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> Measuring causal emergence is like you're looking at the causal structure of a system with a camera (the theory) and as you focus the camera (look at differen

> Measuring causal emergence is like you're looking at the causal structure of a system with a camera (the theory) and as you focus the camera (look at different scales) the causal structure snaps into focus. Notably, it doesn’t have to be “in focus" at the lowest possible scale, the microscale. Talk about abstract metaphors that have no meaning. The core of this argument seems to be: 1) Given a fixed state of a system, you can modify it by apply certain operators on the system. 2) You can model the 'causal structure' by observing changes as you randomly apply operators. 3) High level systems at a macro scale have a greater information density than the sum of their parts. Ie. In a nutshell, you can have high level (ie. real world) systems that display behaviour that is not just hard to predict from changes to low level systems... but actually impossible to predict from them. Which is to say, basically asserting that you cannot predict the behaviour of macro systems from microscale systems; eg. you cannot predict the behaviour of a molecule based on its quantum state / make up (clearly false) and you cannot predict the behaviour of say, a person deciding what to have to for lunch based on their quantum state/ ...but not that you can't because it's hard. You can't because its not possible. Am I misunderstanding? I think that sounds completely crack pot to me.

chadcmulligan 9y ago
Not sure I follow it all either but one thing I think I gleaned is given a series of n measurements, the measurements at the macro scale provide more information than the same number of measurements at the micro scale. Thinking about the switch analogy they use this seams feasible. Though I think that may only be a part of what's being said.
olejorgenb 9y ago
Yeah, I've read through the critique (http://www.scottaaronson.com/blog/?p=3294 http://www.scottaaronson.com/blog/?p=3294) that triggered to linked blog post and the main disagreement seems to be about this. From the critique: "In their new work, Hoel and others claim to make the amazing discovery that scientific reductionism is false—or, more precisely, that there can exist “causal information” in macroscopic systems, information relevant for predicting the systems’ future behavior, that’s not reducible to causal information about the systems’ microscopic building blocks." I think it would've helped cleared thing up if Hoel actually addressed and clarified the above quote. Instead (From the blog post): "Why does causal emergence matter? The theory does imply that universal reductionism is false when it comes to thinking about causation, and that sometimes higher scales really do have more causal influence (or information) than whatever underlies them. This is common sense in our day-to-day lives, but in the intellectual world it’s very controversial." At this point I'd really like the definition of "universal reductionism".. An illuminating example from the critique (possibly taken from Hoels paper). (Basically an example of your 1-3 steps) "For here is the argument from the Entropy paper, for the existence of macroscopic causality that’s not reducible to causality in the underlying components. Suppose I have a system with 8 possible states (called “microstates”), which I label 1 through 8. And suppose the system evolves as follows: if it starts out in states 1 through 7, then it goes to state 1. If, on the other hand, it starts in state 8, then it stays in state 8. In such a case, it seems reasonable to “coarse-grain” the system, by lumping together initial states 1 through 7 into a single “macrostate,” call it A, and letting the initial state 8 comprise a second macrostate, call it B. We now ask: how much information does knowing the system’s initial state tell you about its final state? If we’re talking about microstates, and we let the system start out in a uniform distribution over microstates 1 through 8, then 7/8 of the time the system goes to state 1. So there’s just not much information about the final state to be predicted—specifically, only 7/8×log2(8/7) + 1/8×log2(8) ≈ 0.54 bits of entropy—which, in this case, is also the mutual information between the initial and final microstates. If, on the other hand, we’re talking about macrostates, and we let the system start in a uniform distribution over macrostates A and B, then A goes to A and B goes to B. So knowing the initial macrostate gives us 1 full bit of information about the final state, which is more than the ~0.54 bits that looking at the microstate gave us! Ergo reductionism is false." From this I think we all can agree that finding out how a system with A and B as states is much simpler than figuring out the low level 9 state system. But how that somehow disproved "reductionism" is not clear. I'm not sure that this is type of compression is "controversial in the intellectual world" either? That doesn't mean the study of "Causal emergence" isn't worth studying of course.
- mannykannot 9y ago
