#globalcatastrophicrisk — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #globalcatastrophicrisk, aggregated by home.social.
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New preprint!
We reviewed global catastrophic risks: nuclear war, volcanic eruptions, asteroid impacts, cyberattacks, EMP, geomagnetic storms, and pandemics. Which places on Earth are most resilient?
The short answer: nowhere is safe from everything. Australia and New Zealand come closest, but even they have serious vulnerabilities (trade dependence or volcanic exposure).
The key finding is that resilience factors actively trade off against each other. Geographic isolation helps in pandemics but hurts during infrastructure collapse. A large industrial base helps produce emergency food but increases digital vulnerability.
What could help across scenarios: democratic governance, low inequality, decentralized systems, food self-sufficiency, and preparation. Most of these are policy choices, not geographic fate.
You can find the whole preprint here: https://eartharxiv.org/repository/view/12373/
#GlobalCatastrophicRisk #NuclearWar #Pandemic #GeomagneticStorm #Resilience
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New preprint!
We reviewed global catastrophic risks: nuclear war, volcanic eruptions, asteroid impacts, cyberattacks, EMP, geomagnetic storms, and pandemics. Which places on Earth are most resilient?
The short answer: nowhere is safe from everything. Australia and New Zealand come closest, but even they have serious vulnerabilities (trade dependence or volcanic exposure).
The key finding is that resilience factors actively trade off against each other. Geographic isolation helps in pandemics but hurts during infrastructure collapse. A large industrial base helps produce emergency food but increases digital vulnerability.
What could help across scenarios: democratic governance, low inequality, decentralized systems, food self-sufficiency, and preparation. Most of these are policy choices, not geographic fate.
You can find the whole preprint here: https://eartharxiv.org/repository/view/12373/
#GlobalCatastrophicRisk #NuclearWar #Pandemic #GeomagneticStorm #Resilience
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New preprint!
We reviewed global catastrophic risks: nuclear war, volcanic eruptions, asteroid impacts, cyberattacks, EMP, geomagnetic storms, and pandemics. Which places on Earth are most resilient?
The short answer: nowhere is safe from everything. Australia and New Zealand come closest, but even they have serious vulnerabilities (trade dependence or volcanic exposure).
The key finding is that resilience factors actively trade off against each other. Geographic isolation helps in pandemics but hurts during infrastructure collapse. A large industrial base helps produce emergency food but increases digital vulnerability.
What could help across scenarios: democratic governance, low inequality, decentralized systems, food self-sufficiency, and preparation. Most of these are policy choices, not geographic fate.
You can find the whole preprint here: https://eartharxiv.org/repository/view/12373/
#GlobalCatastrophicRisk #NuclearWar #Pandemic #GeomagneticStorm #Resilience
-
New preprint!
We reviewed global catastrophic risks: nuclear war, volcanic eruptions, asteroid impacts, cyberattacks, EMP, geomagnetic storms, and pandemics. Which places on Earth are most resilient?
The short answer: nowhere is safe from everything. Australia and New Zealand come closest, but even they have serious vulnerabilities (trade dependence or volcanic exposure).
The key finding is that resilience factors actively trade off against each other. Geographic isolation helps in pandemics but hurts during infrastructure collapse. A large industrial base helps produce emergency food but increases digital vulnerability.
What could help across scenarios: democratic governance, low inequality, decentralized systems, food self-sufficiency, and preparation. Most of these are policy choices, not geographic fate.
You can find the whole preprint here: https://eartharxiv.org/repository/view/12373/
#GlobalCatastrophicRisk #NuclearWar #Pandemic #GeomagneticStorm #Resilience
-
New preprint!
We reviewed global catastrophic risks: nuclear war, volcanic eruptions, asteroid impacts, cyberattacks, EMP, geomagnetic storms, and pandemics. Which places on Earth are most resilient?
The short answer: nowhere is safe from everything. Australia and New Zealand come closest, but even they have serious vulnerabilities (trade dependence or volcanic exposure).
The key finding is that resilience factors actively trade off against each other. Geographic isolation helps in pandemics but hurts during infrastructure collapse. A large industrial base helps produce emergency food but increases digital vulnerability.
What could help across scenarios: democratic governance, low inequality, decentralized systems, food self-sufficiency, and preparation. Most of these are policy choices, not geographic fate.
You can find the whole preprint here: https://eartharxiv.org/repository/view/12373/
#GlobalCatastrophicRisk #NuclearWar #Pandemic #GeomagneticStorm #Resilience
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Who actually shapes global risk?
One common answer is to focus on individual apocalyptic actors, like terrorists aiming to end the world. But are these groups really what we should be on the lookout for most? Or should we not also consider institutional actors, like states, large militaries, or companies, who actually have the power and resources to meaningfully tip the global balance?
