and the goals we pursue in turn reshape those environments.
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Drew Engelhardt @amengel.bsky.social · 18/08/2026
Working on a dissertation in American political behavior and need money for survey research? Apply for a grant worth up to $15,000 from the Rapoport Family Foundation. They’re planning on awarding up to 15 grants this cycle. Deadline Oct. 21. www.rapoportfamilyfoundation.com/phdgrant
rapoportfamilyfoundation.com
Rapoport Doctoral Dissertation Grants
Supporting projects for PhD students doing political science research, democracy and social justice.
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Marius Mercier @mariusmercier.bsky.social · 30/03/2026
Thrilled to share that our new paper is now out in @cognitionjournal.bsky.social: "Who knows what? Bayesian Competence Inference guides Knowledge Attribution and Information Search," with @oliviermorin.bsky.social , @hugoreasoning.bsky.social & @tadegquillien.bsky.social! Link: tinyurl.com/ykyhxcc6
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Petter Törnberg @pettertornberg.com · 06/08/2026
We keep asking what social media does to voters. The wrong end of the pipe. Platforms don't just distribute politics — they teach political actors what kind of speech pays. So I measured the going rate: what X, Bluesky and Mastodon reward in public speech. 🧵 arxiv.org/abs/2607.04220
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Jared Moore @jaredlcm.bsky.social · 06/08/2026
Which LLMs tend to facilitate delusion-linked behaviors in realistic multi-turn conversations? We tested 14 models with DelusionEval and found that every evaluated LLM exhibited some of these behaviors, with large differences across categories and model families. 🧵
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Minds, Machines, and Brains (MMB) @mmb-journal.bsky.social · 02/08/2026
Hello world! 👋 We’re Minds, Machines, and Brains (MMB) 👤🤖🧠 a new open access journal from @mitpress.bsky.social exploring the principles of intelligence and cognition across natural and artificial minds. Submissions open this Fall! 🔗 direct.mit.edu/mmb
direct.mit.edu
Minds, Machines, and Brains | MIT Press
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Paul Smaldino @psmaldino.bsky.social · 30/07/2026
A Demographic Theory of Similarity-Biased Social Learning. Now out in PNAS, led by the incomparable @apvelilla.bsky.social . We use modeling to explore conditions for the (cultural) evolution of parochial (and anti-parochial) learning biases. www.pnas.org/doi/10.1073/...
Humans adapt by learning from others, but risk acquiring maladaptive behaviors when learning from individuals facing different conditions. We present an evolutionary model exploring how individuals use social identity markers to select learning targets in diverse populations. Learners evolve biases that track reliable information sources: preferring similar individuals when in-group knowledge is adaptive, and dissimilar individuals when in-group behaviors are disadvantageous. Our framework demonstrates that this adaptive bias makes social learning viable even when it is costlier than individual learning. Additionally, we show that a minimum threshold of reliable cultural information is required for social learning to spread, providing a mathematical explanation for how demographic diversity drives population-level patterns of cultural segregation and assimilation.
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ARC Tracker @arc-tracker.bsky.social · 22/07/2026
The ARC's new grant scheme table, in case you don't want to read through the details (which are here: www.arc.gov.au/system/files...)
Screenshot of a table (black text on variously shaded backgrounds) showing some main aspects of the new grant schemes announced by the ARC.
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Max Taylor-Davies @maxtaylordavies.bsky.social · 29/07/2026
🍄 New paper out in PLOS Computational Biology! 🍄 I, and others, have previously drawn a comparison between the classic “cost-avoidance” argument for social learning and the central idea of resource rationality... (1/8)
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Rafael M Batista @rafmbatista.bsky.social · 24/07/2026
Good morning #CogSci2026! I’ve got a poster to share with you this afternoon Come on by so we can meet and I can tell you about what I’ve working been with on (together w/ Tom Griffiths @cocoscilab.bsky.social ) rafaelmbatista.com/sycophantic-...
