A few observations to add, in the form of common mistakes.

A. "The audience as computer" aka "important things need to be written to memory once." In fact, nobody learns anything except the simplest things the first time. Important points should persist across a few slides.

1/
E.g. if there is an important, longish Bellman equation, DON'T just put it up once and move on. Have it hanging around on several slides.

Blackboards talks are better because of persistence. Most speakers using slides ignore that things take time to absorb.

2/
B. "Literature details before content."

It's amazing how often you hear, at minute 7, "but our eigenvalue condition will actually be in terms of the Laplacian matrix, rather than the usual adjacency matrix as in XYZ (2014)"
before anyone can follow this.

3/
Put contrasts and details where a non-specialist member of your audience (e.g., non-networks theorist, or non-theory economist for a job talk) can appreciate them, not where they went, say, in your paper.

4/
C. Alluding to important background information without teaching it.

If you ever find yourself saying something like, "And as we all remember from X," where X is any literally almost any paper, rethink your model of the audience.

5/
A good talk presents not just your paper but teaches the immediate research context (in a focused and relevant way) when it's needed. If you find yourself talking to an idealized audience of your referees, remember that your actual audience is never that audience.

6/
D. Following conventions in the field, without realizing that the modal talk is unsuccessful.

Think of the best talk you've seen in the last year. Are you trying to imitate ITS best attributes, or enacting a template you're personally bored and confused by?

7/
E. Not treating a talk as an important performance.

A senior theorist told me that after his first big talk, a very senior theorist told him that while a seminar looks like no big deal, it's Carnegie Hall for us - "Why didn't you treat it that way?"
The best speakers take the craft very seriously. They rework important visuals many times, get all the feedback they can, kill their darlings, pare down tirelessly, etc. A good talk takes many days of work, at a minimum, to get right.

9/9

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More from @ben_golub

17 Sep
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/
Read 16 tweets
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

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