What are the scientometric statistics of the 2026 winners of Nobel Prizes in Science? Rankless.org decomposes impact by geography, institutions, topics, and colleagues, using data on 98M papers, 2.1B citations, and 4.3M authors (so you might be there), from OpenAlex. Here we start with the hit-paper* trajectory of two Nobel Prize winners in Medicine: Hegemann and Deisseroth. Hegemann's 6 hit papers include the steps to identify the proteins that were later used in Optogenetics. Deisseroth has instead 58 hits, which means that he has been co-authoring more than one hit paper a year--on average--during his career (he is 54 years old). *Hit papers have 1.5x times the citations needed to enter the top 1% of a year and topic Continue 👇

Do you have what it takes to forecast International trade? Today, we are launching AI4trade, a global challenge where teams compete for cash prizes by submitting their best trade forecasts. But it comes with a couple of twists: 1. This is a future out-of-sample challenge, so teams will need to forecast data that will not exist at the time of submission (impossible to train to the test 👹). 2. Forecasts must be better than simply using the latest trade data available at the time of submission (e.g. using July data as a prediction for October numbers ⏰). This should be a fun global competition. People from all parts of the world are welcome to participate and all methods are allowed. We hope the challenge stimulates more work at the intersection of AI and economics. Link in the first comment: 👇

Interested in the latest developments on economic complexity? This year's CEC will be held in the beautiful city of Toulouse. We have two amazing Keynotes Corinne Autant-Bernard & Ernest Miguelez, and a half-week packed with activities, starting with a one day Summer School followed by two days of conference. Wanna join? Please register and submit. Link in comment.

Today, the Nobel prize recognized the work of Acemoglu, Johnson, and Robinson. In a recent paper, we reproduce their classic Atlantic trade result when validating our machine learning derived historical estimates of GDP per capita.

New PNAS paper. Historical GDP per capita data is scarce, but data on the places of birth, death, and occupations of famous individuals is abundant. In this paper we estimate the historical GDP per capita of hundreds of regions in Europe and North America using a machine learning model that leveraged data on about 500k famous biographies. Our estimates more-or-less quadruple the availability of historical GDP per capita estimates for the last 700 years. So why use biographies to augment historical GDP per capita data? Biographical data contains information about people who might have contributed directly to economic growth, like James Watt, or that were attracted to wealthy places looking for patrons, like Michelangelo. So we--mainly Philipp (@philippmkoch)--used this data to construct hundreds of features describing each European region. Then, we trained a machine learning model to find the features that explained most of the variance in a cross-validation test, where we split regions multiple times into a training set and a test set. On average, the model explained about 90% of the variance in GDP per capita of the regions it had not seen during training. But we wanted to go further, and Philipp really went to town by looking at different ways to validate our estimates. We found our estimates correlate positively with historical measures of wellbeing, church building activity, urbanization, and body height. We also used these measures to reproduce the basic Atlantic trade result of Acemoglu, Johnson, and Robison and to explore the economic consequences of the famous Lisbon earthquake of 1755. But what I personally loved most about this project, other than working with @philippmkoch and @ViktorStojkoski, is that it shows that we can use machine learning methods not only to explore the future, but the past. There is a bright and growing future in the use of machine learning for economic history. Hope you enjoy the paper and the data. You can find links to the paper and a data exploration tool in the first comment.

