November 2023 Updates
Also, a call for feedback
Existential Crunch is a living literature review about societal collapse. When I read new things, which update my views, I’ll also update my posts (1). This post highlights updates I made to three posts. However, before we dive into the changes, I have a little announcement: It’s been six months already since I started this project for real. Since then I have published five posts for the living literature review and two posts about other topics. My reader numbers have grown and by now almost none of the names who sign up sound familiar. Therefore, I thought this would be a good place to ask for some feedback. Has Existential Crunch been helpful for you in any way? Has it influenced how you think about the topics I wrote about? What could I do better?
Feel free to write your answers under this post or you can also write me an email to florian.u.jehn at posteo.de
And now the updates.
Tipping points
My post “The trouble with tipping points” wrestles with the question of how dangerous and urgent climate tipping points really are. I’ve now updated the post to include discussion of three additional papers:
“Tipping Point Detection and Early-Warnings in climate, ecological, and human systems” is a recent preprint that reviews the literature about how we could detect tipping points ahead of time. I added a new section to discuss tipping point detection based on this.
How could we detect tipping points?
While the occurrence of tipping points is abrupt, there are still tools we can use to detect them before they happen. One paper which explores this is literature review by Dakos et al. (2023). The main way to detect tipping points are so-called Early-Warning Signals. They work by identifying changes in a system's behavior as it approaches a tipping point. This change is often marked by a phenomenon known as critical slowing down, where the system becomes less resilient to disturbances. This means once a disturbance happens it takes longer and longer to get back to its original state the closer it is to the tipping point. However, Early-Warning Signals do not predict all types of tipping points and can also appear in smoother transitions. Despite this, they have been successful in detecting tipping points in nearly 70% of cases which Dakos and co-authors looked at.
“Loss of Earth system resilience during early Eocene transient global warming events” looks at isotope data from the deep past to identify the role of tipping points in three major warming events. Their findings indicate a large influx of carbon in the atmosphere, which was likely caused by overstepping tipping points. I added a new paragraph in the last section to explain this:
We also have further evidence for past tipping point behavior by looking into our paleoclimatic record. A recent paper by Setty et al. (2023) identified that three past temperature peaks in the Earth’s system are linked to significant changes in the carbon cycle. This indicates that a large amount of carbon dioxide was added to the atmosphere in a short period of time. Likely this was caused by a self-reinforcing loop of warmer temperatures and higher methane and carbon dioxide release from peatlands, submarine hydrates and permafrost.
“Mechanisms and Impacts of Earth System Tipping Elements” is an extremely thorough recent literature review about tipping points. It mainly emphasized the points I was already making, so I only made some slight changes to bolster up my arguments.
Overview of collapse research
“Mapping out collapse research” explores how the field of societal collapse has developed over time and what schools of thought exist. After reading a literature review by Daniel Hoyer (“Decline and Fall, Growth and Spread, or Resilience? Approaches to Studying How and Why Societies Change”) I added the following to the description of the structural demographic theory school:
The proponents of this school counter that only a large sample history can give you reliable results. In a recent paper, Daniel Hoyer describes the failures of past schools of collapse and links them all to being too focussed on small, biased samples and case studies (Hoyer, 2022). Hoyer thinks that only a large, representative sample gives you a chance of getting general insights.
Resilience and participation
In the post “Participation, inclusion, democracy, and resilience” I try to understand how hierarchy and inclusion shape the way societies react to catastrophes. To do so I looked at several papers that use empirical data to test the reactions of societies of different structures to catastrophic events of differing magnitudes. The paper “Social Resilience to Climate-Related Disasters in Ancient Societies: A Test of Two Hypotheses” tries to empirically test the question if participation and inclusion or strict rule adherence in a society are a better predictor for resilience against climate related disasters. I’ve now added the following discussion to my post:.
Peregrine also has another study that asks the question “what traits make societies resilient” even more explicitly (Peregrine, 2018). In this paper he studied how 33 separate societies reacted to 22 catastrophic climate-related disasters. He uses this dataset to test two conflicting hypotheses, which have been formulated in earlier disasters research:
Societies with more inclusive and participatory structures are more resilient to disasters.
Societies which more strictly adhere to rules are more resilient to disasters.
To do so, he applied a similar approach to the one I just explained. Peregrine defined two indexes:
Looseness-tightness index: This is meant to capture how much a society adheres to rules and is the average of a variety of proxy variables like how much houses or pottery are standardized or if they have very prominent community rituals like rites of passage.
Corporate exclusionary index: This is the same hierarchy index as described above.
He then correlates these two indexes of how much the catastrophic climate-related disasters impacted the affected societies when it comes to things like population levels, health and nutrition and conflict. The results are quite clear. The looseness-tightness index is not correlated with the societal outcomes, while the corporate exclusionary index is positively correlated with societal outcomes (e.g. less hierarchy leads to a more stable population after catastrophe). This means we can accept hypothesis 1 and reject hypothesis 2. Strict adherence to rules does not seem to increase disaster resilience, while more inclusion and participation does.
Until next time
The next post will be about computational models of societal collapse and what we can learn from hydrology to improve those.
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 a mail to existential_crunch at posteo.de and we can schedule something.
Endnotes
Earlier versions remain accessible, as the version control is done via Github.