Thanks, enjoyed that. For p(danger|^T) I think the balance shifts towards a higher probability when you add in second order effects such as impact on food production, mass migrations and war over resources. Likewise p(extinction|danger) the root cause could be due to nonlinear effects, e.g. nuclear war or geo-engineering gone wrong. The longer we live as a species I suspect the probability of causing our own extinction tends to one.
There are likely others that think similarly: they are starting with P(extinction|_____) = 1.0 and perpetually filling in the blank with the largest perceived threat: nuclear, overpopulation, climate... Yet, the Lindy Effect applied to our species as a whole suggests precisely the opposite.
Can the Lindy effect be applied safely to a system which is rapidly increasing in complexity? What we can say for sure is the number of paths towards extinction are increasing and we only need to be unlucky once. If there’s a finite possibility for any of them to happen it just becomes a matter of time.
More complexity doesn’t necessarily mean more fragility. 100 regional insulin factories = a more complex supply system than one central factory, but also far most robust to disruption. Global trade has given each nation’s food and medicine some level of redundancy.
I agree but the pace of change makes it hard to apply the Lindy effect. The fact we survived the last century says less and less about our capacity to survive the next one. Redundancy can be a benefit of complexity but emergent behaviour also becomes more likely and harder to predict. I would argue that the general global trend has been to strip out redundancy in the name of cost efficiency and cascading failures will become more common unless we address this.
I work in manufacturing and I don’t see it that way at all. I’ve converted distillation equipment built for one purpose into another service in days. A car factory can switch to make trucks, soybean crop land can switch to corn, natural gas peaker plants back up our base load plants, when my cable internet goes out I switch to my cellular hotspot…the redundant capacity isn’t a spare factory or spare acre…it’s the next-best thing we can substitute, and we have a *lot* of things.
This all works well at smaller scales. It’s the large scale problems, where we’re vulnerable. Having all the moving parts is one thing but you need the information processing and decision making capacity to make use of it. For big, complex problems we just don’t have good systems at matching scales to set the environment for effective adaptation at smaller scales.
Thanks, enjoyed that. For p(danger|^T) I think the balance shifts towards a higher probability when you add in second order effects such as impact on food production, mass migrations and war over resources. Likewise p(extinction|danger) the root cause could be due to nonlinear effects, e.g. nuclear war or geo-engineering gone wrong. The longer we live as a species I suspect the probability of causing our own extinction tends to one.
There are likely others that think similarly: they are starting with P(extinction|_____) = 1.0 and perpetually filling in the blank with the largest perceived threat: nuclear, overpopulation, climate... Yet, the Lindy Effect applied to our species as a whole suggests precisely the opposite.
Can the Lindy effect be applied safely to a system which is rapidly increasing in complexity? What we can say for sure is the number of paths towards extinction are increasing and we only need to be unlucky once. If there’s a finite possibility for any of them to happen it just becomes a matter of time.
More complexity doesn’t necessarily mean more fragility. 100 regional insulin factories = a more complex supply system than one central factory, but also far most robust to disruption. Global trade has given each nation’s food and medicine some level of redundancy.
I agree but the pace of change makes it hard to apply the Lindy effect. The fact we survived the last century says less and less about our capacity to survive the next one. Redundancy can be a benefit of complexity but emergent behaviour also becomes more likely and harder to predict. I would argue that the general global trend has been to strip out redundancy in the name of cost efficiency and cascading failures will become more common unless we address this.
I work in manufacturing and I don’t see it that way at all. I’ve converted distillation equipment built for one purpose into another service in days. A car factory can switch to make trucks, soybean crop land can switch to corn, natural gas peaker plants back up our base load plants, when my cable internet goes out I switch to my cellular hotspot…the redundant capacity isn’t a spare factory or spare acre…it’s the next-best thing we can substitute, and we have a *lot* of things.
This all works well at smaller scales. It’s the large scale problems, where we’re vulnerable. Having all the moving parts is one thing but you need the information processing and decision making capacity to make use of it. For big, complex problems we just don’t have good systems at matching scales to set the environment for effective adaptation at smaller scales.
Malthusian arguments are frequently rebutted by using anecdotal arguments involving success and failure of various societies: e.g https://reason.com/2005/08/01/under-the-spell-of-malthus-2/. Several more recent tries including: https://environmentalprogress.org/big-news/2020/8/3/bad-science-and-bad-ethics-in-peter-gleicks-review-of-apocalypse-never-at-yale-climate-connections
Absolutely awesome. Thanks and hauʻoli makahiki hou!