  Thanks for this comment, and especially the example, which appears to get to the crux of the matter (though I am not in a position to be sure.) In that example, does not the different result come, in an unsurprising way, from the fact that a uniform distribution over the 8 microstates is not the same as a uniform distribution over the two macrostates? In a separate note[1], Hoel seems to be claiming that the issue is the definition of 'black-boxing', and if I am following the small example there, his definition allegedly allows him to use a uniform distribution over four states, or a uniform distribution over three states, depending on whether the S1 microstate is declared as being 'black-boxed'. Furthermore, this seems to differ from your example in that here, S1 does not become a macrostate, it is completely ignored once it is declared to be black-boxed. I do not see how Hoel is comparing apples to apples. [1] http://www.erikphoel.com/uploads/1/7/8/8/17883727/black-boxing-mistake.pdf http://www.erikphoel.com/uploads/1/7/8/8/17883727/black-boxi...
deleted 9y ago
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SCHiM 9y ago
I'm not a physicist or computer scientist. But I do think I recognize what the author is trying to explain when looking at code sometimes, or even differences in core mathematical operators. When looking at code and 'reading' it, a programmer is (possibly without realizing it) running the snippet of code inside their heads. It's impossible, I think, to understand a loop without running a little part of it in your own head. In a very real way, you're 'executing' the code in your head. A little part of your program is running right at that moment when you read what you or someone else wrote. In philosophy this is called the idea of multiple realizability, or functional isomorphism. The point of this is, that the outcome of the loop/code/w\e cannot be determined by looking _just_ at each and every operation or even keyword or character. The code must be _ran_ in your head to know what it does. As that point you're not looking at the 'parts' but at the 'whole'. Simply reading and comprehending the different statements but not simulating the code in your head is what a person does that cannot program in that language, they understand the words 'do', 'for', 'while', but cannot connect and parse and simulate their meaning. The result is obviously that that person wouldn't be able to understand what a loop or program was doing. They _cannot_ understand so long they don't simulate (= zoom out). Perhaps this is akin what the author and the critics are confused about. It seems that it's perfectly reasonable to start your simulation at the parts, and then scale the simulation up and predict or determine things about the whole. This works as long as you realize that along the way you've started abstracting away the details, compartmentalizing the particles and states, and that you're _actually_ looking at larger parts of the whole, instead of predicting things about the whole by looking at it's parts.
TheOtherHobbes 9y ago
It's not crack pot at all. Given an understanding of quantum chemistry and the periodic table, would a reductionist approach be able to predict Hacker News? You certainly can predict the behaviour - or at least a statistical envelope for the behaviour - of a single molecule. But once you get beyond a certain scale, information starts being processed and abstracted in the relationships that emerge between larger assemblies. Naive reductionism has no tools for modelling that information or predicting how it might appear or develop.
- deleted 9y ago
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- shadowmint 9y ago
  > abstracted in the relationships that emerge between larger assemblies. Yes, but that's the point. Obviously it's hard to make predictions a macro level purely from studying it at a super micro level and without looking at parts of the interactions it might even be impossible; but that's not what's being asserted here. What's being asserted here, is, in scott aaronson's words: > In their new work, Hoel and others claim to make the amazing discovery that scientific reductionism is false—or, more precisely, that there can exist “causal information” in macroscopic systems, information relevant for predicting the systems’ future behavior, that’s not reducible to causal information about the systems’ microscopic building blocks. Think about that for a second. You're saying, there's a kind a of 'meta information' in complex systems, that cannot be reduced into information about its constituent parts or how they interact. For example, 'what you might pick for lunch' cannot be represented as a information about your blood, body, atoms, stomach. It's a stupid assertion; if you assert that a system of any complexity cannot be predicted by the behavior of its constituent parts, you're basically saying that 'nothing makes sense'. It's patently false. It's just a flip off to people building probabilistic models like, oh hey, don't bother, that doesn't work. It's just not true. What's mathematically interesting is how they've devised the paper. If you can show, mathematically, that you have more information for predicting future state from considering a macro state than all micro states, that's a pretty interesting result; but it's also exactly the point scott demolishes in his blog post. ...and the rebuttal? > Doing a series of A/B tests to capture the effects of the macroscale states doesn’t correspond to doing a series of A/B tests to capture the effects of microscale states. A randomized trial at the macroscale of medical treatments to see their effect on tumors won’t correspond to an underlying set of microscale randomized trials, because many different microstates make up the macrostates. Which is where we started; ie. the assertion that the behaviour of microscale effects doesn't reflect macro scale effects. ...but we know that it does. We don't invent new drugs by going off and randomly trying crap; we model the molecules and predict the macro scale effects they'll have. What he's asserting here is quite literally, demonstrably false.