rafaelmbatista.com
A Rational Analysis of the Effects of Sycophantic AI
CogSci 2026 | Poster Session 3
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Rayna Tang @raynatang.bsky.social · 22/07/2026
Excited to share that my first-ever first-author paper (w/ @atabk.bsky.social @AngeliqueDelarazan @zreagh.bsky.social) is now out in PNAS! Building a story to link two objects boosts associative memory and inference, but not memory for the objects themselves. www.pnas.org/doi/10.1073/... 🧵
pnas.org
Active linking through narratives facilitates associative inference | PNAS
In daily life, we often draw inferences about novel associations from prior experiences. This ability, associative inference, is thought to be a ke...
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Robert Hawkins @rdhawkins.bsky.social · 23/07/2026
excited for the first #cogsci2026 poster session this afternoon! come by and chat with @ymacci.bsky.social @amirzur.bsky.social @clairebergey.bsky.social
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Tobias Gerstenberg @tobigerstenberg.bsky.social · 20/07/2026
The Causality in Cognition Lab is pumped for #CogSci2026 🇧🇷
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Erik Brockbank @erikbrockbank.bsky.social · 16/07/2026
Check out our preprint and/or come chat at #CogSci2026 next week to learn more! Poster during session 3 (P3-M-124) on Friday July 24. And stay tuned for full manuscript coming soon!
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Beth Clarke @bethclarke.bsky.social · 03/07/2026
Very excited to announce that I’ll be joining @zpid.bsky.social in Trier, Germany as a tenure-track Jun.-Prof. in Psychological Metascience in November! 🤸🏻‍♀️✨
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Bella Fascendini @bellafascendini.bsky.social · 26/06/2026
New paper! w/ @cocoscilab.bsky.social🧵Can large language models reason flexibly, or have they learned what reasoning looks like? We introduce a new paradigm to test this question—the riddle riddle—and find that humans and LLMs show opposite patterns of performance. 📜
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Georgia Turner @georgiaturner.bsky.social · 25/06/2026
🎉 Now published in Nature Communications! 🧠 We develop a novel computational reinforcement learning & habit model of Twitter data, and show that younger people & women are more behaviourally sensitive to 'Likes'. Read the paper: bit.ly/4f1vsKS
bit.ly
A computational model of reward learning and habits on social media - Nature Communications
The cognitive processes driving social media use could be key to understanding social media’s impact. Here, the authors develop a computational model of real-world social media behaviour, identifying ...
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Randall Munroe @xkcd.com · 24/06/2026
Sports Commentary xkcd.com/3262/
Comic. [A person and a second person with a ponytail sitting at a table with a screen showing a soccer game behind them.] PERSON 1: They could be in trouble. Over the last 36 years, they’ve gone 0 for 2 when they’ve scored in the 37th minute to lead 2-1 against a team whose country comes before theirs alphabetically. [caption] I wish sports commentators hadn’t discovered p-hacking.
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Aki Vehtari @avehtari.bsky.social · 23/06/2026
New paper "To select or not to select: predictively consistent priors instead of model selection" with Anna Elisabeth Riha, Leevi Lindgren, @davidkohns.bsky.social, @paulbuerkner.com arxiv.org/abs/2606.22850 Model selection is not a substitute for building good models in the first place 1/
To select or not to select: predictively
consistent priors instead of model selection

Anna Elisabeth Riha, Leevi Lindgren, David Kohns, Paul-Christian Bürkner, Aki Vehtari

Bayesian modelling workflows often consider multiple candidate models of varying complexity. Model selection is commonly used to navigate potential trade-offs between model complexity and generalisability to new data. We study when model selection is unnecessary or can even be harmful for predictive performance in finite data regimes and find that the need for selecting simpler models can depend on prior choice. We formalise predictively consistent priors, which keep prior predictive implications stable as model complexity increases. Across examples and numerical experiments, including adding covariates in linear and logistic regression, forward variable selection, and nonlinear modelling, flexible models with predictively consistent priors typically match or outperform selected simpler models in out-of-sample predictive performance. When selection helps, it can indicate poor joint prior implications, such as excessive prior mass on implausible predictive values. Based on our findings, we propose replacing the notion of sparsity or parsimony at the level of model components with specifying priors that remain sensible in predictive space as models become more complex.
