Yes, thank you for the example about how multiple variables would need to be changed for this system to work, and how ideas get dismissed on the basis that they can only be implemented in the current system without looking at the other variables.
We should consider separately whether a situation A could be improved by changing into a situation B (by changing multiple variables), and how to realistically get from A to B.
Because, I can talk about all the variables that should be changed… but the only variable I can change is my own behavior. (Technically, my own behavior is also a set of variables, but that is important for different kind of decisions.) So we sometimes get into a multi-player Prisonners’ Dilemma. (For example, I can wait until women start showing their interest in me first, but… some other guy may just move faster; and the punchline is that at the end most of those women will appreciate that he did.)
There is also a technical risk in changing multiple variables. What if we succeed in changing some of them, but fail in changing others of them, and end up with a situation C which may be even worse than A? And that’s actually pretty likely, because you often can’t change many variables at the same time, and when you change only half of them, you may get a worse situation, which may make people say: “OK, this is just getting worse, let’s stop.”
Also, the more variables we change, the harder it is to predict the consequences. There is a big chance that something unexpected happens, and the reality will look differently than the B we predicted. And if it happens to be worse than A, now what? Change all the variables back? How politically likely is that?
Uhm… it’s complicated. I don’t want to make it a fully general counterargument against multi-variable changes, but it should increase the burden of proof significantly.
Great point. It is definitely much harder to change many variables than one.
I think that what is the correct thing to do depends lot on context and importance.
For example, picking the right government system, or preventing rape, are worth a lot of effort in my book. And worth some seriously challenging systemic changes. In other cases, it may be best to only consider what can be accomplished with single variable shifts, and to throw out more complex possibilities.
Also, I think a lot of things you simply can’t shift by only changing one thing at a time, so in those cases, it is many or nothing.
One thing that helps is to do a lot of study before making changes. If you can run models ahead of time, that is awesome. Then the cost of looking at many variables is greatly reduced, because its not a big deal if a model fails.
Yes, thank you for the example about how multiple variables would need to be changed for this system to work, and how ideas get dismissed on the basis that they can only be implemented in the current system without looking at the other variables.
We should consider separately whether a situation A could be improved by changing into a situation B (by changing multiple variables), and how to realistically get from A to B.
Because, I can talk about all the variables that should be changed… but the only variable I can change is my own behavior. (Technically, my own behavior is also a set of variables, but that is important for different kind of decisions.) So we sometimes get into a multi-player Prisonners’ Dilemma. (For example, I can wait until women start showing their interest in me first, but… some other guy may just move faster; and the punchline is that at the end most of those women will appreciate that he did.)
There is also a technical risk in changing multiple variables. What if we succeed in changing some of them, but fail in changing others of them, and end up with a situation C which may be even worse than A? And that’s actually pretty likely, because you often can’t change many variables at the same time, and when you change only half of them, you may get a worse situation, which may make people say: “OK, this is just getting worse, let’s stop.”
Also, the more variables we change, the harder it is to predict the consequences. There is a big chance that something unexpected happens, and the reality will look differently than the B we predicted. And if it happens to be worse than A, now what? Change all the variables back? How politically likely is that?
Uhm… it’s complicated. I don’t want to make it a fully general counterargument against multi-variable changes, but it should increase the burden of proof significantly.
Great point. It is definitely much harder to change many variables than one.
I think that what is the correct thing to do depends lot on context and importance.
For example, picking the right government system, or preventing rape, are worth a lot of effort in my book. And worth some seriously challenging systemic changes. In other cases, it may be best to only consider what can be accomplished with single variable shifts, and to throw out more complex possibilities.
Also, I think a lot of things you simply can’t shift by only changing one thing at a time, so in those cases, it is many or nothing.
One thing that helps is to do a lot of study before making changes. If you can run models ahead of time, that is awesome. Then the cost of looking at many variables is greatly reduced, because its not a big deal if a model fails.