July 2025 Updates
Beauty and citations
Time to update posts again. I know it is not that long ago since the last update post, but there are just so many interesting things I want to share with you. The first thing observant readers probably have already noticed: Every Substack post now has its own historical image. You can watch all of them by browsing through the archive. I tried to find ones that fit the general vibe of the post and I am quite happy with the result. If you are curious about those pictures, they are the work of Albert Kahn and the photographers he hired. Kahn was a rich banker, who liked the arts and one day thought: “Wouldn’t it be cool if we photograph everything on the planet?”. And as he had the means to do so, the project became a reality. He hired several photographers, who took a total of around 70,000 pictures all around the world. I find them quite fascinating. They often have a kind of silent beauty and rich texture. If you want to learn more about this, here is an article and here is an archive of the majority of the pictures.
Another important piece of news here is that every entry in this blog can now be cited. You can find all entries on Rogue Scholar. Every post gets a DOI with rich metadata (it even includes the references). More info about this below.
And now the actual updates.
Trade collapse
Food trade generally is a good idea, as it gives you access to more resources than you would have otherwise available. However, this also exposes you to the agricultural production shocks of other countries. I added a paper by Keys et al. (2025) to the post, which explores how trading food can make us both more and less vulnerable to climate shocks, depending on who we are trading with.
Another paper which digs deeper into the danger of trade amplifying shocks throughout the system is by Keys et al. (2025). They wanted to understand how climate hazards are transmitted through global food trade. More specifically, they focussed on how both hot and dry and hot and wet climate shocks reduced agricultural production and how this reduction impacted both the producing country and their trading partners. To model this, they ran a climate model (CESM 2) 100 times for 10 years in each run. Every run has slightly different starting conditions and thus develops differently over time. In total these 100 runs represent the plausible variability of the climate over 10 years. The variability they found thus allowed them to assess which parts of the Earth have a chance of being exposed to climate shocks.
With this knowledge they could assess how much of our global agricultural production might be affected by those climate shocks. To check how these production shocks might then impact trading partners, they used trading data to understand how much food countries are importing and from where. This allowed them to calculate how much of the food supply in a given country might be affected by local as well as climate shocks in other countries (Figure 5). This showed them that there are many countries that can expect that 30 % or more of their crop supply might be disrupted due to climate shocks in the next ten years (with up to 93 % in the case of Serbia).
Figure 5: Exposure of crop supply for different countries to climate shocks. Black lines indicate single model runs. Violin plot overall indicates the distribution of the exposure throughout all runs.
Generally, they found that the best shield against climate shocks is having as many countries you import food from as possible, which should also be distributed throughout different climate zones. If you only rely on your local crops and those of your neighbours, it is likely you will have a bad time due to climate change at some point.
Another paper I wanted to add here is by Jain (2024). It explores how food trade happens on a sub-country level. It is the most detailed analysis of this kind I have read so far and reveals quite interesting patterns.
To dig even deeper into the concentration, we can look at how trade happens on a sub-country level. This was recently explored in quite some detail by Jain (2024). The main idea behind the paper was that we would probably be able to understand trade better, if we could look at it in more detail, meaning admin levels below the country level. Problem is that this data mostly does not exist. This means we have to create it.
To do this, Jain trained machine learning models on national scale-trade relations of food trade and predictive variables like crop area, livestock count, population and transportation networks. The resulting model allowed Jain to predict the out and ingoing flow of trade for lower admin levels for all countries. These numbers were then adjusted until the sums lined up with the actually traded amounts on a country level. Due to the high uncertainties involved, the resulting numbers are likely still somewhat off, but they reveal quite interesting patterns of trade (Figure 2). It turns out production is not only concentrated on a few countries, but even in those countries most of the production comes from a small number of regions. This means even a relatively local shock can quickly have global consequences.
Figure 2: Sub-national global cereal trade flows.
Simulating the end of the world
One way to understand the past better is to try to model it. This can often lead to surprising conclusions and strengthen or discard past hypotheses. In my original post, I highlighted several models. In this update, I added a paper by Kondor et al. (2024), which uses an agent based model to explore the question if holocene population dynamics can be explained by people fleeing aggressive neighbours.
