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.
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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.
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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!
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PS/ I think there is a LOT of denial about how unsuccessful the traditional econ slide format is at getting information across. Speakers rely on way more memory than there is.
There were some more questions about this so I added a bit to it. First, here's how this slide looked presented. (Time is not to scale.)
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I used PowerPoint. Its equation editor produces reasonably civilized output in presenter view, though it doesn't do TeX natively (I'm told Keynote does!)
Nothing fancy: used shapes and text to make a basic panel, and then cloned and modified it for consistency of formatting.
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(For what it's worth, basic TeX commands do work.)
A tool that made this much less painful than it might have been is Grouping and the Selection pane (Alt+F10). It lets you select exactly what you want, control what's in front of what, and show/hide entire blocks of content.
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That's it! Here's the source if anyone wants to play around with it.
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.
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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.
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(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.
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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:
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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.
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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.
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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.
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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?
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Our perceptions of some of the things we experience are deeply inaccurate. 🧵
Case 1: The vast majority of restaurants get few visits and go out of business quickly. This seems surprising because the typical restaurant you experience is busy and long-lived.
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The gap between reality and perception happens because few people experience any given unpopular, short-lived restaurant. Precisely because it is unpopular and short-lived!
The brilliant @CFCamerer, who gave this example, notes that it's not just curious but consequential.
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We, including aspiring restauranteurs, undersample unsuccessful restaurants so badly that it can make the restaurant business intuitively feel easy.
So too many people start restaurants who should have done other things instead.
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Excited to watch this talk by @renee_bowen_lyn : a model of echo chambers in social networks and how they take way less "behavioral error" than you might have thought to get started.
Behind the scenes there's a sort of puzzle based on a "naive martingale intuition": if there's abundant data and you understand the information process you're seeing, then a Bayesian should converge to accurate beliefs.
Here are some important statements that come up in economics:
"Nice estimators are consistent even in complicated models."
"Nice financial markets are informationally efficient."
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"Nice markets have price equilibria."
"Nice games have Nash equilibria."
The way these ideas are taught to Ph.D. economists in any field, even in core courses, involve very explicitly and extensively ideas extending ones in basic analysis.
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In particular, those ideas are: convergence (in fairly big spaces), integration and probability/martingales, continuity and fixed points.
Though you could get across aspects of these ideas at a high school level, econ grad school doesn't do them that way.
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