So, yeah, one thing that’s going on here is that I have recently been explicitly going in the other direction with partial agency, so obviously I somewhat agree. (Both with the object-level anti-realism about the limit of perfect rationality, and with the meta-level claim that agent foundations research may have a mistaken emphasis on this limit.)
But I also strongly disagree in another way. For example, you lump logical induction into the camp of considering the limit of perfect rationality. And I can definitely see the reason. But from my perspective, the significant contribution of logical induction is absolutely about making rationality more bounded.
The whole idea of the logical uncertainty problem is to consider agents with limited computational resources.
Logical induction in particular involves a shift in perspective, where rationality is not an ideal you approach but rather directly about how you improve. Logical induction is about asymptotically approximating coherence in a particular way as opposed to other ways.
So to a large extent I think my recent direction can be seen as continuing a theme already present—perhaps you might say I’m trying to properly learn the lesson of logical induction.
But is this theme isolated to logical induction, in contrast to earlier MIRI research? I think not fully: Embedded Agency ties everything together to a very large degree, and embeddedness is about this kind of boundedness to a large degree.
So I think Agent Foundations is basically not about trying to take the limit of perfect rationality. Rather, we inherited this idea of perfect rationality from Bayesian decision theory, and Agent Foundations is about trying to break it down, approaching it with skepticism and trying to fit it more into the physical world.
Reflective Oracles still involve infinite computing power, and logical induction still involves massive computing power, more or less because the approach is to start with idealized rationality and try to drag it down to Earth rather than the other way around. (That model feels a bit fake but somewhat useful.)
(Generally I am disappointed by my reply here. I feel I have not adequately engaged with you, particularly on the function-vs-nature distinction. I may try again later.)
I’ll try respond properly later this week, but I like the point that embedded agency is about boundedness. Nevertheless, I think we probably disagree about how promising it is “to start with idealized rationality and try to drag it down to Earth rather than the other way around”. If the starting point is incoherent, then this approach doesn’t seem like it’ll go far—if AIXI isn’t useful to study, then probably AIXItl isn’t either (although take this particular example with a grain of salt, since I know almost nothing about AIXItl).
I appreciate that this isn’t an argument that I’ve made in a thorough or compelling way yet—I’m working on a post which does so.
If the starting point is incoherent, then this approach doesn’t seem like it’ll go far—if AIXI isn’t useful to study, then probably AIXItl isn’t either (although take this particular example with a grain of salt, since I know almost nothing about AIXItl).
Hm. I already think the starting point of Bayesian decision theory (which is even “further up” than AIXI in how I am thinking about it) is fairly useful.
In a naive sort of way, people can handle uncertain gambles by choosing a quantity to treat as ‘utility’ (such as money), quantifying probabilities of outcomes, and taking expected values. This doesn’t always serve very well (e.g. one might prefer Kelley betting), but it was kind of the starting point (probability theory getting its starting point from gambling games) and the idea seems like a useful decision-making mechanism in a lot of situations.
Perhaps more convincingly, probability theory seems extremely useful, both as a precise tool for statisticians and as a somewhat looser analogy for thinking about everyday life, cognitive biases, etc.
AIXI adds to all this the idea of quantifying Occam’s razor with algorithmic information theory, which seems to be a very fruitful idea. But I guess this is the sort of thing we’re going to disagree on.
As for AIXItl, I think it’s sort of taking the wrong approach to “dragging things down to earth”. Logical induction simultaneously makes things computable and solves a new set of interesting problems having to do with accomplishing that. AIXItl feels more like trying to stuff an uncomputable peg into a computable hole.
So, yeah, one thing that’s going on here is that I have recently been explicitly going in the other direction with partial agency, so obviously I somewhat agree. (Both with the object-level anti-realism about the limit of perfect rationality, and with the meta-level claim that agent foundations research may have a mistaken emphasis on this limit.)
But I also strongly disagree in another way. For example, you lump logical induction into the camp of considering the limit of perfect rationality. And I can definitely see the reason. But from my perspective, the significant contribution of logical induction is absolutely about making rationality more bounded.
The whole idea of the logical uncertainty problem is to consider agents with limited computational resources.
Logical induction in particular involves a shift in perspective, where rationality is not an ideal you approach but rather directly about how you improve. Logical induction is about asymptotically approximating coherence in a particular way as opposed to other ways.
So to a large extent I think my recent direction can be seen as continuing a theme already present—perhaps you might say I’m trying to properly learn the lesson of logical induction.
But is this theme isolated to logical induction, in contrast to earlier MIRI research? I think not fully: Embedded Agency ties everything together to a very large degree, and embeddedness is about this kind of boundedness to a large degree.
So I think Agent Foundations is basically not about trying to take the limit of perfect rationality. Rather, we inherited this idea of perfect rationality from Bayesian decision theory, and Agent Foundations is about trying to break it down, approaching it with skepticism and trying to fit it more into the physical world.
Reflective Oracles still involve infinite computing power, and logical induction still involves massive computing power, more or less because the approach is to start with idealized rationality and try to drag it down to Earth rather than the other way around. (That model feels a bit fake but somewhat useful.)
(Generally I am disappointed by my reply here. I feel I have not adequately engaged with you, particularly on the function-vs-nature distinction. I may try again later.)
I’ll try respond properly later this week, but I like the point that embedded agency is about boundedness. Nevertheless, I think we probably disagree about how promising it is “to start with idealized rationality and try to drag it down to Earth rather than the other way around”. If the starting point is incoherent, then this approach doesn’t seem like it’ll go far—if AIXI isn’t useful to study, then probably AIXItl isn’t either (although take this particular example with a grain of salt, since I know almost nothing about AIXItl).
I appreciate that this isn’t an argument that I’ve made in a thorough or compelling way yet—I’m working on a post which does so.
Hm. I already think the starting point of Bayesian decision theory (which is even “further up” than AIXI in how I am thinking about it) is fairly useful.
In a naive sort of way, people can handle uncertain gambles by choosing a quantity to treat as ‘utility’ (such as money), quantifying probabilities of outcomes, and taking expected values. This doesn’t always serve very well (e.g. one might prefer Kelley betting), but it was kind of the starting point (probability theory getting its starting point from gambling games) and the idea seems like a useful decision-making mechanism in a lot of situations.
Perhaps more convincingly, probability theory seems extremely useful, both as a precise tool for statisticians and as a somewhat looser analogy for thinking about everyday life, cognitive biases, etc.
AIXI adds to all this the idea of quantifying Occam’s razor with algorithmic information theory, which seems to be a very fruitful idea. But I guess this is the sort of thing we’re going to disagree on.
As for AIXItl, I think it’s sort of taking the wrong approach to “dragging things down to earth”. Logical induction simultaneously makes things computable and solves a new set of interesting problems having to do with accomplishing that. AIXItl feels more like trying to stuff an uncomputable peg into a computable hole.