Find out more in post: https://existentialcrunch.substack.com/p/who-shapes-global-risk
#GlobalCatastrophicRisk #ExistentialRisk #Terrorism #Military
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New paper is out: The State of Global Catastrophic Risk Research: A Bibliometric Review.
I am quite happy with this one. I think the paper gives a nice overview of what people in the global catastrophic risk community think about and what their research focuses on. So, if you are even remotely interested in global catastrophic risk research, this paper is worth checking out. I don't think you will a more exhaustive overview anywhere else.
You can find the whole paper here: https://esd.copernicus.org/articles/16/1053/2025/esd-16-1053-2025.html
If you want to just get a quick overview, I also wrote a summary: https://existentialcrunch.substack.com/p/the-state-of-global-catastrophic
And for a super quick overview, just take a look at the visual abstract attached to this post.
#GlobalCatastrophicRisk #GCR #ExistentialRisk #Review #bibliometrics
And thanks to @OpenAlex for their great database, which made this project possible!
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Happy to share our new #preprint—the first-ever #SystematicReview on global catastrophic risk. 🌍
We explores the growing field of #GlobalCatastrophicRisk and #ExistentialRisk, which focus on global threats like #NuclearWar. This bibliometric analysis shows how the field has expanded and diversified over the past 20 years and has made substantial contributions to understanding and preparing for #humanity's biggest risks.
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@johnwehrle It's on my radar, though that radar is rather crowded ....
As for fringe / cultish things: there is a long history of such schemes existing Largely To Separate Rich Idiots From Their Money, and it turns out that you can also find a through line through much cultish-thinking generally that it's a #MakeMOneyFast scheme.
All the more so if there is #InsanelyComplexReasoning behind the core notions, in which we find again that #SmartPeopleAreMoreEasilyFooled. I've been meaning to mention #Kant and his #CritiqueOfPureReason in this thread before, so let's do it now. If what Kant showed is that #reason is very often inferior to #epmiricism, that is direct #evidence and #experience, then the field of #GlobalCatastrophicRisk, for all the reasons (ahem, go with me here, please) given above is absolute fucking catnip because there can be no definitive evidence.
That's the fundamental problem of forecasting, prediction, and/or prophecy: it's inherently non-empirical. At best you can point to a track record of past successes, though that has some obvious issues:
Sufficiently vague / subjective predictions that judging is a crapshoot. A/K/A the Nostradamus and/or Cold Reading problems. https://en.wikipedia.org/wiki/Cold_reading (I suspect that much the success of current LLM Generative AI models has foundations here.)
The Stock Picker's Scam: Find 1,024 marks, send each a stock pick prediction, half saying it goes up, half down. Whichever proves correct, repeat the mailing to the remaining 512, then 256, then 128, then 64. Finally offer your next set of predictions for some fee to the final 32. Each of those 32 has just seen a record of five perfect predictions. What they don't see are the 992 others who received incorrect predictions. So a full prediction history is required.
Beyond that, as noted above, similarities, mechanisms, mathematical foundations (e.g., thermodynamics), etc., are the best guides we have.
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@johnwehrle It's on my radar, though that radar is rather crowded ....
As for fringe / cultish things: there is a long history of such schemes existing Largely To Separate Rich Idiots From Their Money, and it turns out that you can also find a through line through much cultish-thinking generally that it's a #MakeMOneyFast scheme.
All the more so if there is #InsanelyComplexReasoning behind the core notions, in which we find again that #SmartPeopleAreMoreEasilyFooled. I've been meaning to mention #Kant and his #CritiqueOfPureReason in this thread before, so let's do it now. If what Kant showed is that #reason is very often inferior to #epmiricism, that is direct #evidence and #experience, then the field of #GlobalCatastrophicRisk, for all the reasons (ahem, go with me here, please) given above is absolute fucking catnip because there can be no definitive evidence.
That's the fundamental problem of forecasting, prediction, and/or prophecy: it's inherently non-empirical. At best you can point to a track record of past successes, though that has some obvious issues:
Sufficiently vague / subjective predictions that judging is a crapshoot. A/K/A the Nostradamus and/or Cold Reading problems. https://en.wikipedia.org/wiki/Cold_reading (I suspect that much the success of current LLM Generative AI models has foundations here.)
The Stock Picker's Scam: Find 1,024 marks, send each a stock pick prediction, half saying it goes up, half down. Whichever proves correct, repeat the mailing to the remaining 512, then 256, then 128, then 64. Finally offer your next set of predictions for some fee to the final 32. Each of those 32 has just seen a record of five perfect predictions. What they don't see are the 992 others who received incorrect predictions. So a full prediction history is required.
Beyond that, as noted above, similarities, mechanisms, mathematical foundations (e.g., thermodynamics), etc., are the best guides we have.
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@johnwehrle It's on my radar, though that radar is rather crowded ....
As for fringe / cultish things: there is a long history of such schemes existing Largely To Separate Rich Idiots From Their Money, and it turns out that you can also find a through line through much cultish-thinking generally that it's a #MakeMOneyFast scheme.
