Tessera

Kevin Elliott @kjephd.bsky.social · 4w ago

A very welcome digression into normative territory by @yamilrvelez.bsky.social in which he reflects over why a focus on the specific issues individual citizens care about matters for democracy—drawing from a recent paper of mine as well as Converse—to address deep polisci Qs like citizen competence.

Yamil Ricardo Velez@yamilrvelez.bsky.social · 4w ago

I veer dangerously into normative territory in this piece: how new measures allow us to grapple with the messy, idiosyncratic preferences of Americans, what political specialization means for democracy, and why I’m doing this work in the first place.

Yamil Ricardo Velez @yamilrvelez.bsky.social · 4w ago

I veer dangerously into normative territory in this piece: how new measures allow us to grapple with the messy, idiosyncratic preferences of Americans, what political specialization means for democracy, and why I’m doing this work in the first place.

Mapping Islands of Opinion

The case for measuring, rather than assuming, what voters care about

substack.com

Daniel de Kadt @dandekadt.bsky.social · last mo.

Tomorrow I will co-moderate a panel on AI use in grad school with @aricaschuett.bsky.social, who has put together a superb panel: @yiqingxu.bsky.social Rachel Gillum @yamilrvelez.bsky.social @lpargyle.bsky.social Grad students (& others) should tune in! Register: connect.apsanet.org/graduate/

APSA Graduate Student Committee

American Political Science Association

connect.apsanet.org

Jake Grumbach @jakemgrumbach.bsky.social · 2mo ago

My new theory paper (early draft) A new reason why concentrated wealth destroys democracy: common ownership A standalone media owner maximizes profit. A common owner like Musk (who owns X + SpaceX) will run the media firm at a loss to increase the $ of his total portfolio tinyurl.com/mcekr7cd

Wealth Concentration and Democratic Erosion:
A Theory of Media and Common Ownership*
Jacob M. Grumbach
June 18, 2026
Abstract
I develop a model of how extreme wealth concentration harms democracy through
a common ownership problem. A standalone profit-seeking media firm will maximize advertising revenue and audience size, producing content that is orthogonal
to democracy. In contrast, an owner who controls both a media firm and other
large firms can use the media firm to produce anti-democratic content, which
reduces the profitability of the media firm but maximizes the profitability of the
overall business portfolio through greater rents from an authoritarian leader (e.g.,
preferential regulatory treatment or public contracts). Anti-democratic content
grows in the owner’s wealth and in the authoritarian premium, the differential
value of concentrated capital under authoritarian governance. I theorize three
extensions: an oligopolistic setting in which reduced democratic content is a common pool resource; an endogenous authoritarian premium based in the authoritarian’s targeting problem; and owners that vary in proactively or reluctantly
supporting the authoritarian.

Jake Grumbach@jakemgrumbach.bsky.social · 2mo ago

Wealth concentration was already way off the rails before Musk became a trillionaire. The wealthiest megabillionaires in the country have basically been doubling their wealth annually in recent years. Feels near impossible to maintain a liberal democracy in this kind of situation.

Yamil Ricardo Velez @yamilrvelez.bsky.social · 2mo ago

Why do arguments often change people’s beliefs without changing their attitudes? In a new APSR with @scottclifford.bsky.social and @patrickpliu.bsky.social, we point to belief relevance: arguments are more persuasive when they grapple with the idiosyncratic reasons people hold their political views.

APSA Preprints @apsa-preprints.bsky.social · 2mo ago

The APSA Pres Task Force on AI, Politics, & Political Science's report comes in the form of an edited volume identifying questions & establishing a foundation for the empirical study of how politics & governance are affected by AI. Check out these early chapter drafts: shorturl.at/cMZzI #polisky

Yamil Ricardo Velez @yamilrvelez.bsky.social · 3mo ago

Happy to share that my Cambridge Element with @patrickpliu.bsky.social, Tailored Experiments: Personalized Interventions Using Generative AI, has been accepted as part of Jamie Druckman’s Experimental Political Science series.

