Ben Golub 🇺🇦 🕸️ Profile picture
Sep 17, 2021 16 tweets 6 min read Read on X
Hard to convey my excitement at seeing an argument by @ojblanchard1 for a networks perspective on three seemingly distinct kinds of fragility.

This is something that I have worked on for a few years now, and I hope that network theory can really help.

1/
I think it's right that there are commonalities between the fragility of

(i) production when institutions are shocked;
(ii) financial systems when asset values are shocked;
(iii) supply when shipping technology is shocked.

2/
One perspective that network theorists have been especially interested in is that there is something qualitative about some collapses: it's not just a matter of some things working worse, but the whole system entering a crisis.

3/
In two papers published in 2014-15, some network theory was brought to bear on the financial crisis. Acemoglu, Ozdaglar, and Tahbaz-Salehi showed that the same structures that are robust to normal shocks are especially bad for the rare large shocks.

citeseerx.ist.psu.edu/viewdoc/downlo…

4/
That is the large shocks trigger a crisis.

Simultaneously, Elliott, @JacksonmMatt and I looked at large stochastic financial networks, and found that diversification and integration can both exacerbate the network linkages that make a network susceptible to crises.

5/
Link mfm.uchicago.edu/wp-content/upl…

Blanchard emphasizes a similarity between domino effects in financial networks and dominoes of failure in real production. Though the economics is different, this analogy seems potentially fruitful!

6/
A key ingredient in the analogy is the high complementarity in production memorably discussed by Kremer (QJE 93). Complementarities in production are a huge deal! In finance, a few counterparties failing can destroy you. In production, it might take one small missing part.

7/
Blanchard and Kremer traced the implications of this when there are large institutional changes. But the aggregation of O-ring production has important implications in crises like the present one, with shortages everywhere.

8/
Of course, firms optimize against these risks. They maintain inventories and maintain multiple suppliers. What are the aggregate implications? There are two literatures that are relevant here. One is from operations -- e.g.,

pubsonline.informs.org/doi/abs/10.128…
There can be severe externalities, and it may not be in firms' interest to make themselves more robust: by correlating the kinds of shocks they are exposed to, they might make more profits but make the system more fragile in the aggregate.

10/
There are two threads one can follow from this point. One of them incorporates some of these forces into the production functions of a canonical networked production model and analyzes its reaction to (formally small) TFP shocks.

onlinelibrary.wiley.com/doi/abs/10.398…

11/
An exciting, growing literature using standard macro models to explore these themes is surveyed by Carvalho and Tahbaz-Salehi

annualreviews.org/doi/full/10.11…

12/
Another thread is more focused on network theory and investigates when we see very large cascades of shutdown. This is more analogous to the financial networks modeling via graph theory, and is studied in this recent paper:

bengolub.net/SNFF

13/
Supply network contagions can look quite different from financial contagions. This is because real complementarities are different: you need *all* inputs to produce (but you can have multiple options for sourcing each of them).

14/
Seoarately, Matt and have just finished the first draft of a survey trying to trace the commonalities Blanchard emphasized through a network theory lens.

(We're not quite ready to circulate, but I'll tweet soon!)

15/
We have a hope (which we articulate systematically in the survey) that network theory can help in unifying our understanding of the forces behind sudden, systemic disruptions.

15/15

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More from @ben_golub

Apr 27
Small observation on research trajectory in economic theory.

When you first try to do it, most projects you propose are
(i) ridiculous/uninteresting to experts or
(ii) trivial
or, often, both!

This phase is very difficult and it's a big thrill to escape it.

1/
To escape, you have to sense

(i) what directions other scholars consider substantively interesting/promising;
(ii) what experts consider non-obvious.

Judging (i) and (ii) and doing well by these standards is pragmatically what makes young theorists successful
Succeeding at these is a fun, challenging sport.

So fun and challenging that it permanently reshapes how we direct our research.

I think perhaps because failing on these metrics is so painful in youth, once you can do well on them, you really prioritize that.

3/
Read 7 tweets
Jan 28
The notion that amazing papers should not get rejected is an odd one.

Any genuinely important idea is more likely to be strongly disliked. (Some reasons below in a short thread.)

To publish important work, editors have to be bold and overrule some negative experts.
Non-exhaustive list of reasons

1. The first technical work in a new paradigm is often crude and simple relative to the sophisticated and elaborate papers written late in a paradigm, when methods are being polished by a large community of experts in those methods.
2. Good ideas are often counterintuitive and have obvious drawbacks. They often prove valuable mainly through their later consequences.

Experts see the reasons not to pursue the counterintuitive paths: often, they have thought about and rejected these paths themselves before.
Read 7 tweets
Nov 23, 2023
I generally recommend

1. Constructing an n-by-m matrix whose rows are people and columns are issues (or dimensions of issues).

2. Finding the largest few and smallest few singular values.

3. Looking at the corresponding singular vectors in issue space.

(cont.)

1/
The top few singular vectors in issue space will tell you about "bundles" of issues along which there are considerable distances in the group.

(If these have high singular values, that corresponds to those differences explaining a lot of the group's variation in opinions.)
For example, you might find an axis of disagreement between Buttigieg and Warren-inclined democrats, visible across issues.

Buttigieg people would be in favor of things like market-rate housing, while the Warren people would focus on poking Jeff Bezos with a sharp stick.

3/
Read 11 tweets
Nov 23, 2023
Talking to GPT4 about the Sylvester-Gallai Theorem and formalizing it

1/ Image
2/ Image
3/ Image
Read 7 tweets
Oct 22, 2023
Some new progress in math makes me hopeful about finally making progress on a big open question about opinion dynamics in social networks.

The question is: in simple models where people update opinions by averaging in friends' opinions, how long can polarization persist?

1/
In a 2012 QJE paper, Matt Jackson and I

(i) studied "time to consensus" in such learning by adapting the standard EIGENVALUE analysis of convergence times for reversible Markov matrices

(ii) showed how to approximate the answer knowing only "GROUP-level" linking data.

2/


Image
(The second part matters because group-level or "average patterns" linking data is often MUCH, MUCH easier to collect than a complete network census.)



2b/ web.stanford.edu/~arungc/BCMP.p…
Image
Read 13 tweets
Jun 17, 2023
For those who (like me) were interested in the "GPT can ace MIT" paper,

Here's a great short writeup by three MIT EECS seniors explaining the many things wrong with analysis.

dub.sh/gptsucksatmit
A few quick notes.

Chowdhuri, Deshmukh, and Koplow (from now on CDK) point out some things about the methods that would probably be surprising to those who excitedly retweeted the flashiest claims.

First, GPT-4 was often fed the same problems that it was asked to solve. Image
Finding identical problems in the training set is bad news.

But I disagree with CDK that the method would be "all right" otherwise -- priming with "additional context" (template problems and solutions) is very different from what a student is faced with on an exam.
Read 10 tweets

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