Landscapes of fear
To give an example of agent based modelling, I want to discuss a model by Kondor et al. (2024). They build this to understand how inter-group conflict has influenced population dynamics in prehistory. We know from archeological data that there have been boom-and-bust patterns in non-state societies in Mid-Holocene Europe, but it is still debated what exactly caused them. One theory is that this is due to conflict. Partly by just killing people, but potentially more strongly by people avoiding areas where aggressive neighbours might roam.
To test this Kondor and co-authors created an agent based model which goes beyond direct casualties to capture how the mere threat of violence reshapes where people live. They call this the "landscape of fear," borrowing from ecology where prey animals avoid areas with predators even without actual predation.
Their models include two key mechanisms. When conflicts break out, people abandon vulnerable settlements and crowd into defensible locations like hilltops. The presence of aggressive groups also prevents normal expansion into new territories, keeping populations concentrated in "safe" zones.
Testing against radiocarbon data from Mid-Holocene Europe, they found that a scenario with zero direct conflict casualties could still reproduce the observed boom-and-bust population patterns. The models show that these indirect effects alone can cause large-scale regional population declines lasting centuries, cyclical patterns matching archaeological evidence, and population concentration leading to resource stress and elevated mortality.
This research reveals how conflict's psychological and social impacts can be far more devastating than direct violence. Fear itself becomes a powerful demographic force, reshaping entire landscapes and potentially explaining major population patterns in human prehistory.
How to write a living literature review
I extended the post by a section that explains how you can more easily find specific papers you are looking for.
Finding specific papers
Sometimes you already have a hunch of what kind of papers you are looking for, like when there is a gap in argument that would profit from a fitting citation. For this case, there are also a bunch of very helpful tools out there:
Elicit: This is a search engine optimized for finding the papers you need and it can also automate information extraction. Just ask whatever interests you and it will try to find papers which answer your questions. It is kind of optimized for medical papers, but in my experience it can also be quite helpful for many other fields.
Connected Papers: The main usage of this tool is to find the web of papers that surrounds a specific paper. You put in the name of a paper that you find interesting and you’ll get a network of papers relevant to it. These papers are selected based on similarity, which is calculated by checking if papers are overlapping in their citations and references.
Research Rabbit: This is a bit of a mix between the other two tools. Allowing you to search in the network of papers you have specified.
I also added a new section that explains how I use Rogue Scholar.
Making everything citable
As this blog is essentially just another form of doing research, I wanted to make sure that my blog is both findable via academic channels and also that the people I cite can become aware of what I am doing here. This seemed quite difficult to accomplish, until I came across Rogue Scholar, which aims to be a home for scientific blogs. It is a platform which automatically creates DOIs for my blog posts, as well as extracting the references I have, so other people can see that I cited them.
To also set this up for your living literature review you have to go through a few steps:
Create an account at Rogue Scholar (optional).
Fill out the blog questionnaire. This gives Rogue Scholar the information it needs to figure out if your work is actually a scientific blog.
Wait a day or so.
Once Rogue Scholar has decided that your work is a scientific blog, they will go through all your existing posts to archive them and provide them with DOIs. The end result will look something like this.
Every time you add a new post on your blog, this will also be added to the archive. The DOI forwards you to the original blogpost, unless this is not available anymore, in which case it falls back to an archived version in the Internet Archive.
When I have updated posts, I have to inform Rogue Scholar manually, as Substack does not automatically provide this information (Wordpress for example does). Rogue Scholar will then update the version on their end as well.
You can also add additional meta data like your ORCID or funding information, if you want to.
Overall, it is a pretty cool solution! And now every post of mine also has a “How to cite” section.
Counterfactual catastrophes
In this post I explored how we can use counterfactual reasoning to better understand collapse and catastrophes. I especially highlighted the idea of storylines. As this term tends to be a bit vague and people understand it differently, I added a review paper that analyzed the main ways storylines are used in academia.
Using storylines can sometimes be a bit confusing, as people tend to understand quite different things under the term. Thankfully, a recent review by Baulenas et al. (2023) tries to clear up this confusion by highlighting the main ways people tend to use the term storylines. They identified three main ways:
Scenario-based storylines: These are qualitative descriptions of how the world might evolve. A typical example would be the IPCC’s Shared Socioeconomic Pathways. They describe the general idea of a potential future, which then can be integrated and modeled with climate models.
