Dan Thompson

@danmthomp

Studying elections and political economy. Associate Prof of Political Science, UCLA

Los Angeles, CA danmthompson.com Joined November 2016

989 Following 1.3K Followers 417 Posts

Latest posts 5

Dan Thompson @danmthomp Oct 2

Whoa, the economist is running ads with a plot from
@vavreck, @RyanBaxterKing, @RyanDEnos, and Jake Brown

The Economist Verified account @TheEconomist Aug 25

The mental health of conservatives suffers when they live in progressive places
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Dan Thompson @danmthomp Oct 1

Follow-up to our @freesystemslab experiment this week: Rewarding AI agents for the swarm’s performance rather than their own makes the swarm less likely to converge on a false belief. But even with the right incentives, agents don’t always figure out how to avoid getting it wrong

Dan Thompson @danmthomp Mar 11

A few weeks ago, @ahall research and I spent election night predicting the Texas Senate primary results using a mix of our normal political science knowledge and some light-speed coding from @claudeai. We learned a lot! Check out Andy’s post if you’re interested.

Andy Hall Verified account @ahall_research Mar 11

As we head towards the 2026 elections, information feels shaky. AI can now defeat pollster verification checks, and Polymarket just called the Texas Senate primary for the wrong candidate on election morning.

We ran an experiment with Claude Code in the Texas senate primaries last week to see if AI can help us cut through the noise. Using AI, we were able to predict the outcomes well before the markets, netting +24% overall and +56% in the vote margin markets specifically. We crushed!

But the way AI helped us was surprising! Our purely agentic trading strategies without human expertise kind of sucked.

Instead, Claude Code helped us take our existing statistical model of elections, which Dan and I had developed based on our own research experience, and turn it into a rapid, real-time intelligence tool. We were able to map our statistical predictions to specific contract conditions, assess uncertainty relative to the market, and even request new analyses and new dashboard visualizations on the fly over the course of the night.

Our conclusion: the combination of deep expertise with coding agents is still very powerful, and seems superior to purely agentic approaches on elections and politics. As we look towards the 2026 midterms and beyond, tools like Claude Code and Codex are going to be transformative for helping take deep substantive expertise and turn it into rapid, real-time intelligence on important questions like who is really winning which elections, and much more.

Joint work with @danmthomp -- check out the full piece linked below.

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Dan Thompson @danmthomp Feb 27

Everyone working on election admin research should fully absorb this paper. The average voter thinks the act of registering and voting is easy but deciding who to vote for is hard, especially in local races. This tidy result explains so many patterns in our literature.

Justin Grimmer Verified account @JustinGrimmer Feb 27

How do we measure the cost of voting? In a new paper @seanjwestwood , @eitanhersh , and I document serious problems with current measurement strategies and address those problems with a new methodology to elicit citizens' perceived costs. Our elicited measures reveal a surprising fact: citizens perceive deciding who to support as more difficult than logistical steps, like registering to vote or casting a ballot in person.

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Dan Thompson @danmthomp Oct 10, 2023

Private donors gave $350M+ to support election administration in 2020. Many critics argued that these grants swung the election to Biden. In a new paper, @Apoorva__Lal and I find that these grants did not provide Democrats a noticeable advantage.

bit.ly/lal_thompson_e…