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Andrew Perfors @perfors.net · 17/06/2026
... which I find heartwarming! But it also makes us vulnerable to deception; the bias is so strong that we have a hard time adjusting our inferences enough even when we have good reason to believe the information provider is being misleading. Kudos to @manikyaalister.bsky.social for awesome work!
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Andrew Perfors @perfors.net · 17/06/2026
So proud to share this work! A deep dive on how good people are at learning from others when we don't know if they're helpful (with cool models! and several experiments!). tl;dr: It's super hard and even optimal Bayesian models struggle. People have a strong bias to assume others are helpful...
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Per Engzell @pengzell.bsky.social · 17/06/2026
Society works because we assume most people are decent. Society occasionally fails for the same reason.
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Mike Le Pelley @mikelepelley.bsky.social · 09/06/2026
FIVE academic jobs currently open in the School of Psychology at UNSW Sydney, across a variety of areas; see links below. Please come and join us! It's an outstanding department with excellent people. tinyurl.com/4p6tp2j3 tinyurl.com/yxm565xs tinyurl.com/2u8m77z8 tinyurl.com/3wu9hnb5
tinyurl.com
Lecturer/Senior Lecturer/ Associate Professor - Psychology
A Lecturer (Level B) / Senior Lecturer (Level C) is expected to carry out activities to develop their scholarly research and professional activities both nationally and internationally and to contribu...
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Manikya Alister @manikyaalister.bsky.social · 09/06/2026
One of the first studies from my PhD is out now in JEP:G 🥳We tested whether people can infer the truth from teachers who were either helpful, misleading, or randomly sampling. With Keith Ransom and @perfors.net psycnet.apa.org/fulltext/202...
Title of paper: When a Helpful Bias Is Unhelpful: Limitations in Reasoning
About Random and Deliberately Misleading Evidence
Abstract: Social information aids learning: By making assumptions about other people’s knowledge and intentions,
people can draw strong and accurate inferences from limited data. In this study, we systematically tested
people’s ability to reason from information providers with different intentions. The task was an adaptation of
Shafto et al.’s (2014) rectangle game, where learners guessed a rectangle’s size and location based on
provided clues. We examined reasoning based on information from four types of providers: a helpful
provider, a provider who sampled randomly, and two misleading providers (who could mislead but not lie).
We also varied whether people were given a cover story describing the provider in advance or whether they
could infer how helpful a provider was based on what the provider shared. Participants learned efficiently
from helpful providers, aligning closely with the predictions of a normative Bayesian model, even without a
cover story. However, while people usually recognized unhelpful providers, they struggled to identify and
respond appropriately to misleading strategies. Overall, our results suggest a helpful bias: In our task,
participants assumed helpful intent unless given explicit feedback, and even then, they did not fully adjust in
line with Bayesian predictions. People also struggled to overcome this bias when learning from randomly
sampled information, especially when they had experience being an information provider themselves
(Experiment 3).
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Marius Mercier @mariusmercier.bsky.social · 04/06/2026
New paper accepted as a proceedings of the Cognitive Science Society: "Inferring arithmetic skills from speed and accuracy”! We tested whether people were optimal in their inference of others' math skills. doi.org/10.31234/osf...
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Berna Devezer @devezer.bsky.social · 25/05/2026
that's what we need. anyhow, if i studied this, i'd think more carefully about how to choose a meaningful control group. and try to have a good theory of under what conditions people seek advice, from whom, with what expectations, and when they actually take it.
