Associate Professor, UBC Vancouver School of Economics. Interested in the (macro)economics of growth/AI/ML, and use of ML within economics itself. Swiftie.
Joined February 2011
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Amazing. You must see lots of connections between macro time-series and how the physical sciences investigate the spectrum.
Jesse Perla retweeted
For those interested in a broader result, namely a tâtonnement algorithm which is not stable only in some cases but almost always, check out our paper from 2024.
In our paper, we introduce mirror extratâtonnement — a generalized process with polynomial-time global convergence in Walrasian economies, including the notoriously hard Scarf economy.
More in this thread: nitter.cf/plazined/status/206153…
Jesse Perla retweeted
If you want a full picture of price-adjustment, in our paper, we resolve Herbert Scarf's 50 year old challenge—namely “a general method for the explicit numerical solution of the neoclassical model.”
arxiv.org/pdf/2502.11449
Submitted 2x to ACM EC, and overturned by editors 🥱.
Jesse Perla retweeted
Very happy that my 𝐃𝐞𝐞𝐩𝐇𝐀𝐌 paper with @JiequnH and Weinan E appears as a lead article at QE!
It’s been almost five years since our first @arxiv draft. Many thanks to everyone who helped us along the way, especially @glviolante for numerous discussions and support!
Jesse Perla retweeted
DeepHAM is out as a lead article at QE 🎉 my second econ publication, continuing work from my first PhD project, which introduced continuous-time methods to heterogeneous-agent models. Aggregate shocks were the bottleneck back then; DeepHAM finally makes them tractable with DL.
Very happy that my 𝐃𝐞𝐞𝐩𝐇𝐀𝐌 paper with @JiequnH and Weinan E appears as a lead article at QE!
It’s been almost five years since our first @arxiv draft. Many thanks to everyone who helped us along the way, especially @glviolante for numerous discussions and support!
My annual reminder that this is one of most important papers I have read on deep learning and its place in ML.
Non ML/CS/Stats readers can otherwise attribute properties to deep learning and "neural networks" that have nothing to do with that any particular hypothesis class.
This is your annual reminder that we don’t need to speculate about whether we will have a “theory of deep learning” and what form it might take, because we already have a basic understanding of generalization in deep learning: arxiv.org/abs/2503.02113
Jesse Perla retweeted
Beautiful and deeply deserved.
Truly grateful for your guidance and the opportunity to work with you❣️❣️❣️
I know we shouldn't be driven by desire for accolades but I am proud of this one. Maybe I didn't entirely waste my short moment of time on this beautiful planet 🥹❤️ comp-econ.com/contest-and-pr… @SocCompEcon
Jesse Perla retweeted
I know we shouldn't be driven by desire for accolades but I am proud of this one. Maybe I didn't entirely waste my short moment of time on this beautiful planet 🥹❤️ comp-econ.com/contest-and-pr… @SocCompEcon
Jesse Perla retweeted
Famously (there is a beautiful Works in Progress piece on this) in 2016, Geoffrey Hinton told an audience in Toronto that medical schools should stop training radiologists, since AI would soon outperform them at reading scans. Ten years later, there are more radiologists than ever, and they earn more than they did then.
Hinton was right about the task, but he was wrong (so far!) on the future of the radiology profession. Times have never been better for them. The gap between those two claims, the difference between tasks and jobs, is the subject of a paper I have written with Jin Li and Yanhui Wu, and that we release today: "Weak Bundle, Strong Bundle: How AI Redraws Job Boundaries." (Very relatedly we are also finishing the first draft of our book "Messy Jobs" on AI and Jobs!! You will be the first to hear).
We start from the observation that the growing literature on AI and labor markets measures the AI shock by task exposure: people count how many tasks AI can perform in a given occupation AI can perform, and infer that more exposure means more displacement. Eloundou et al. published a paper in Science in 2024 that started this literature, and many follow the same logic. The inference they make is that the more exposed tasks, the worse the outcomes.
This is incomplete, because labor markets price jobs, not tasks. A radiologist does not just sell image classification, but does many other jobs: triages cases, communicates with other physicians, trains residents, makes the difficult decisions, and signs a diagnosis. The market buys a bundled service. The question AI poses is not whether it can do one task inside the bundle. The question is whether that task can be pulled out.
