Michael Eddy
@michaeleddy.bsky.social
380 followers 340 following 120 posts
Helping put social science to use for society at Stanford Impact Labs Twitter: @michaeleddy 🏳️‍🌈
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michaeleddy.bsky.social
I love it when papers have accompanying websites!

also, neat when i found out @stanfordimpactlabs.bsky.social had a small role in supporting this work!

📄 & 💻by @marshallburke.bsky.social & a impressive team!

adaptationatlas.org/...
www.nber.org/papers/...
michaeleddy.bsky.social
@ukri.org has announced a £11.5m investment in AI-driven evidence synthesis (METIUS).

Backed by a $126m global alliance including @wellcometrust.bsky.social, the project aims to make research more accessible for policymakers on climate, education, justice and development.
michaeleddy.bsky.social
Fascinating paper on a timely question... kudos!!
Reposted by Michael Eddy
mattsclancy.bsky.social
New research by Pierre Azoulay, Danielle Li, Bhaven Sampat and me.

Earlier this year, the President’s budget proposed a 40% cut to the budget of the NIH. This motivated us to ask: what if the NIH had been 40% smaller?
michaeleddy.bsky.social
With a few notable exceptions, I’m struck by how few funders are openly sharing what they’re learning from AI in decision-making.

What am I missing?
michaeleddy.bsky.social
We’ve embedded this directly in our latest RFP. (screenshot 👇)

This is about experimentation, transparency, and learning—together with the research & funding community.
michaeleddy.bsky.social
Here’s what “responsible” means to us:
➡️ AI augments—not replaces—human decision-making
➡️ New workflows that weren’t previously possible
➡️ Robust safeguards
➡️ A learning agenda to test key claims
michaeleddy.bsky.social
That’s why @StanfordImpact is piloting responsible AI use in our funding processes.

Our goal: accelerate impact-focused science R&D.
✅ Faster decisions
✅ Lower costs
✅ Reduced burden on applicants
michaeleddy.bsky.social
Right now:
⚠️ Many funders ban AI outright.
🙈 Others ignore it.
🤖 And slick AI vendors make bold, untested claims.

None of this felt right to me
michaeleddy.bsky.social
Ever applied for funding… and then had to wait months for a response? ⏳

What if funders could move faster and make better decisions—so applicants can secure funding & get straight to work?

AI could help. But the current landscape is messy. 🧵
michaeleddy.bsky.social
After a disastrous launch, the Dept. of Education's new team fixed the FAFSA system, helping 14M+ students. But the GAO criticized their modern methods with an outdated playbook.

It's high time we focus on outcomes not broken processes, argues @pahlkadot
GAO gets schooled by the Department of Education
The FAFSA team snaps back, then punches back
www.eatingpolicy.com
michaeleddy.bsky.social
I wonder why more academics don't publish their papers with companion websites? I find it so much more enjoyable reading an interactive site!

"AI Agents for Economic Research" by @antonkorinek www.genaiforecon.org
Generative AI for Economic Research
www.genaiforecon.org
michaeleddy.bsky.social
I remember meeting Karthik & @singhabhi.bsky.social w/ the Mindspark/EI team when I was working at Global Innovation Fund back in 2017—and being blown away by how they were designing for impact @ scale from day 1.

A stellar example of academics + do-ers partnering for scale 🚀📈👇
singhabhi.bsky.social
Many interventions “work” in small trials but fail at scale

Also, EdTech often promises much, but delivers little

In a new paper (bit.ly/3JKLgVn)
@karthik-econ.bsky.social
& I show how personalized adaptive learning (PAL) software can sharply improve learning outcomes at scale🧵1/16
michaeleddy.bsky.social
my main concern is that these lab-style games don't mirror interventions at scale....

But they do provide a controlled testbed to explore whether underlying mechanisms generalize, and if mechanisms don't generalize, unlikely you're intervention will generalize either.
michaeleddy.bsky.social
Impact-focused funders often ask: if it worked here, will it work there?

This paper is a small, but impt step toward a generalized approach (grounded in theory + human data) to generalize research across settings—core to putting social science to use at scale.💪
emollick.bsky.social
This is a cool paper that suggests that AI agents can indeed be used for social science experiments, but that just using a chatbot isn't good enough, instead prompts developed based on social & game theory makes AI agent actions predictive of real human outcomes. benjaminmanning.io/files/optimi...
michaeleddy.bsky.social
Science faces a replication crisis: billions wasted, progress slowed, policy misled

A new idea from @i4replication.bsky.social Brodeur & Barbarioli in @ifp.bsky.social's #LaunchEngine imagines how to build automated replication infrastructure for *better* & *faster* science ifp.org/the-replicat...
The Replication Engine | IFP
How to build automated replication infrastructure for better, faster science
ifp.org
michaeleddy.bsky.social
Authors: @anaisfabre.bsky.social at IFS; Chris Nielson @yaleeconomics.bsky.social; Ignacio Rios @utdallas.bsky.social; & Tomas Larroucau @arizonastateuni.bsky.social with collaborators at Chile’s Min of Education @mineducchile.bsky.social

Kudos to everyone involved!! 🎉
michaeleddy.bsky.social
This wasn’t a one-off: the policy scaled nationwide with persistent enrollment gains—evidence that “designed-for-scale” information can work in real systems.
michaeleddy.bsky.social
Results: previously unmatched students were 44% more likely to get an offer; placements into higher-ranked programs rose 20%
michaeleddy.bsky.social
The intervention: show applicants tailored admission probabilities + program info + smart nudges (e.g., "don’t forget a safety school"). Delivered inside the application portal.
michaeleddy.bsky.social
Few things I love more than when I see a paper report on how research was put to use at scale!!👇

New study on fixing college admissions. In Chile’s centralized system, personalized, in-platform info helped students avoid common app mistakes & improved outcomes🧵⬇️
michaeleddy.bsky.social
Policy takeaway: Pair income support with access to obstetric care.

Cost-effective, scalable complement to maternal/child health programs—and a great example of research that fuels real-world impact