chadcmulligan 9y ago

Not sure I follow it all either but one thing I think I gleaned is given a series of n measurements, the measurements at the macro scale provide more information than the same number of measurements at the micro scale. Thinking about the switch analogy they use this seams feasible. Though I think that may only be a part of what's being said.

olejorgenb 9y ago

Yeah, I've read through the critique (http://www.scottaaronson.com/blog/?p=3294 http://www.scottaaronson.com/blog/?p=3294) that triggered to linked blog post and the main disagreement seems to be about this. From the critique: "In their new work, Hoel and others claim to make the amazing discovery that scientific reductionism is false—or, more precisely, that there can exist “causal information” in macroscopic systems, information relevant for predicting the systems’ future behavior, that’s not reducible to causal information about the systems’ microscopic building blocks." I think it would've helped cleared thing up if Hoel actually addressed and clarified the above quote. Instead (From the blog post): "Why does causal emergence matter? The theory does imply that universal reductionism is false when it comes to thinking about causation, and that sometimes higher scales really do have more causal influence (or information) than whatever underlies them. This is common sense in our day-to-day lives, but in the intellectual world it’s very controversial." At this point I'd really like the definition of "universal reductionism".. An illuminating example from the critique (possibly taken from Hoels paper). (Basically an example of your 1-3 steps) "For here is the argument from the Entropy paper, for the existence of macroscopic causality that’s not reducible to causality in the underlying components. Suppose I have a system with 8 possible states (called “microstates”), which I label 1 through 8. And suppose the system evolves as follows: if it starts out in states 1 through 7, then it goes to state 1. If, on the other hand, it starts in state 8, then it stays in state 8. In such a case, it seems reasonable to “coarse-grain” the system, by lumping together initial states 1 through 7 into a single “macrostate,” call it A, and letting the initial state 8 comprise a second macrostate, call it B. We now ask: how much information does knowing the system’s initial state tell you about its final state? If we’re talking about microstates, and we let the system start out in a uniform distribution over microstates 1 through 8, then 7/8 of the time the system goes to state 1. So there’s just not much information about the final state to be predicted—specifically, only 7/8×log2(8/7) + 1/8×log2(8) ≈ 0.54 bits of entropy—which, in this case, is also the mutual information between the initial and final microstates. If, on the other hand, we’re talking about macrostates, and we let the system start in a uniform distribution over macrostates A and B, then A goes to A and B goes to B. So knowing the initial macrostate gives us 1 full bit of information about the final state, which is more than the ~0.54 bits that looking at the microstate gave us! Ergo reductionism is false." From this I think we all can agree that finding out how a system with A and B as states is much simpler than figuring out the low level 9 state system. But how that somehow disproved "reductionism" is not clear. I'm not sure that this is type of compression is "controversial in the intellectual world" either? That doesn't mean the study of "Causal emergence" isn't worth studying of course.

mannykannot 9y ago
Thanks for this comment, and especially the example, which appears to get to the crux of the matter (though I am not in a position to be sure.) In that example, does not the different result come, in an unsurprising way, from the fact that a uniform distribution over the 8 microstates is not the same as a uniform distribution over the two macrostates? In a separate note[1], Hoel seems to be claiming that the issue is the definition of 'black-boxing', and if I am following the small example there, his definition allegedly allows him to use a uniform distribution over four states, or a uniform distribution over three states, depending on whether the S1 microstate is declared as being 'black-boxed'. Furthermore, this seems to differ from your example in that here, S1 does not become a macrostate, it is completely ignored once it is declared to be black-boxed. I do not see how Hoel is comparing apples to apples. [1] http://www.erikphoel.com/uploads/1/7/8/8/17883727/black-boxing-mistake.pdf http://www.erikphoel.com/uploads/1/7/8/8/17883727/black-boxi...

