I participate a bit in the econ RA market on the hiring side, and I also hang out occasionally on the @Academic_Econ discord and talk with ppl looking for positions. Strikes me that labor markets (probably all markets!) are shrouded in a perpetual N-way "fog of war"
Each participant in the market sees a small bubble of info, that's available to and relevant to them. No single person in the market actually has the big picture at any particular point in time!
Applicants know the set of positions hiring, but not the screening criteria/hiring bar/etc. I know my own criteria and some colleagues', but other schools could be very different. I see roughly who we win against and who we lose to, but only in a small ball of programs around us
I also don't have a very strong incentive to keep track of broader market developments very closely. So, e.g. my sense is well-researched applicants know the relative placement rankings of different programs better than I do!
At some level this is just Hayek: markets can function well and achieve reasonable outcomes, even if everyone only has "local knowledge" and does local optimization
In theory I learned this in PhD first year? In practice I find it kind of amazing to see it work out in the real world
I use the term "fog-of-war" inspired by watching some free-for-all starcraft on youtube. 8 pros, last one standing wins. Fun, though crazy format. Everyone sees a small ball around themselves, and has to kind of infer global state/optimal strategy

I suppose everyone has to kind of guess who's in the lead, band up to kill the perceived leader, etc. I haven't ever played it but I can imagine theses guesses/common consensus can be hilariously off sometimes
It seems this is a nice analogy for labor markets (and markets more generally?) Everyone is kind of guessing the shape of the elephant from the tiny chunk they see, and what their buddies on neighboring chunks see
The nature of the system is that no one who's an active market participant actually sees the entire elephant, and their locally-extrapolated guesses as to what the elephant looks like can be hilariously far off

en.wikipedia.org/wiki/Blind_men…

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More from @AnthonyLeeZhang

27 Jul
Tl;dr: independent seller claims that:

- Seller launched product on amazon
- Amazon identified most profitable product, sold data to competitor
- Some product rankings shenanigans
Without commenting on the specifics of this case. This seems like a pretty tricky regulation/antitrust issue! Amazon also creates basics products, which directly compete with their sellers. Does Amazon have an unfair advantage, bc they have data from fulfilling sellers' orders?
One could argue grocery stores have a similar "unfair advantage" since, e.g. Walmart observes cookie sales, and can choose which kind of cookie is more profitable to sell. Is this also an unfair advantage?
Read 14 tweets
25 Jul
Conjecture: there is a decent (though not perfect) correlation between how well an industry/area/etc is doing, and how nice the median person in the area is
My guess at the reason for this is that people tend to be nicer when they are in environments of relative abundance, that feel positive-sum: on average, helping a stranger will be good for myself at some point
When people sense that growth is slowing down, there is not enough for everyone, perhaps the aggregate mentality shifts to be more zero-sum: my neighbor is generically my competitor, and helping them will tend to hurt myself slightly on average
Read 6 tweets
25 Jul
Prediction: in the near future, every luxury good (handbags/clothes, wine, cars, etc) will have an NFT associated, and Instagram will integrate and allow linking NFT's to photos. I post a photo of me with a fancy bottle of scotch, and the NFT to prove I actually bought it
70% of the point of buying luxury stuff is to instagram about it. Now, at the moment, a nontrivial % of ferrari instagrams are "hey look at this ferrari on the street let's pretend we own it". NFT's would solve this
Now, why couldn't I just borrow a friend's ferrari NFT and instagram using it? Well, if I use the code, my friend can't also use it, and again, 70% of the point of the thing is to post it on instagram...
Read 10 tweets
25 Jul
Interesting alternative property rights mechanism: when you buy a bottle of Laphroaig (who seem not to be on Twitter), you get this cool little certificate Image
When you enter it on their website (which only works on IE, chrome breaks it) you get this big certificate, which entitles you to a square foot of land at their distillery. They now owe me rent of a dram of Laphroaig per year. Yay! I am now a landowner! Image
Ownership rights are conveniently bundled with survey apparatus for verifying the dimensions of the plot of land, as well as weather-appropriate equipment for fending off attacks from wild geese, stoats, and otters while attempting to access the land Image
Read 7 tweets
22 Jul
Here's a set of questions. It seems a large % of people believe Tether is insolvent, which I define as: Tether does not actually have enough USD to pay $1 for each USDT, if everyone redeemed at once
Questions:

1. Why is a USDT still worth $1, if Tether is insolvent? Why isn't it worth like $0.9, or the market's best guess at how much USD assets Tether actually holds?
2. Couldn't a hedge fund or someone "attack" Tether to exploit the fact that Tether is insolvent?
Here is what I think about 1. The key is that USDT cannot trade below $1, _as long as Tether allows you to redeem 1 USDT for $1_. The argument is super simple! If ever someone was willing to sell a USDT for like, $0.99, you could just buy it from them and redeem it for $1!
Read 25 tweets
22 Jul
In tech firms, from what I've heard, it's rare for very early stage startups to have many data scientists. You need some product guys, hackers, and sales/marketing people. Make something, try to sell it, pivot if it fails, repeat until you succeed or run out of money
At this stage, I guess data science isn't needed because success or failure is obvious. Or sufficiently obvious not to need p-values. You have users, or you don't. You also realistically don't have big enough N's to actually run experiments even if you wanted to
Data scientists seem to enter later when firms are more mature. Changes are more incremental, and they're run through A/B tests, datasci folks pore over metrics, before deciding whether or not to shift. I think this is an intrinsically slower process
Read 9 tweets

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