If you can’t do math at a fairly advanced level—at least having competence with information theory, probability, statistics, and calculus—you can’t understand the world beyond what’s visible on its (metaphorical) surface.
While as a mathematician I find that claim touching, I can’t really agree with it. To use the example that was one of the starting points of this conversation, how much math do you need to understand evolution? Sure, if you want to really understand the modern synthesis in detail you need math. And if you want to make specific predictions about what will happen to allele frequencies you’ll need math. But in those cases it is very basic probability and maybe a tiny bit of calculus (and even then, more often than not you can use the formulas without actually knowing why they work beyond a very rough idea).
Similar remarks apply to other areas. I don’t need a deep understanding of any of those subjects to have a basic idea about atoms, although again I will need some of them if I want to actually make useful predictions (say for Brownian motion).
Similarly, I don’t need any of those subjects to understand the Keplerian model of orbits, and I’ll only need one of those four (calculus) if I want to make more precise estimations for orbits (using Newtonian laws).
The amount of actual math needed to understand the physical world is pretty minimal unless one is doing hard core physics or chemistry.
The amount of actual math needed to understand the physical world is pretty minimal unless one is doing hard core physics or chemistry.
For example… trying to work out what happens when I shoot a neverending stream of electrons at a black hole. The related theories were more or less incomprehensible to me at first glance. Not being able to do off the wall theorizing on everything at the drop of the hat has to at least make 49!
The human-scale physical world is relatively easy to understand, and we may have evolved or learned to perform the trickier computations using specialized modules, such as perhaps recognizing parabolas to predict where a thrown object will land. You get far with linear models, for instance, assuming that the distance something will move is proportional to the force that you hit it with, or that the damage done is proportional to the size of the rock you hit something with. You rarely come across any trajectory where the second derivative changes sign.
The social world, the economic world, ecology, game theory, predicting the future, and politics are harder to understand. There are a lot of nonlinear and even non-differentiable interactions. To understanding a phenomenon qualitatively, it’s helpful to perform a stability analysis, and recognize likely stable areas, and also unstable regions where you have phase transitions, period doublings, and other catastrophes, You usually can’t do the math and solve one of these systems; but if you’ve worked with a lot of toy systems mathematically, you’ll understand the kind of behaviors you might see, and know something about how the number of variables and the correlations between them affect the limits of linear extrapolation. So you won’t assume that a global warming rate of .017C/year will lead to a temperature increase of 1.7C in 100 years.
I’m making this up as I go; I don’t have any good evidence at hand. I have the impression that I use math a lot to understand the world (but not the “physical” world of kinematics). I haven’t observed myself and counted how often it happens.
While as a mathematician I find that claim touching, I can’t really agree with it. To use the example that was one of the starting points of this conversation, how much math do you need to understand evolution? Sure, if you want to really understand the modern synthesis in detail you need math. And if you want to make specific predictions about what will happen to allele frequencies you’ll need math. But in those cases it is very basic probability and maybe a tiny bit of calculus (and even then, more often than not you can use the formulas without actually knowing why they work beyond a very rough idea).
Similar remarks apply to other areas. I don’t need a deep understanding of any of those subjects to have a basic idea about atoms, although again I will need some of them if I want to actually make useful predictions (say for Brownian motion).
Similarly, I don’t need any of those subjects to understand the Keplerian model of orbits, and I’ll only need one of those four (calculus) if I want to make more precise estimations for orbits (using Newtonian laws).
The amount of actual math needed to understand the physical world is pretty minimal unless one is doing hard core physics or chemistry.
For example… trying to work out what happens when I shoot a neverending stream of electrons at a black hole. The related theories were more or less incomprehensible to me at first glance. Not being able to do off the wall theorizing on everything at the drop of the hat has to at least make 49!
The human-scale physical world is relatively easy to understand, and we may have evolved or learned to perform the trickier computations using specialized modules, such as perhaps recognizing parabolas to predict where a thrown object will land. You get far with linear models, for instance, assuming that the distance something will move is proportional to the force that you hit it with, or that the damage done is proportional to the size of the rock you hit something with. You rarely come across any trajectory where the second derivative changes sign.
The social world, the economic world, ecology, game theory, predicting the future, and politics are harder to understand. There are a lot of nonlinear and even non-differentiable interactions. To understanding a phenomenon qualitatively, it’s helpful to perform a stability analysis, and recognize likely stable areas, and also unstable regions where you have phase transitions, period doublings, and other catastrophes, You usually can’t do the math and solve one of these systems; but if you’ve worked with a lot of toy systems mathematically, you’ll understand the kind of behaviors you might see, and know something about how the number of variables and the correlations between them affect the limits of linear extrapolation. So you won’t assume that a global warming rate of .017C/year will lead to a temperature increase of 1.7C in 100 years.
I’m making this up as I go; I don’t have any good evidence at hand. I have the impression that I use math a lot to understand the world (but not the “physical” world of kinematics). I haven’t observed myself and counted how often it happens.