Physical climate storylines: This is the kind of storyline which is meant by Shepherd et al. as explained above.
Discourse-analytical storylines: This kind of analysis aims at capturing what is discussed in climate policy to understand the main narratives and metaphors.
As you see, these approaches are quite different. I think for societal collapse research, all of them could be helpful. I would love to read it if someone did the work to capture what the main narratives and metaphors around societal collapse are.
Baulenas et al. also recommend that we could combine these three approaches to create more useful storylines. For example, doing a physical climate storyline and then doing a workshop about it to try to find out what kind of narratives this sparks in the participants.
Democratic Resilience
While many of the things I highlight on this blog are more on the negative side of things, there is also good news to be found. One of those good news is a paper by Nord et al. (2025) which shows that democracies are apparently becoming more resilient against the call of autocracy.
But there is also some evidence that democracies seem to be a strong attractor to get back to, once you have been a stable democracy for a while. This is explored in Nord et al. (2025). They wanted to understand how democracies fare in the present “wave of autocratization”. To do so, they looked into history, as this is not the first time democracies face headwinds. They used a dataset of democracies from 1900 to 2023, which tracked how democratic these countries were over this period.
This provided them with a time series for every country. In this timeseries they looked for breaks when countries suddenly are becoming less democratic or less autocratic. Using this approach they found a process which they simply call “U-Turns”. This means that a given country turned toward autocracy for a while, but then quickly reversed course and became more democratic again.
They identify 102 of these U-Turn episodes. Which is significant, because this represents 52 % of turns towards autocracy. This means roughly half of all democratic backsliding gets quickly resolved again (meaning in less than five years). And it gets even better, because if we only look at the last 30 years 73 % of all turns towards autocracy got quickly reversed. So, even in the present day we apparently have a good chance to save our democracies. Additionally, in recent decades these U-Turns mostly start before a country becomes an autocracy, hinting that democracies got more resilient against the call of autocracy. Though, it remains a bit unclear why exactly.
Share your favorite
As a little thank you for reading my stuff, here’s a song I recently enjoyed (hope you like folk punk).
If you made it this far in the post, I assume that you find my writing interesting. I am still working on growing my audience, so if you want to do me a favor, please send your favorite article to someone who might find it interesting.
Go ahead, you can do it right now. Nothing more to read in this post anyway.
Much appreciated!
Until next time
Thanks for reading! If you want to talk about this post or societal collapse in general, I’d be happy to have a chat. Just send me an email to existential_crunch at posteo.de and we can schedule something.
How to cite
Jehn, F. U. (2025, July 16). July 2025 Updates. Existential Crunch. https://doi.org/10.59350/rnf1r-jh869
References
Baulenas, E., Versteeg, G., Terrado, M., Mindlin, J., & Bojovic, D. (2023). Assembling the climate story: Use of storyline approaches in climate-related science. Global Challenges, 7(7), 2200183. https://doi.org/10.1002/gch2.202200183
Jain, S. (2024). Mapping Global Cereal Flow at Subnational Scales Unveils Key Insights for Food Systems Resilience. Research Square. https://doi.org/10.21203/rs.3.rs-5204730/v1
Keys, P. W., Barnes, E. A., Diffenbaugh, N. S., Hertel, T. W., Baldos, U. L. C., & Hedlund, J. (2025). Exposure to compound climate hazards transmitted via global agricultural trade networks. Environmental Research Letters, 20(4), 044039. https://doi.org/10.1088/1748-9326/adb86a
Kondor, D., Bennett, J. S., Gronenborn, D., & Turchin, P. (2024). Landscape of fear: Indirect effects of conflict can account for large-scale population declines in non-state societies. Journal of The Royal Society Interface, 21(217), 20240210. https://doi.org/10.1098/rsif.2024.0210
Nord, M., Angiolillo, F., Lundstedt, M., Wiebrecht, F., & Lindberg, S. I. (2025). When autocratization is reversed: Episodes of U-Turns since 1900. Democratization, 0(0), 1–24. https://doi.org/10.1080/13510347.2024.2448742