Brendan Nyhan @brendannyhan.bsky.social · 3mo ago

Our chapter! preprints.apsanet.org/engage/apsa/... "AI is more likely to reinforce existing patterns of [information] exposure and behavior than it is to transform how people understand and relate to the political world" (w/@jenpans.bsky.social @aasiegel.bsky.social @yamilrvelez.bsky.social)

Nate Persily@persily.bsky.social · 3mo ago

The Draft Volume of the APSA AI Task Force, cochaired by Josh Tucker and me, is now available. ARTIFICIAL INTELLIGENCE, POLITICS, AND POLITICAL SCIENCE will be published by Cambridge Press later this year, but the draft chapters are available today: apsanet.org/wp-content/u...

Omar Wasow @owasow.bsky.social · 3mo ago

“Writing is hard.” Thrilled to share that this simple idea led to a new paper in Political Analysis! Where most text methods focus on content, I test if expression is also effortful action. I find simple measures like character counts reveal attitudes and predict voting. cup.org/4cUmoXi 1/

Text as Behavior
Text as Behavior published in Political Analysis

by Omar Wasow

Abstract

Text analysis typically focuses on content—such as sentiment or topic—but expression is also a form of effortful action. Building on this insight, I propose using simple features of open-ended tasks to study text as behavior. This approach treats expression, such as writing, as cognitively, emotionally and temporally “costly” for subjects but inexpensive for researchers. I show basic statistics like the number of characters can approximate effort and significantly improve estimation of quantities of interest, including candidate choice, the probability of turning out to vote and psychological states about which a subject may not be fully aware. Further, these methods can convert nonresponse into informative data; validate survey instruments; serve as mechanism checks; be hard for a subject to “game”; work across different languages and analogize well to real-world situations. In sum, text as behavior can help address a range of issues related to quantifying attitudes and actions.

Yamil Ricardo Velez @yamilrvelez.bsky.social · 3mo ago

It was a pleasure to chat with Andy Luttrell about my new paper on persuasion, joint with Scott Clifford and Patrick Liu. It's an exciting time to revisit classic debates about opinion formation and change!

Andy Luttrell@andyluttrell.bsky.social · 3mo ago

Ep 116! @yamilrvelez.bsky.social uses innovative methods to understand how people arrive at their views. We talk about beliefs vs. opinions, why correcting misinformation doesn’t necessarily move opinions, and the power of tailoring messages to the reasons that matter to someone's view.

Yamil Ricardo Velez @yamilrvelez.bsky.social · 3mo ago

People already hand off decisions to AI when drafting emails, polishing papers, or writing code. Survey research will not be immune. In my new post, I offer an initial look at preferences for AI delegation in surveys and who is most open to it. newinstruments.substack.com/p/all-hail-o...

John V. Kane @uptonorwell.bsky.social · 4mo ago

🚨New working paper w/ the legendary @cbwlezien.bsky.social! Lots of talk about inflation & voters' political views. But how well do voters actually understand info about inflation? We find that most citizens conflate changes in rates w/ changes in prices. This has important consequences...

Yamil Ricardo Velez @yamilrvelez.bsky.social · 4mo ago

New Substack: how I think about adaptivity in research design, and the strange path that got me there newinstruments.substack.com/p/surveys-th...

Surveys That Listen

How Existential Terror Pushed Me Toward Adaptive Research Designs

newinstruments.substack.com

Jon Green @jongreen.bsky.social · 4mo ago

extremely happy to share that "Political Pundits and the Maintenance of Ideological Coalitions" is conditionally accepted at @polbehavior.bsky.social osf.io/8c3fr/files/...

Title: Political Pundits and the Maintenance of Ideological Coalitions
Authors: Allison Wan and Jon Green
Abstract: Political ideologies help groups advance diverse sets of interests under common agendas. However, it is unclear how these groups maintain their norms regarding what it means to be
a member in good standing in a dynamic information environment. Building on theories of “long” political coalitions, we hypothesize that ideological elites’ rhetorical influence on one another will tend to be concentrated and specialized with respect to specific concepts. We
find support for this expectation using an original dataset of over 1,000 prominent political pundits in the United States, in which we infer coalition membership and the diffusion of novel language over a range of specific concepts. While pundits may discuss many concepts,
they tend to “send” language to other pundits in relatively few, resulting in both concentration and specialization of influence within coalitions. These results clarify conceptual distinctions between political ideologies and political philosophies, and demonstrate real-time dynamics of contemporary ideological coalitions.