All the more so if there is #InsanelyComplexReasoning behind the core notions, in which we find again that #SmartPeopleAreMoreEasilyFooled. I've been meaning to mention #Kant and his #CritiqueOfPureReason in this thread before, so let's do it now. If what Kant showed is that #reason is very often inferior to #epmiricism, that is direct #evidence and #experience, then the field of #GlobalCatastrophicRisk, for all the reasons (ahem, go with me here, please) given above is absolute fucking catnip because there can be no definitive evidence.
That's the fundamental problem of forecasting, prediction, and/or prophecy: it's inherently non-empirical. At best you can point to a track record of past successes, though that has some obvious issues:
Sufficiently vague / subjective predictions that judging is a crapshoot. A/K/A the Nostradamus and/or Cold Reading problems. https://en.wikipedia.org/wiki/Cold_reading (I suspect that much the success of current LLM Generative AI models has foundations here.)
The Stock Picker's Scam: Find 1,024 marks, send each a stock pick prediction, half saying it goes up, half down. Whichever proves correct, repeat the mailing to the remaining 512, then 256, then 128, then 64. Finally offer your next set of predictions for some fee to the final 32. Each of those 32 has just seen a record of five perfect predictions. What they don't see are the 992 others who received incorrect predictions. So a full prediction history is required.
Beyond that, as noted above, similarities, mechanisms, mathematical foundations (e.g., thermodynamics), etc., are the best guides we have.
-
@johnwehrle It's on my radar, though that radar is rather crowded ....
As for fringe / cultish things: there is a long history of such schemes existing Largely To Separate Rich Idiots From Their Money, and it turns out that you can also find a through line through much cultish-thinking generally that it's a #MakeMOneyFast scheme.
All the more so if there is #InsanelyComplexReasoning behind the core notions, in which we find again that #SmartPeopleAreMoreEasilyFooled. I've been meaning to mention #Kant and his #CritiqueOfPureReason in this thread before, so let's do it now. If what Kant showed is that #reason is very often inferior to #epmiricism, that is direct #evidence and #experience, then the field of #GlobalCatastrophicRisk, for all the reasons (ahem, go with me here, please) given above is absolute fucking catnip because there can be no definitive evidence.
That's the fundamental problem of forecasting, prediction, and/or prophecy: it's inherently non-empirical. At best you can point to a track record of past successes, though that has some obvious issues:
Sufficiently vague / subjective predictions that judging is a crapshoot. A/K/A the Nostradamus and/or Cold Reading problems. https://en.wikipedia.org/wiki/Cold_reading (I suspect that much the success of current LLM Generative AI models has foundations here.)
The Stock Picker's Scam: Find 1,024 marks, send each a stock pick prediction, half saying it goes up, half down. Whichever proves correct, repeat the mailing to the remaining 512, then 256, then 128, then 64. Finally offer your next set of predictions for some fee to the final 32. Each of those 32 has just seen a record of five perfect predictions. What they don't see are the 992 others who received incorrect predictions. So a full prediction history is required.
Beyond that, as noted above, similarities, mechanisms, mathematical foundations (e.g., thermodynamics), etc., are the best guides we have.
-
@johnwehrle It's on my radar, though that radar is rather crowded ....
As for fringe / cultish things: there is a long history of such schemes existing Largely To Separate Rich Idiots From Their Money, and it turns out that you can also find a through line through much cultish-thinking generally that it's a #MakeMOneyFast scheme.
All the more so if there is #InsanelyComplexReasoning behind the core notions, in which we find again that #SmartPeopleAreMoreEasilyFooled. I've been meaning to mention #Kant and his #CritiqueOfPureReason in this thread before, so let's do it now. If what Kant showed is that #reason is very often inferior to #epmiricism, that is direct #evidence and #experience, then the field of #GlobalCatastrophicRisk, for all the reasons (ahem, go with me here, please) given above is absolute fucking catnip because there can be no definitive evidence.
That's the fundamental problem of forecasting, prediction, and/or prophecy: it's inherently non-empirical. At best you can point to a track record of past successes, though that has some obvious issues:
Sufficiently vague / subjective predictions that judging is a crapshoot. A/K/A the Nostradamus and/or Cold Reading problems. https://en.wikipedia.org/wiki/Cold_reading (I suspect that much the success of current LLM Generative AI models has foundations here.)
The Stock Picker's Scam: Find 1,024 marks, send each a stock pick prediction, half saying it goes up, half down. Whichever proves correct, repeat the mailing to the remaining 512, then 256, then 128, then 64. Finally offer your next set of predictions for some fee to the final 32. Each of those 32 has just seen a record of five perfect predictions. What they don't see are the 992 others who received incorrect predictions. So a full prediction history is required.
Beyond that, as noted above, similarities, mechanisms, mathematical foundations (e.g., thermodynamics), etc., are the best guides we have.