**New DataViz Project** Curious about academic impact? Tired of rankings? Today we are introducing Rankless (rankless.org/) a new data exploration platform that can help you explore the academic impact of thousands of universities. All universities produce impact that is specific to certain topics and geographies, but rankings flatten that information. Rankless wants to change that. Consider a comparison between the University of Utah and the University of Vienna, two universities ranked similarly in the Shanghai ranking. These universities differ in their geographical and topical footprint. The University of Utah specializes in Neuroscience and Medicine whereas the University of Vienna specializes in Physics, Astronomy, and Environmental Sciences. Their geographic impact is also quite different. Utah receives a large fraction of citations from medical centers in the U.S., Canada, and Israel, whereas Vienna receives many citations from technical institutes in Austria, Germany, and Hungary. These differences are easy to explore in Rankless but hard to see in rankings. Rankless was developed by a talented team at the Center for Collective Learning at Corvinus University (@uni_corvinus). It was brought to life by Endre Mark Borza (@endremborza), a Hungarian economist and data engineer at CCL with the help of Máté Barkóczi, a Hungarian designer form MOME, and Veronika Hamar executive director at CCL. By moving beyond rankings, Rankless offers a fresh perspective on how universities influence each geography and topic, emphasizing diverse forms of impact and providing a richer understanding of academic influence. To learn more visit Rankless.org

Have you played the Wikipedia navigation game? And, are you any good at it? In this new paper @ManranZhu from CCL together with @TahaYasseri and Janos Kertesz asked hundreds of people to play the "Wikipedia navigation game:" an online competition where you must navigate from one Wikipedia page to another using only links. For example, from the Barack Obama page to the Vincent Van Gogh page. They then looked at several characteristics of the participants, such as their age, gender, and personality, and found that those who completed the game faster or in fewer clicks tended to be younger, spoke a second language, and were more familiar with the game, Wikipedia, and the target page. They also found that male participants were better at the speed game, but males and females performed equally well in the least-clicks game. Interestingly, personality characteristics (the "big 5") did not significantly explain variance in performance (once other characteristics were controlled for) If you are interested in this line of research you can access the paper at: nature.com/articles/s41598-0…

***New Paper*** Many of you are familiar with urban scaling, the idea that outcomes such as innovation and crime grow super-linearly with city size. But what if I told you that the "network of cities" was better at explaining some of these outcomes? In a new paper led by @xiaofanliang we explore this hypothesis by estimating networks for cities in the United States and China. In these networks, cities are connected based on social media friendships, travel patterns (human mobility), and scientific co-authorship (see figure). We use these networks to explain the patenting activity of American and Chinese cities, and ask whether a city's patents are better explained by population (urban scaling hypothesis) or by their centrality in these networks. We find that after we take the measures of centrality into account, population is no longer a significant predictor. In China, all three networks help explain innovative activity. In the US, the scientific co-authorship network is the most robust predictor. Network scaling seems to trump urban scaling when it comes to innovation. To explore this hypothesis further, we compare cities with similar population and different levels of connectivity, such as Corvallis (high connectivity) and Cumberland (low connectivity) in the US, or Shenzhen (high connectivity) and Handan (low connectivity) in China. This reveals that differences in connectivity explain the residuals observed in the urban scaling relationship between population and patenting. Certainly, this was not easy paper to make. It required collecting network level data for hundreds of cities in two large countries. But the prize was that it helped us explore beyond urban scaling, showing us that when it comes to patenting activity, a city's network connectivity explains more than its population. While our results are not causal, they are consistent with the idea that innovation depends on the flow and recombination of ideas. The paper also provides some insights about the network geography of China and the United States. China is a diamond (rhombus), with a Beijing-Shenzhen north-south axis and a Shanghai-Chongqing east-west axis (with Wuhan in the middle). But when we remove the geography, we see networks centered on Beijing, at least for the social media and the scientific co-authorship network. The US has more of an arc, or flyover structure, especially when it comes to the co-authorship networks linking the east and west coasts. In the US, social media friendships revolve around New York City, human mobility around N.Y.C and L.A., and science around Boston. This was a long project (about six years from idea to publication), so I am happy to finally see it published. My congratulations to @xiaofanliang who in the process became an assistant professor at the University of Michigan. Enjoy the networks and what they teach us about the world. sciencedirect.com/science/ar… centerforcollectivelearning.…

The Center for Collective Learning seminar series is back. This year, our opening speaker will be Linda Argote from CMU, one of the most influential scholars in organizational learning. Linda will be discussing recent work on transactive memory (knowledge of who knows what). The webinar is open to the public and free to register at: centerforcollectivelearning.…