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How To Play Billiards: Keep It Simple (And Silly)

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Colin Coningham 24-11-04 18:39 view8 Comment0

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So in evolution, you have natural selection selecting organisms to reproduce themselves, or in AI, we’re using stochastic gradient descent to select a system which optimizes some objective function on some data. They’re saying if you are Pareto optimal in some sense, then you should have a utility function. John Wentworth: Then the second step on top of that is once you do have the right language in which to talk about it, you’d expect to find theorems saying things like if you have this sort of selection pressure, then you should see this sort of modularity pop out. Then go update the theorems. John Wentworth: The thing I chose the poor name of selection theorems for was this more general strategy of trying to figure out properties of systems which pop out of selection like this. So more general properties. This method allows you to pocket more balls. Taking an additional 15 consecutive hazards is more than 75 cannons fired in quick succession. For instance, four points are earned if a player hits the red ball, cannons onto the opponent's ball, and then goes off.


We go and figure these things out by having some intuition for how the thing is supposed to work, and then backing out what the math looks like from that. Your brain has to get the concepts from somewhere and get them all loaded into your head before you can actually figure out what the math is supposed to look like. If you’re Isaac Newton in 1665 or ‘66, or whenever it was, trying to figure out basic physics, you’ve got concepts like force and mass and velocity and position and acceleration, and all these things. John Wentworth: We don’t go figuring these things out just by doing brute force search on theorem space. Many pros use this technique where they look through the ghost ball with the object ball’s actual edge, rather than an imaginary centre in a space (ghost ball centre). Be relaxed and don't invade her personal space too much.


John Wentworth: So I wouldn’t say that agency is the concept to understand so much as it’s the name for that whole cluster. John Wentworth: It’s going to be sort of at cross-purposes to the thing that the selection pressure is actually selecting for. It seems like a pretty consistent pattern that modularity pops out of systems under selection pressure. John Wentworth: That’s the sort of thing where if you have something under selection pressure, you would expect that selection pressure to select for using resources efficiently, for instance. That’s a general property of systems that are under selection pressure. John Wentworth: It seems intuitively like there should be some general principles that apply to systems which pop out of selection pressure. Thinking about how you think about agency, I get the sense that you’re interested in selection theorems. Do you think we have any guesses? I would say - I don’t have a clear, good person. Daniel Filan: I guess an obvious concern is that if we don’t have the right language for modularity, then presumably there’s no theorem in which that language appears.


Or, sorry, you’ll need overly restrictive preconditions in the theorem and then you’ll get not very good post-conditions, right? If you want to know what ideal agency is going to look like, if you want to get bits of information about that, the way you’re going to do that is go look at real agents. Daniel Filan: Or you might think, "Oh, I want to understand microeconomics" or something, how do I get a thing that achieves its goal in the real world by making trades with other intelligent agents or something? John Wentworth: That also means if you go look at real agents and see that they don’t match your current math for ideal agents, that’s a pretty strong hint that something is wrong with that math. If it’s the wrong concept of modularity, there’s going to be systems that are modular but don’t satisfy this definition, or systems which do satisfy this definition but are not actually that modular in some important way.



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