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Kartik Chandra @kartikchandra.bsky.social · 19/05/2026
I wrote a tiny LaTeX package that creates a wordcount environment. You can use it to auto-print word counts, when paring down text to meet limits. For example: \begin{wordcount}{\abstractcount} This is my abstract. \end{wordcount} \textbf{Word count:} \abstractcount % prints 4 Source code below…
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Courtney Hilton @courtneybhilton.bsky.social · 12/05/2026
Woah! @zotero.org version 9 now has a 'read aloud' feature, where an artificial voice can read articles to you. The premium voices option is actually pretty decent. I can imagine myself using this to listen to articles on the tram in to work.
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M. S. AtKisson @igrrrl.bsky.social · 08/05/2026
Out in Science. Years ago I recall another paper that said how fields make bigger leaps in the few years after one of the "greats" retires. www.sciencemagazinedigital.org/sciencemagaz...
sciencemagazinedigital.org
Science Magazine - Aging and the narrowing of scientific innovation
Scientific careers today are marked by growing polarization: A small number of scientists now remain active and influential for longer than ever (1), whereas many others pass through research as...
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Berna Devezer @devezer.bsky.social · 30/04/2026
📣 The difference between replicable and not replicable is not itself scientifically replicable. 📣 New work with Erkan Buzbas, showing that verdicts such as "X% of results replicated" are based on an inferential machinery that doesn't work. arxiv.org/abs/2604.26268
Screenshot of a paper's title page. Title: "The Difference Between 'Replicable' and 'Not replicable' is not Itself Scientifically Replicable". Authors: Berna Devezer and Erkan O. Buzbas, both at the University of Idaho — Devezer in the Department of Business and the Institute for Modeling Collaboration and Innovation, Buzbas in the Department of Mathematics and Statistical Science. Authors contributed equally. Corresponding author: bdevezer@uidaho.edu.
Abstract: Replication studies estimate the replicability rate of scientific results by aggregating binary verdicts of experiments. Exact replications are rarely attainable, so most replication sequences are non-exact. Experiments differ in ways that matter and do not share a single common data-generating process. We formalize two statistical interpretations of this non-exactness. In a shared latent rate model (benchmark), experiments are exchangeable and depend on a common random replicability rate. In a conditionally independent rates model (operational), each experiment has its own replicability rate drawn independently from a population distribution. Under the shared latent rate model, even small variability among replicability rates induces an irreducible variance floor on the estimated mean replicability rate that cannot be eliminated by adding more replications. Under the conditionally independent rates model, the degree of non-exactness is not identifiable from standard replication data, because one binary verdict per experiment contains no information about between-experiment heterogeneity. Researchers therefore cannot tell which precision regime they are operating in or whether high- and low-replicability sequences can be distinguished in principle. As a result, the usual data structure of one binary verdict per experiment cannot support reliable demarcation between "replicable" and "not replicable" results and systematically understates uncertainty, making high- and low-replicability sequences appear discrim…
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Manikya Alister @manikyaalister.bsky.social · 28/04/2026
Follow my PhD sibling!
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Andrew Wang @andreww3-wang.bsky.social · 28/04/2026
📄 Long overdue paper announcement: Core Vocabulary in Language Representation and Processing was published late last year in Cognitive Science. The work explores novel approaches of quantifying vocabulary centrality and tests them in an empirical task. 🔗 onlinelibrary.wiley.com/share/author...
onlinelibrary.wiley.com
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Courtney Hilton @courtneybhilton.bsky.social · 24/04/2026
Last week for submissions (deadline April 30th)! If you do work in any part of experimental psych or vision science come join us in Auckland, New Zealand! It's shaping up to be a great conference.
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Mark Ho @markkho.bsky.social · 24/04/2026
Excited that this work with @rachitdubey.bsky.social and @cocoscilab.bsky.social is finally officially out!
