📢 𝗡𝗲𝘄 𝗽𝗮𝗽𝗲𝗿 alert! We document how 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗶𝘀 𝗿𝗲𝘀𝗵𝗮𝗽𝗶𝗻𝗴 𝗱𝗲𝗺𝗮𝗻𝗱 𝗳𝗼𝗿 𝗳𝗿𝗲𝗲𝗹𝗮𝗻𝗰𝗲𝗿 𝘀𝗸𝗶𝗹𝗹𝘀. 💼 💻

📊 We analysed 𝟯𝗠+ 𝗷𝗼𝗯 𝗽𝗼𝘀𝘁𝘀, used embeddings to cluster them into 𝟭𝟬𝟬+ 𝗳𝗶𝗻𝗲-𝗴𝗿𝗮𝗶𝗻𝗲𝗱 𝘀𝗸𝗶𝗹𝗹𝘀, and LLMs to classify them as s𝘂𝗯𝘀𝘁𝗶𝘁𝘂𝘁𝗮𝗯𝗹𝗲, 𝗰𝗼𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝗿𝘆, or 𝘂𝗻𝗮𝗳𝗳𝗲𝗰𝘁𝗲𝗱.

🚨 New paper alert! Economic Agent Based Models have become increasingly data-driven. What does that mean and what impact can this have? arxiv.org/abs/2412.16591 📖✨

Chapter with @marcopangallo.bsky.social forthcoming in @sfiscience.bsky.social volume #Economics #ComplexSystems Part IV

👇🧵

Please get in touch with us if you are thinking about a PhD or postdoc and are interested in these topics.

Apply at

𝘗𝘰𝘴𝘵𝘥𝘰𝘤 -> lnkd.in/dES76TZJ

𝘗𝘩𝘋 -> lnkd.in/duCkZDni

8/8

Finally, we will (i) model the impact of specific floods, such as the September 2024 "Storm Boris" floods, and (ii) test policy mixes that take advantage of the joint focus on household heterogeneity and supply chain resilience. 7/8

We will also do substantial data work, that could be interesting by itself. More specifically, we will build spatially-explicit synthetic populations; reconstruct production networks across firms; use catastrophe modeling to estimate physical risks of floods across space. 6/8

We will also look at impacts on small firms through improving modeling of transportation, pricing and input substitutability, taking into account the characteristics of households that work within these firms. 5/8

For instance, we will consider the contributions to production of workers with different skills, so that we can evaluate the effects of disaster-driven displacement -- a key impact on well-being -- across the household distribution and on the economy as a whole. 4/8

We will improve the realism of these models to better understand the impacts on individuals, households and firms at a more detailed level. 3/8

We will start from our agent-based models on the economic impact of the Covid-19 pandemic (sciencedirect.com/science/arti..., nature.com/articles/s41...), and adapt them to focus on natural disasters, mostly floods. 2/8

DISADIST-ABM: 📢Open positions

With Anton Pichler and @maria-drc.bsky.social we got a grant to model the well-being impacts of natural disasters beyond standard economic measures.

We are looking for a 3-year PhD student and 2-year postdoc, deadline 15/01.

More info 👇
1/8

t is my only solo paper, from the last chapter of my PhD thesis. Happy to publish it in the JEBO special issue on complexity economics that I’m co-editing (all editor papers went through the editor-in-chief of the journal to avoid conflicts of interest). t.co/wugwCWtrM7

This is not the first paper having this idea, but it is the first that quantitatively tests the synchronization hypothesis. It also provides an eigenvalue-eigenvector decomposition that helps understand how synchronization and shocks interact through the trade network.

The empirical level of comovement can only be matched by a combination of exogenous shocks and endogenous cycles. Shocks alone cannot generate enough comovement, as is well-known in international macroeconomics. Thus, business cycles are at least in part endogenous.

If business cycles are exogenous, economies live in stable steady states and positive correlation of economic activity ("co-movement") comes from shock propagation. If they are endogenous, co-movement comes from synchronization of non-linear dynamics through trade linkages.

A debate that is almost as old as economics itself is what recessions and booms originate from. Are they driven by shocks external to the economy, like natural disasters, or by forces internal to the economy, such as debt accumulation? Are business cycles exogenous or endogenous?

If business cycles are (at least partly) caused by forces endogenous to the economy, rather than by exogenous shocks, it is much easier to explain why economic activity tends to co-move across countries.

New paper in JEBO: authors.elsevier.com/a/1kB5Vc24b7...

🧵👇

Blog post with a synopsis of the course: marcopangallo.it/blog/2024/06...
Course materials: fisica-sc.campusnet.unito.it/do/didattica...
Instructions: start with the syllabus and the summary table to get an idea of the topics. Then, you can look at slides, lecture notes and Jupyter notebooks. 7/7

I am now sharing the course materials and seeking feedback from the community, which will be as valuable as students’ feedback in refining the course over the coming years. My hope is that this course will eventually evolve into a textbook on complexity economics. 6/7

- No equations on the slides. Use chalks and the blackboard.
- For any topic, start giving the big picture, then discuss a specific application in great detail, finally zoom out to provide an overview.
- No choice of topics to promote my own research. 5/7

Some principles that guided my choice of topics and teaching philosophy:
- Economics is about the economy, not human behavior under scarce resources.
- Though motivated by data and experiments, make the course theory-heavy. When possible, privilege analytical calculations. 4/7

To do so, I had to distill the contributions of complexity economics that were most meaningful for the students. I tried to strike a balance between concepts & methods, blackboard calculations and Jupyter notebooks, theory and data, and traditional and complexity approaches. 3/7

My students had no prior knowledge of economics, so I aimed to teach them the fundamentals of economics while showcasing the contributions of complex systems to the field. To my knowledge, there was no course like that, so I had to design it from scratch. 2/7

📢**A master-level course on complexity economics** Over the last few months, I’ve taught a 48-hours course in the master of Physics of Complex Systems at
@unito
. Here's an overview, and at the end I'll link to course materials - feedback welcome! 1/7

Calling all complexity scientists whose work addresses 21C challenges, from climate change and net zero transition to polarization. Special issue of JEBO, proposals due Feb 15 2024 #econsky polisky www.sciencedirect.com/journal/jour...

Shoutout to my coauthors @maria-drc.bsky.social, @mattk7.bsky.social, @estebanmoro.bsky.social (the others are still on the other platform)

For more info on background and results, see our "behind the paper" blog: socialsciences.nature.com/posts/the-un.... All code is available as a Docker image here: doi.org/10.5281/zeno.... 12/12

This paper is the result of 3 years of work of a team of economists and epidemiologists united by a common background in complex systems. Being all interdisciplinary scientists made it for a "deep" collaboration that went much beyond discipline-specific contributions. 11/12

We calibrate and validate our model on the first wave of Covid-19 in NY.The ABM reproduces epidemic & economic statistics at the aggregate level and across industries and income groups. Eg. we show that the poor got more unemployed but reduced consumption less than the rich 10/12

We rely on the quantitative interpretation of our results because the model is initialized from real-world, granular, data from the New York metro area, including the mobility traces of half a million people and the physical locations of shops, offices, construction sites.. 9/12

These results are only possible because the agent-based model *quantitatively* simulates the epidemic and economic outcomes of half a million individuals heterogeneous by age, income, etc., getting infected and consuming in different places, settings and industries. 8/12