Thread (1/3)
dropbox.com/scl/fo/689u1g785…
Jesse Perla retweeted
Tom and I have finally finished a draft of Dynamic Programming Vol 2! Exhausting but satisfying. New approach to DP theory, advanced material, many applications... dp.quantecon.org/
Jesse Perla retweeted
I spend way too much time on social media debunking "economic slop" promulgated by lawyers pretending to be economists, so I built Show Me the Model: a tool that uses AI to check whether the economic reasoning in an essay actually holds up.
showmethemodel.io
Give it a URL or paste some plain text, and the tool flags hidden assumptions, internal inconsistencies, and other problem areas, and tells you how a real economist would think through the issue.
Right now, it has 4 "personas:" macro, trade, IO/price theory, and labor. The tool first figures out which persona is right for the job, and then uses a parallelized prompt scaffold specific to that persona to process the source text.
Here are some example outputs based on some essays that triggered me hard:
Citrini Research's viral essay on how AI could trigger a self-reinforcing financial crisis rivaling the GFC:
showmethemodel.io/#/results/…
American Compass on the harms of trade deficits:
showmethemodel.io/#/results/…
@oren_cass on why Built-to-Rent should be banned:
showmethemodel.io/#/results/…
American Compass on the "China Shock:"
showmethemodel.io/#/results/…
@michaelxpettis on why China's trade surplus reduces global output:
showmethemodel.io/#/results/…
Try it yourself at showmethemodel.io. You'll need to bring your own API key (OpenAI or Anthropic), and a typical analysis costs $0.50–$1.50.
It's super preliminary and will probably break on you. I'd love feedback about both the functionality as well as the quality of the output.
Jesse Perla retweeted
🚨Replication alert🚨
I'm pleased to announce that my replication of Moretti (2021) is now accepted as a comment at AER.
I find ten issues in the paper. My comment focuses on two major problems; in the appendix, I document eight (relatively) minor problems.
1/
Jesse Perla retweeted
Lecture 1 & 2 of my computational econ notes, based on Judd's textbook, explain floating point and its propagation. For regression, differences arise due to multicollinearity and mixed scaling, but different algorithms matter a lot in high dimesnions! github.com/donskerclass/Comp…
Jesse Perla retweeted
#julialang homework for our floswald.github.io/CompEcon/ course @CollegioCA : reverse engineering the #esselunga presto spesa handheld supermarket scanner system. custom types, simulation, optimization. notebook:
pluto.land/n/yt8hux1c
Jesse Perla retweeted
today I learned about the chicken McNugget theorem...
kids and I were talking and about prime numbers and nfl sores (fun conversation)
I made the claim any integer > 1 can be represented as a NFL score.
Turns out the proof of this claim is just a case of the chicken McNugget theorem
Jesse Perla retweeted
Our search at @ubcVSE is over and we are very happy to have Sydney Costantini (Berkeley) sydneycostantini.com/ and Nagisa Tadjfar (MIT) nagisatadjfar.github.io/ joining @ubcVSE in the fall and improving our already amazing applied micro group
Jesse Perla retweeted
I want to take a moment to defend calibration. A common critique of macro by non-macro people centers on the supposed lack of scientific rigor associated with calibration of models. 1/10
Jesse Perla retweeted
QuantEcon lectures can now be launched straight into Google Colab—no local setup needed🎉🎉🎉
Thanks @jlperla for making this even more accessible❣️❣️❣️
#JuliaLang #QuantEcon #OpenSource #ComputationalEconomics #EconTeaching #JuliaNotebooks #GoogleColab #Economics #DataScience
In the latest @QuantEcon Julia release, you can now launch notebooks directly in @GoogleColab.
Just click ▶️ on the online lecture to open it in Colab.
On first run, the notebook automatically installs all required packages.
Jesse Perla retweeted
How can we account for inequality when measuring economic mobility❓
Many mobility measures focus on rank changes across generations or time.
But ranks only capture relative positions regardless of income/wealth differences.
New paper w/@mcgeeconomics: ifs.org.uk/publications/beyo…
