Remember that time economists used a gravity model to find ancient lost cities from the Bronze Age?
If you do or you don't, check out this thread🧵
The authors gained access to a collection of almost 12,000 deciphered and edited texts that were excavated primarily at the archaeological site of Kültepe, ancient Kaneš.
The ruins (pictured) are located in central Turkey, in the province of Kayseri.
The texts look like this.
They were inscribed on clay tablets in the Old Assyrian dialect of Akkadian in cuneiform by ancient Assyrian merchants, business partners, and their family members.
This tablet is dated to between 1930 and 1775 B.C.
The tablets were all from between 1930 and 1775 B.C., and 90% of the sample came from just one generation of traders, between 1895 and 1865 B.C.
The reason is that Kaneš experienced a major fire in 1840 B.C. and the commercial archives in the city were sealed off.
Tablets were largely business letters, shipment documents, accounting records, seals, and contracts.
A typical shipment document or expense account in which a merchant would inform partners about their cargo and expenses would read like this:
Some business letters would contain information about market and transport conditions, like this:
The tablets are spread across the world in museums and institutions, but many have been transcribed.
The transcribed ones mentioned 79 cities distributed across modern-day Iraq, Syria, and Turkey and 2,806 mentioned at least two Anatolian city names simultaneously, like so:
That tablet identified three shipments: Durhumit to Kaneš, Kaneš to Wahšušana, and Durhumit to Wahšušana.
So the itinerary is A→B→C, and there were 227 of these, with 391 examples of travel between city pairs.
Specifically, 25 city pairs: 15 known (gray), 10 lost (black).
Using trade among known cities, they estimated the distance elasticity of trade (how sensitive trade btwn cities is to the distance btwn them), so they could estimate the prbblity of shipments from city i to city j given their distance
Thus, probable locations for 10 lost cities
These estimates largely concurred with those of historians, and since the historians' conjectures weren't used in the model, this suggests people should start pursuing those estimations.
In fact, this modeling exercise might help to decide among the different proposals made by historians.
But the authors weren't done. They supplemented their analysis with data from merchant itineraries. For example, consider this letter:
That letter was submitted to the Assyrian port authorities at Kaneš from emissaries in Wahšušana, and it described how missives would travel through two different routes:
Wahšušana→Ulama→Purušhaddum
W→Šalatuwar→P
But only Wahšušana, Ulama, and Šalatuwar are known cities.
Using every multistop itinerary, a model with just two constraints offers a lot of info. The constraints are simple:
1. When deciding itineraries, merchants like direct routes. 2. Caravans have to make stops to rest, replenish supplies, feed pack animals, and make side trades.
With estimates constrained to regions that are admissible given those constraints (dashed lines), the locations of the newly-identified lost cities are now more certain!
With the exception of Purušhaddum.
But how do we know this method works?
Easy! Just lose known cities and see if the method rediscovers them.
As the picture shows, the average distance between estimated and known city locations wasn't huge. In fact, estimates were a median of 33km away (mean = 40km).
This method also helps to identify the names of sites that people have continued living in, like Kırşehir Kalehöyük, which might have been located under where the Alaaddin Mosque and a high school were later built.
There are other interesting findings here, too.
Consider this: geography has deep and persistent impacts on the economy of the area, and cities tend to show up where there are "natural roads".
Ancient cities were estimated to be larger when the natural roads were better!
And, modern cities are larger when nearby ancient cities were estimated to be larger as well.
The deep geographic reasons for cities to crop up in certain locations are still powerful forces today!
And for the real nerds, Zipf's law looks to basically hold for ancient city populations.
There you have it: economists might have discovered the locations of ancient lost cities from the Bronze Age, and supported a number of other fun facts while they were at it.
Only time will tell if these discoveries end up being true 🤞
Link:
The model the authors used was the gravity model: the workhorse model of trade.
The biggest news of the day is not so much that @RobertKennedyJr was confirmed by the Senate, but what he's going to do next.
@realDonaldTrump just issued an Executive Order making it official:
America stands against chronic disease and closed science🧵
The first thing the EO does is outline the problem
It talks about how unhealthy America is, how unacceptable that is, and how we have a duty to change that
We do: Americans should not just be the richest people in the world, they should be the hottest, healthiest, and strongest
Now beyond outlining the problems America faces, the Order outlines some policy prerogatives that will be front-and-center during this new administration.
I want to preface something here: Regardless of what you think about the people involved, something here will make you happy
The biggest news of the day should once again be about DOGE.
A new Executive Order was passed a few minutes ago.
It empowers DOGE to spearhead the complete reorganization of the federal government🧵
The first part of this Order is simple:
The OMB will put out a plan to make the federal workforce smaller and more efficient, including a stipulation that agencies must remove four existing employees for each new hire, with some exceptions.
The second part is meatier.
New hires have to be approved by newly-installed DOGE Team Leads in each agency. These Team Leads will report what goes on in the agency they're assigned to on a monthly basis.
It seems shocking nowadays, but the best major American city for a young person to be in as late as 1980 was Detroit.
The Motor City was America's richest city, not too long ago. Plenty of you reading this will remember a prosperous, beautiful Detroit.
If you're in tech, you might have noticed that a disproportionate number of your friends are from Michigan, and specifically, from suburbs like Troy, Novi, Farmington, Royal Oak, Rochester, and so on.
When Detroit went, so did the reasons for talented young people to stay.
Antibiotics are one of those things where I'm a hypocrite about policy.
I think I should be able to stock up, but also that most people should still have to go to the doctor for a prescription as-needed.
I want to avoid what happens in the developing world:
In the developing world, antibiotics are widely available, with little if any regulations over them. And this makes total sense, because there aren't that many doctors. Poor countries, few doctors per capita, limited access to by-the-book healthcare...
Loads of self-medication!
That self-medication results in lots of people taking an informal approach to medication usage. For example, if you buy a bunch of antibiotics in India, you might get them in a bag that has a reminder to finish your dose printed on it, even if you feel better before it's through.
On the left, you can see a map of corruption indexed by the number of mob crimes per 100,000. On the right, you can see corruption indexed by how much people steal from the public purse.
And in the middle, a map of inbreeding.
Clannish people do clannish crimes.
Though it's noted in the image, I want to reiterate that the corruption measure on the right is reverse-coded, so higher values indicate lower corruption.
The correlations with consanguinity are 0.65 and -0.52, and they hold up splitting the country in half and in other specs.
Outside of Italy, in the wider world, corruption perceptions also relate to consanguinity.
The correlation is high, and far from perfect, but both measures contain error, so keep that in mind.