I’m writing a book about epistemology. It’s about The Problem of the Criterion, why it’s important, and what it has to tell us about how we approach knowing the truth.
I’ve also written a lot about AI safety. Some of the more interesting stuff can be found at the site of my currently-dormant AI safety org, PAISRI.
This reminds me that not everyone knows what I know.
If you work with distributed systems, by which I mean any system that must pass information between multiple, tightly integrated subsystems, there is a well understood concept of maximum sustainable load and we know that number to be roughly 60% of maximum possible load for all systems.
I don’t have a link handy to show you the math, but the basic idea is that the probability that one subsystem will have to wait on another increases exponentially with the total load on the system and the load level that maximizes throughput (total amount of work done by the system over some period of time) comes in just above 60%. If you do less work you are wasting capacity (in terms of throughput); if you do more work you will gum up the works and waste time waiting even if all the subsystems are always busy.
We normally deal with this in engineering contexts, but as is so often the case this property will hold for basically anything that looks sufficiently like a distributed system. Thus the “operate at 60% capacity” rule of thumb will maximize throughput in lots of scenarios: assembly lines, service-oriented architecture software, coordinated work within any organization, an individual’s work (since it is normally made up of many tasks that information must be passed between with the topology being spread out over time rather than space), and perhaps most surprisingly an individual’s mind-body.
“Slack” is a decent way of putting this, but we can be pretty precise and say you need ~40% slack to optimize throughput: more and you tip into being “lazy”, less and you become “overworked”.