Figure 1: Stylized expectations. Each node reflects a hypothetical coalition member, colors denote concepts, and arrows indicate members’ influence on each other. For example, member (a) influences members (b), (c), and (e) with respect to one concept, such as immigration, while member (d) influences members (b), (c), and (e) with respect to a different concept, such as health care. Member (e) acts as a broker, both sending and receiving influence across the coalition. Every member is influential, but influence tends to be concentrated (for any given concept, one member accounts for 3/4 of influence) and specialized (4/5 members are influential on only one concept).

Figure 3: Concentration of influence within concepts, observed network compared to permuted networks. Degree, Community, and Concept denote structural features either are or are not preserved in network permutations.

Figure 4: Specialization of influence within concepts, observed network compared to permuted networks. Degree, Community, and Concept denote structural features either are or are not preserved in network permutations.

Jay Van Bavel, PhD @jayvanbavel.bsky.social · 4mo ago

Come share your research at NYU! The NYU Social Psychology Program is inviting speakers for 2026-2027! If you’re passing through NYC next year and are interested in giving a research talk nominate yourself using this short form by MAY 15th: docs.google.com/forms/d/1yTI...

Kevin Collins @kwcollins.bsky.social · 4mo ago

A thoughtful essay from @yamilrvelez.bsky.social on the LLM-bots-in-surveys problem newinstruments.substack.com/p/who-answer...

Who Answered This Survey?

Deception, Satisficing, and Delegation in the Agentic Era

newinstruments.substack.com

Patrick Liu @patrickpliu.bsky.social · 4mo ago

New paper w/ @yamilrvelez.bsky.social! A lot of great research on political microtargeting discounts personalization: tailored ads (using AI or not) rarely beat a single-best message. We define two types of microtargeting, clarify when tailoring matters, & showcase a novel audio-based design.

Alex Coppock @aecoppock.bsky.social · 5mo ago

New paper with Don Green and @ethanvporter.bsky.social in the QJPS. After much deliberation, we went with a title that just states the result. 📝 journal: www.emerald.com/qjps/article...

abstract: While attempts to change Americans’ partisanship via persuasive treatments largely fail, partisanship can and does change over time. In this paper, the authors first confirm, via survey and field experiments, that typical campaign messaging in the United States does not budge partisanship. The authors then present experiments in which participants encounter extraordinary hypothetical scenarios (e.g. one party causes economic collapse) before reporting what their partisanship would be under such circumstances. Twelve percent of partisans imagine switching parties in the pro-out-party hypothetical conditions, compared with 5% in the control hypotheticals in which the status quo persists, for a seven-percentage point (SE 1.5 points) difference. These hypothetical shifts are on par with the largest changes in American macropartisanship ever recorded. While the act of ruminating on hypothetical scenarios is not followed by changes in partisanship measured post-treatment, the evidence suggests that extraordinary world events may be able to shift partisan affiliation.

Yamil Ricardo Velez @yamilrvelez.bsky.social · 5mo ago

New paper with Stephanie Zonszein! Political news is more important than ever, but local papers are shuttering across the US. In recent years, innovative community-centered outlets led by journalists have taken to WhatsApp and social media to reach groups such as immigrants. What are their effects?

Alex Coppock @aecoppock.bsky.social · 5mo ago

Congratulations to @yamilrvelez.bsky.social, @patrickpliu.bsky.social, and @scottclifford.bsky.social ! we think attitudes are some function of beliefs; our exps routinely move beliefs but not (even correlated) attitudes. This team found a way to guess which beliefs matter more (and they do!)

Yamil Ricardo Velez@yamilrvelez.bsky.social · 5mo ago

Conditionally accepted at the APSR (w/ @scottclifford.bsky.social & @patrickpliu.bsky.social): Why does political information so often change beliefs but NOT attitudes? We highlight the role of belief relevance, or the extent to which beliefs bear on attitudes.

Yamil Ricardo Velez @yamilrvelez.bsky.social · 5mo ago

Conditionally accepted at the APSR (w/ @scottclifford.bsky.social & @patrickpliu.bsky.social): Why does political information so often change beliefs but NOT attitudes? We highlight the role of belief relevance, or the extent to which beliefs bear on attitudes.

