I spent a bit of time reading the first few chapters of Complexity: A Guided Tour. The author (also at the Santa Fe institute) claimed that, basically, everyone has their own definition of what “complexity” is, the definitions aren’t even all that similar, and the field of complexity science struggles because of this.
However, she also noted that it’s nothing to be (too?) ashamed of: other fields have been in similar positions, have come out ok, and that we shouldn’t rush to “pick a definition and move on”.
We have to theorize about theorizers and that makes all the difference.
That doesn’t really seem to me to hit the nail on the head.
I get the idea of how in physics, if billiards balls could think and decide what to do it’d be much tougher to predict what will happen. You’d have to think about what they will think.
On the other hand, if a human does something to another human, that’s exactly the situation we’re in: to predict what the second human will do we need to think about what the second human is thinking. Which can be difficult.
Let’s abstract this out. Instead of billiards balls and humans we have parts. Well, really we have collections of parts. A billiard ball isn’t one part, it consists of many atoms. Many other parts. So the question is of what one collection of parts will do after it is influenced by some other collection of parts.
If the system of parts can think and act, it makes it difficult to predict what it will do, but that’s not the only thing that can make it difficult. It sounds to me like difficulty is the essence here, not necessarily thinking.
For example, in physics suppose you have one fluid that comes into contact with another fluid. It can be difficult to predict whether things like eddies or vortices will form. And this happens despite the fact that there is no “theorizing about theorizers”.
Another example: if is often actually quite easy to predict what a human will do even though that involves theorizing about a theorizer. For example, if Employer stopped paying John Doe his salary, I’d have an easy time predicting that John Doe would quit.
The problem with the difficulty frame is that I don’t really see any reason to believe that you get the same problems & solutions to increasing the difficulty of the problems you try to solve in the following fields:
Economics
Sociology
Biology
Evolution
Neuroscience
AI
Probability theory
Ecology
Physics
Chemistry
Except of course from the sources of
Increasing the difficulty of these in some ways plausibly leads to insights about agency & self-reference
There are a bunch of mathematical problems we don’t have efficient solution methods for yet (and maybe never will), like nonlinear dynamics and chaos.
I’m happy with 1, and 2 sounds like applied math for which the sea isn’t high enough to touch yet. Maybe its still good to understand “what are the types of things we can say about stuff we don’t yet understand”, but I often find myself pretty unexcited about the stuff in complex systems theory which takes that approach. Maybe I just haven’t been exposed enough to the right people advocating that.
I didn’t mean to propose the difficulty frame as the answer to what complexity is really about. Although I’m realizing now that I kinda wrote it in a way that implied that.
I think what I’m going for is that “theorizing about theorizers” seems to be pointing at something more akin to difficulty than truly caring about whether the collection of parts theorizes. But I expect that if you poke at the difficulty frame you’ll come across issues (like you have begun to see).
I spent a bit of time reading the first few chapters of Complexity: A Guided Tour. The author (also at the Santa Fe institute) claimed that, basically, everyone has their own definition of what “complexity” is, the definitions aren’t even all that similar, and the field of complexity science struggles because of this.
However, she also noted that it’s nothing to be (too?) ashamed of: other fields have been in similar positions, have come out ok, and that we shouldn’t rush to “pick a definition and move on”.
That doesn’t really seem to me to hit the nail on the head.
I get the idea of how in physics, if billiards balls could think and decide what to do it’d be much tougher to predict what will happen. You’d have to think about what they will think.
On the other hand, if a human does something to another human, that’s exactly the situation we’re in: to predict what the second human will do we need to think about what the second human is thinking. Which can be difficult.
Let’s abstract this out. Instead of billiards balls and humans we have parts. Well, really we have collections of parts. A billiard ball isn’t one part, it consists of many atoms. Many other parts. So the question is of what one collection of parts will do after it is influenced by some other collection of parts.
If the system of parts can think and act, it makes it difficult to predict what it will do, but that’s not the only thing that can make it difficult. It sounds to me like difficulty is the essence here, not necessarily thinking.
For example, in physics suppose you have one fluid that comes into contact with another fluid. It can be difficult to predict whether things like eddies or vortices will form. And this happens despite the fact that there is no “theorizing about theorizers”.
Another example: if is often actually quite easy to predict what a human will do even though that involves theorizing about a theorizer. For example, if Employer stopped paying John Doe his salary, I’d have an easy time predicting that John Doe would quit.
The problem with the difficulty frame is that I don’t really see any reason to believe that you get the same problems & solutions to increasing the difficulty of the problems you try to solve in the following fields:
Economics
Sociology
Biology
Evolution
Neuroscience
AI
Probability theory
Ecology
Physics
Chemistry
Except of course from the sources of
Increasing the difficulty of these in some ways plausibly leads to insights about agency & self-reference
There are a bunch of mathematical problems we don’t have efficient solution methods for yet (and maybe never will), like nonlinear dynamics and chaos.
I’m happy with 1, and 2 sounds like applied math for which the sea isn’t high enough to touch yet. Maybe its still good to understand “what are the types of things we can say about stuff we don’t yet understand”, but I often find myself pretty unexcited about the stuff in complex systems theory which takes that approach. Maybe I just haven’t been exposed enough to the right people advocating that.
Hm, good points.
I didn’t mean to propose the difficulty frame as the answer to what complexity is really about. Although I’m realizing now that I kinda wrote it in a way that implied that.
I think what I’m going for is that “theorizing about theorizers” seems to be pointing at something more akin to difficulty than truly caring about whether the collection of parts theorizes. But I expect that if you poke at the difficulty frame you’ll come across issues (like you have begun to see).