deleted 9y ago

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SCHiM 9y ago

I'm not a physicist or computer scientist. But I do think I recognize what the author is trying to explain when looking at code sometimes, or even differences in core mathematical operators. When looking at code and 'reading' it, a programmer is (possibly without realizing it) running the snippet of code inside their heads. It's impossible, I think, to understand a loop without running a little part of it in your own head. In a very real way, you're 'executing' the code in your head. A little part of your program is running right at that moment when you read what you or someone else wrote. In philosophy this is called the idea of multiple realizability, or functional isomorphism. The point of this is, that the outcome of the loop/code/w\e cannot be determined by looking _just_ at each and every operation or even keyword or character. The code must be _ran_ in your head to know what it does. As that point you're not looking at the 'parts' but at the 'whole'. Simply reading and comprehending the different statements but not simulating the code in your head is what a person does that cannot program in that language, they understand the words 'do', 'for', 'while', but cannot connect and parse and simulate their meaning. The result is obviously that that person wouldn't be able to understand what a loop or program was doing. They _cannot_ understand so long they don't simulate (= zoom out). Perhaps this is akin what the author and the critics are confused about. It seems that it's perfectly reasonable to start your simulation at the parts, and then scale the simulation up and predict or determine things about the whole. This works as long as you realize that along the way you've started abstracting away the details, compartmentalizing the particles and states, and that you're _actually_ looking at larger parts of the whole, instead of predicting things about the whole by looking at it's parts.

TheOtherHobbes 9y ago

It's not crack pot at all. Given an understanding of quantum chemistry and the periodic table, would a reductionist approach be able to predict Hacker News? You certainly can predict the behaviour - or at least a statistical envelope for the behaviour - of a single molecule. But once you get beyond a certain scale, information starts being processed and abstracted in the relationships that emerge between larger assemblies. Naive reductionism has no tools for modelling that information or predicting how it might appear or develop.

deleted 9y ago
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shadowmint 9y ago
> abstracted in the relationships that emerge between larger assemblies. Yes, but that's the point. Obviously it's hard to make predictions a macro level purely from studying it at a super micro level and without looking at parts of the interactions it might even be impossible; but that's not what's being asserted here. What's being asserted here, is, in scott aaronson's words: > In their new work, Hoel and others claim to make the amazing discovery that scientific reductionism is false—or, more precisely, that there can exist “causal information” in macroscopic systems, information relevant for predicting the systems’ future behavior, that’s not reducible to causal information about the systems’ microscopic building blocks. Think about that for a second. You're saying, there's a kind a of 'meta information' in complex systems, that cannot be reduced into information about its constituent parts or how they interact. For example, 'what you might pick for lunch' cannot be represented as a information about your blood, body, atoms, stomach. It's a stupid assertion; if you assert that a system of any complexity cannot be predicted by the behavior of its constituent parts, you're basically saying that 'nothing makes sense'. It's patently false. It's just a flip off to people building probabilistic models like, oh hey, don't bother, that doesn't work. It's just not true. What's mathematically interesting is how they've devised the paper. If you can show, mathematically, that you have more information for predicting future state from considering a macro state than all micro states, that's a pretty interesting result; but it's also exactly the point scott demolishes in his blog post. ...and the rebuttal? > Doing a series of A/B tests to capture the effects of the macroscale states doesn’t correspond to doing a series of A/B tests to capture the effects of microscale states. A randomized trial at the macroscale of medical treatments to see their effect on tumors won’t correspond to an underlying set of microscale randomized trials, because many different microstates make up the macrostates. Which is where we started; ie. the assertion that the behaviour of microscale effects doesn't reflect macro scale effects. ...but we know that it does. We don't invent new drugs by going off and randomly trying crap; we model the molecules and predict the macro scale effects they'll have. What he's asserting here is quite literally, demonstrably false.