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

2 Sep
A real-world high stakes game of experimentation with externalities:

Last night at 10, my car was at the front of several miles of cars on the Garden State Parkway all stuck behind a segment of road 3-5 feet underwater. You could try to drive through if you wanted,
but most people were dissuaded by the half dozen stalled/flooded cars in the water.

For about three hours, one vehicle every 15 minutes or so would go for it. Whether it succeeded depended on its type, the path it took, and the water depth.
The interesting thing is that a failed experiment (trying and having your car stall in 3 feet of water) has considerable private cost: deeply flooded cars are totaled, and the cost of even a lucky recovery in such a case is more than a few thousand dollars.
Read 8 tweets
29 Aug
Sometimes faculty complain about the stubborn Ph.D. student, who seems unaffected by advice. Talent and energy are risk factors for this disease, and, worse, is closely related to personality traits of many successful academics.

A few random thoughts.

1/
What "bad stubborn" looks like from the advisor perspective is that you thoughtfully engage with the work, repeatedly say something (that you feel is) REALLY IMPORTANT that should affect the project, and perceive it not to be affecting the project or the student's thought.

2/
A friend wishes they could tell students one cheat code for success. When faculty say, "This seems like a question you can answer in your project and people would really care about the answer," *actually try to do that*, or at least have serious conversations about it.

3/
Read 11 tweets
14 Jun
An applied mathematician I know thinks it's hilarious that economists care about formal rigor so much more than, e.g., applied physicists do.

Rigor, he says, is valuable, but other inputs currently seem to have a much higher return for advancing economic theory.

1/
For example, if our theorizing about long-run outcomes of social learning falls short of our potential, it's not because we forgot to check a subtle condition in applying the martingale convergence theorem in our model of their Bayesian behavior.

2/
(their = the agents').

"His people" (applied mathematicians, applied physicists) would not worry about that. Instead, they would quickly work through much more "theory," but without great rigor, and use the results to refine the collective decision about how to continue.

3/
Read 10 tweets
10 Jun
A few simple facts that some people find surprising the first time they hear them.

Imagine $100 is behind door A or B and I give you independent hints about which. The hint says either A or B but is right only 55% of the time.

First hint is worth $5, second hint is worth... $0!
Why? Because the second hint never makes you *want* to change your decision. (Think about the four possible hint combinations.)

This is a key idea behind a beautiful paper by Meg Meyer, here:

2/
If you want the second hint to be useful, you need to make it biased, "favoring" the leading option, so that if it comes back a surprising negative against the leader, you might actually change your decision.

Meyer uses this to derive implications about organizations.

3/
Read 7 tweets
28 Apr
I've been playing around with a virtual talk format that's different from traditional slides, which deals with my biggest complaint about slides: lack of persistence of information.

Almost always, I want to see "setup" again during the first result/example, but it's gone.

1/ Image
In the format here, each panel is basically a slide, and I reveal these panels one by one.

That might be too small on a projector screen. But when everyone is in front of a monitor anyway, this seems to be better for giving the "lookback opportunities" I would want.

2/
Overall, I've never learned/understood better than during college math classes where professors slowly filled up six nice sliding boards.

In an ideal world, we could have this but with prepared content.

Probably not happening soon, but virtual talks allow an approximation!
3/3
Read 8 tweets
24 Apr
David Blackwell would be turning 102 today.

He's best known for the Blackwell information ordering, the way to formalize when some signals give you more information than other signals.

A thread on Blackwell's lovely theorem and a simple proof you might not have seen.

1/
Blackwell was interested in how a rational decision-maker uses information to make decisions, in a very general sense. Here's a standard formalization of a single-agent decision and an information structure.

2/
One way to formalize that one info structure, φ, dominates another, φ', is that ANY decision-maker, no matter what their actions A and payoffs u, prefers to have the better information structure.

While φ seems clearly better, is it definitely MORE information?

3/
Read 18 tweets

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