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Younes Strittmatter @younesstrittmatter.bsky.social · 22/04/2026
Too many star chefs spoil the broth. In an Overcooked-like task, mixed teams beat uniformly strong players. Fittingly, the project came from an interdisciplinary team: different fields, better recipe. Thanks all! We’ll present at CogSci. See y’all there. www.researchgate.net/publication/...
researchgate.net
(PDF) When Collaboration Beats Ability: Mixed-Ability Teams Can Outperform High-Ability Teams Under Coordination Demands
PDF | Collective intelligence describes the capacity of groups to achieve levels of performance that cannot be explained by the abilities of their... | Find, read and cite all the research you need on...
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Jay Van Bavel, PhD @jayvanbavel.bsky.social · 21/04/2026
A small fraction of online actors exerts outsized influence over what the public sees, believes, and discusses. In a new paper, we trace how social media influencers turn fringe claims into viral narratives by exploiting a feedback loop between influencers, algorithms & crowds osf.io/preprints/ps...
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Marina Dubova @mdubova.bsky.social · 20/04/2026
Ever thought we acquire generalizable knowledge by discarding details and compressing our experiences? In a new BBS paper, @sabinasloman.bsky.social and I argue otherwise, proposing a novel way of studying human learning inspired by double descent in ML. Disagree? Propose a commentary by May 15 :)
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Alison Gopnik @alisongopnik.bsky.social · 16/04/2026
New preprint of a paper with Eunice Yiu to appear in Philosophical Transactions A, Special issue: World models, 2026. The theoretical link between empowerment in RL and Bayesian causal models with cool new data. arxiv.org/abs/2512.08230
arxiv.org
Empowerment Gain and Causal Model Construction: Children and adults are sensitive to controllability and variability in their causal interventions
Learning about the causal structure of the world is a fundamental problem for human cognition. Causal models and especially causal learning have proved to be difficult for large pretrained models usin...
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Max Berger @maxberger.bsky.social · 14/04/2026
I love paying TurboTax a few hundred bucks every year to figure out how much money I owe the government, which the government already knows, but won't tell me because TurboTax pays legislators to keep the government from telling me. 😍
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Sydney Levine @sydneylevine.bsky.social · 14/04/2026
@michael.muthukrishna.com and I are hiring a postdoc to join our labs at NYU!  We're looking for someone excited to work on one of society's newly emerging and potentially generation-shaping challenges: the multi-agent alignment problem.
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Oleg Urminsky @olegurminsky.bsky.social · 09/04/2026
The paper, led by Eugina Leung, is here: www.pnas.org/doi/10.1073/... We document that the human tendency for confirmation bias in question framing (the tendency of people to frame search in terms of their prior beliefs) and algorithms that optimize for relevance combine to impede belief updating.
pnas.org
The narrow search effect and how broadening search promotes belief updating | PNAS
Information search platforms, from Google to AI-assisted search engines, have transformed information access but may fail to promote a shared factu...
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Tobias Gerstenberg @tobigerstenberg.bsky.social · 03/04/2026
The Causality in Cognition Lab -- a supportive, bluesky-colored team -- is looking for a predoc to join us! Here are infos about the lab (cicl.stanford.edu) and the position (careersearch.stanford.edu/jobs/iriss-p...). The application deadline is May 1st. Please share, thank you 🙏
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Carlos Scheidegger @cscheid.net · 06/04/2026
And another Quarto announcement; I've alluded to it before, but we're making it "official". We've started work on Quarto 2. The blog post has an overview: quarto.org/docs/blog/po... We'll share more in future blog posts, but here's what you can expect from the Quarto 2 dev effort: (1/)
quarto.org
What’s next: Quarto 2 – Quarto
We’ve started working on quarto-dev/q2, a full rewrite of Quarto in Rust.
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Paul Smaldino @psmaldino.bsky.social · 02/04/2026
I've been thinking a lot about the foundations of cultural evolutionary theory. While there's been a lot of work on transmission mechanisms, there has been far less work on cultural *selection*. Here's a new working paper presenting a taxonomy of cultural selection processes. osf.io/preprints/so...
Title page for working paper: "The Varieties of Cultural Selection"
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