Semra Sevi @semrasevi.bsky.social · 5mo ago

Science communication has never been more important. In this animation, @yamilrvelez.bsky.social, Donald Green and I break down our research exploring whether AI chatbots can increase political engagement among young, politically unaligned voters. Link to animation: www.youtube.com/watch?v=iCuX...

Can AI Chatbots Increase Political Engagement of Young Unaligned Voters?

YouTube video by Semra Sevi

youtube.com

Semra Sevi @semrasevi.bsky.social · 8mo ago

🚨Excited to share our new paper published in PNAS (joint with @yamilrvelez.bsky.social and Don Green)! AI can enhance political knowledge and provide balanced information about politics with proper guardrails and vetted sources (e.g., party platforms). www.pnas.org/doi/full/10....

Dan Silverman @dmsilverman.bsky.social · 8mo ago

People feeling hopeless about AI/LLMs spoiling all online polling and survey research should take a look at what @yamilrvelez.bsky.social is doing with “Pulse” — a tool to detect proof of life that’s compatible with Quatrics

Yamil Ricardo Velez@yamilrvelez.bsky.social · 8mo ago

As @seanjwestwood.bsky.social's terrifying new PNAS article demonstrates, LLMs can now pass almost every attention check, mirror personas, stay consistent across pages, and systematically bias responses in the aggregate. So here’s a different angle: verify physical presence, not text.

Jay Van Bavel, PhD @jayvanbavel.bsky.social · 8mo ago

An AI Voter bot improves knowledge about politics But, the AI bot has weak effects on downstream outcomes like vote preferences and party evaluations among respondents whose primary issue position aligns closely with one of the parties. Partisan action is hard to change. www.pnas.org/doi/10.1073/...

Yamil Ricardo Velez @yamilrvelez.bsky.social · 8mo ago

This paper was a blast to work on. The challenge: present party positions across many issues, in real time, using language voters actually use. 🧵 on why we went with a more involved retrieval-based approach and where I think these tools are headed.

Semra Sevi @semrasevi.bsky.social · 8mo ago

🚨Excited to share our new paper published in PNAS (joint with @yamilrvelez.bsky.social and Don Green)! AI can enhance political knowledge and provide balanced information about politics with proper guardrails and vetted sources (e.g., party platforms). www.pnas.org/doi/full/10....

Semra Sevi @semrasevi.bsky.social · 8mo ago

🚨Excited to share our new paper published in PNAS (joint with @yamilrvelez.bsky.social and Don Green)! AI can enhance political knowledge and provide balanced information about politics with proper guardrails and vetted sources (e.g., party platforms). www.pnas.org/doi/full/10....

Lisa Argyle @lpargyle.bsky.social · 8mo ago

Looking for a tl;dr for these two excellent papers? I've got you covered: www.science.org/doi/10.1126/...

Political persuasion by artificial intelligence

Large-scale studies of persuasive artificial intelligence reveal an extensive threat of misinformation

science.org

David Rand@dgrand.bsky.social · 8mo ago

🚨 New in Nature+Science!🚨 AI chatbots can shift voter attitudes on candidates & policies, often by 10+pp 🔹Exps in US Canada Poland & UK 🔹More “facts”→more persuasion (not psych tricks) 🔹Increasing persuasiveness reduces "fact" accuracy 🔹Right-leaning bots=more inaccurate

David Rand @dgrand.bsky.social · 8mo ago

🚨 New in Nature+Science!🚨 AI chatbots can shift voter attitudes on candidates & policies, often by 10+pp 🔹Exps in US Canada Poland & UK 🔹More “facts”→more persuasion (not psych tricks) 🔹Increasing persuasiveness reduces "fact" accuracy 🔹Right-leaning bots=more inaccurate

Yamil Ricardo Velez @yamilrvelez.bsky.social · 8mo ago

The Economist@economist.com · 8mo ago

The first wave of AI survey-takers may not distort much in the beginning. Their answers often mimic patterns established by existing polling. But a more insidious feedback loop will emerge, if it has not already

Yamil Ricardo Velez @yamilrvelez.bsky.social · 8mo ago

As @seanjwestwood.bsky.social's terrifying new PNAS article demonstrates, LLMs can now pass almost every attention check, mirror personas, stay consistent across pages, and systematically bias responses in the aggregate. So here’s a different angle: verify physical presence, not text.