Re:Redefining the Scale of Social Problems [pdf]
by yboris on Friday June 12, 2020 @02:54PM
(#23502432)
A strategy of "small wins" has been what Henry Spira has done during his tremendous work with animal rights (documented in "Ethics Into Action", a book by Peter Singer).
As examples: his year-long campaign was the first in history to end an experiment on animals: specifically an experiment in which the Smithsonian was (via taxpayer dollars) mutilating cats to see the effects on their sex lives.
Compounding on his concrete wins, he eventually got makeup manufacturers to design tests (that ended up being better than before!) that did not require the use of animals.
Re:Redefining the Scale of Social Problems [pdf]
by yboris on Friday June 12, 2020 @02:49PM
(#23502382)
I can't find the article again that uses an analogy for "revolutionary" change vs "incremental" change: that of slowly climbing a mountain in a fog (roughly knowing which direction the peak is and going there) vs launching up into the air to randomly land somewhere. The takeaway is that if we've made it far up the mountain of progress, a revolution is more-likely to land us somewhere far below the progress we've already made.
Anyone remember anything like this? I'd love to find the blog post again!
Re:Redefining the Scale of Social Problems [pdf]
by tehjoker on Friday June 12, 2020 @04:23PM
(#23503483)
This metaphor only makes sense if there are no inherit limits to the system you are working in and you're climbing the right mountain.
For software engineers, think about single core vs multi-threading or scalar processing vs SIMD. You are limited in the performance you can get in a particular regime without making radical changes.
Re:Redefining the Scale of Social Problems [pdf]
by luckylion on Friday June 12, 2020 @06:09PM
(#23504410)
> You are limited in the performance you can get in a particular regime without making radical changes.
But you don't know where you are relative to those limits. You may be in 1999 on a 400Mhz Celeron and you'll still see plenty of increase in single-core performance.
Re:Redefining the Scale of Social Problems [pdf]
by perl4ever on Saturday June 13, 2020 @03:35AM
(#23507609)
This reminds me of something I was reading earlier (may be a HN thread) about how a possibly optimal procedure for searching an area when you have no information is called a "Lévy flight", which "is a random walk in which the step-lengths have a probability distribution that is heavy-tailed". It's distinct from Brownian motion which keeps you closer to the same area. If you wander too far around your mountain in small steps, you probably get away from the peak, but if you want to find another mountain peak, you have to take some much longer steps.
Re:Redefining the Scale of Social Problems [pdf]
by zackees on Friday June 12, 2020 @08:50PM
(#23505598)
"Lest we forget at least an over-the-shoulder acknowledgment to the very first radical: from all our legends, mythology, and history (and who is to know where mythology leaves off and history begins — or which is which), the first radical known to man who rebelled against the establishment and did it so effectively that he at least won his own kingdom — Lucifer."
As examples: his year-long campaign was the first in history to end an experiment on animals: specifically an experiment in which the Smithsonian was (via taxpayer dollars) mutilating cats to see the effects on their sex lives.
Compounding on his concrete wins, he eventually got makeup manufacturers to design tests (that ended up being better than before!) that did not require the use of animals.
Anyone remember anything like this? I'd love to find the blog post again!
For software engineers, think about single core vs multi-threading or scalar processing vs SIMD. You are limited in the performance you can get in a particular regime without making radical changes.
But you don't know where you are relative to those limits. You may be in 1999 on a 400Mhz Celeron and you'll still see plenty of increase in single-core performance.
Leopold Kohr famously wrote "Whenever something is wrong, something is too big."
— SAUL ALINSKY (Rules for Radicals)