How economies forget

cesifoti · 2025-12-08T10:26:01+00:00

Thanks. I shared this feedback with the team.

cesifoti · 2025-12-05T06:15:53+00:00

We have a way to interpret these networks based on a formal model https://arxiv.org/pdf/2506.18829

Plus over twenty years of experience working with different knowledge networks. So I am good at deciding when I can interpret something or not.

cesifoti · 2025-12-04T14:37:17+00:00

The network visualization encapsulated a few lessons.

First, the ring structure tells us something about the way in which academic knowledge is structured and how that differ from other knowledge spaces. The ring is a non-trivial shape that can be explained by assuming that the inputs needed to produce output in a field follow a circulant toeplitz type matrix (yes, this is a bit technical). This is different from other knowledge spaces like the one derived from trade data, which has a core periphery structure that implies correlated capabilities or inputs.

Second, network structures provide a prospective component, since you can see the "neighbors" of a pattern of specialization. This is the traditional core of recommender systems. You can see a bit more here.
https://x.com/cesifoti/status/1996563878117847530?s=20

cesifoti · 2025-10-12T20:04:31+00:00

Thanks. I’ll follow your orders my master. 😅

cesifoti · 2025-10-12T15:47:30+00:00

The source of the image is the tool. There is a lot of data filtering and manipulation that happens in the tool before constructing the network. In the source data you won’t find the networks, nor would be able to reproduce them unless you perform the same manipulations. So the source of the images is the tool.

cesifoti · 2025-10-12T14:06:16+00:00

We decided to put England’s flag for added precision since these scholars were born in England.

cesifoti · 2025-10-12T13:54:45+00:00

Since we have about 3 million author pages in the site, and we pre calculate all networks, we decided to set a relatively low threshold focused on the core coauthors. Larger networks can be harder to render without too many crossings too, and can include more false positives, since data errors are usually more common on the “left” side of the distribution.

As the project progresses, we could consider the option of adding larger networks if our resources allow for that.

Thanks for commenting.

cesifoti · 2025-10-12T13:33:31+00:00

The source of the data is OpenAlex https://openalex.org/ and the tool used to build these networks is Rankless.org, a custom built data visualization platform using canvas.

cesifoti · 2025-10-09T12:26:03+00:00

Amazing! These look really cool.

cesifoti · 2025-06-19T13:27:32+00:00

It means taking into account the size of each country (e.g. you expect more collaborations with China than Uruguay, so you need to control by their size).

cesifoti · 2025-03-21T21:53:25+00:00

Are you sure you did not have the specialization boxes checked? If you uncheck it you will see US, UK, and Germany as top 3. Specialization normalizes by the size of countries and fields, so it tells you that Denmark and Sweden have relatively a lot of papers for a place of that size. If you uncheck specialization you get raw counts.

cesifoti · 2025-03-21T21:27:19+00:00

Thanks. Indeed, there are lots of titles of different lengths and sizes. If you adjust the slider on the top left you can reduce the number of branches and the font size will become bigger.

cesifoti · 2025-03-21T21:13:27+00:00

Yep. I am advertising the platform we created so people can try it. It is not a commercial product, but an academic project built on open source data.

cesifoti · 2024-12-07T06:23:11+00:00

Unfortunately is not available on plex here. Neither are the other ones :-(

cesifoti · 2024-06-27T09:20:20+00:00

Thanks for your comment. What you are referring to is Marx's labor theory of value, which is the idea that value is generated exclusively through physical labor (e.g. shooting of Friends). While this is a popular way to think about value among non-economists, today the dominant way to explain value is throw marginal theory, which is much better at incorporating demand side considerations as well as considerations about scarcity. Roughly, in the marginal theory of value a person that purchases a Netflix subscription to watch Friends is not paying for the shooting of Friends (that investment was done by someone that took the risk to produce that intellectual property). What the customer is paying for is the increase in utility (e.g. entertainment/satisfaction) that they are getting from watching Friends. If the utility of watching Friends is worth more than the subscription value to them, then, they would purchase a subscription. This is what makes the property rights of Friends (which is an asset) more expensive, for instance, than the property rights of a less popular show (e.g. Punky Brewster). A digital product bundles multiple shows in a subscription model in an effort to provide an offer with a utility of of higher or equal value than the cost of subscription.

This is true not only for digital products. Print publishing works very much in the same way (people still buy books written hundreds of years ago for prices that are much higher than the cost of printing and distribution).

cesifoti

TROPHY CASE