In My Bayesian Enlightenment, Eliezer writes that he was born a Bayesian. That he decided to become a Bayesian no more than fish prefer to breathe water.
Maybe all people are born Bayesian? Although, in that case, why doesn't everyone use Bayesian statistics? And why do many people learn little new by studying Bayesianism, while for some almost everything in Bayesianism is new? And finally, why are people who read books are much better Bayesians than those who spend all their time on the farm?
I think I have found a very simple and good explanation for this phenomenon.
Imagine that you live in a world where cars are everywhere. Even if you haven't intentionally tried to study cars, your brain automatically detects that [these iron boxes] are fast, and can suddenly change direction or stop. In general, your brain will automatically learn about machines, and, as a result, you will intuitively understand them.
What if you live in a world full of people? Then, just by spending time with them, you will find that they are practically not dangerous, that they look like you, that they do not like it when you eat food that they call "their own"...
̶B̶u̶t̶ ̶i̶f̶ ̶y̶o̶u̶ ̶l̶i̶v̶e̶ ̶i̶n̶ ̶a̶ ̶w̶o̶r̶l̶d̶ ̶w̶h̶o̶s̶e̶ ̶s̶t̶r̶u̶c̶t̶u̶r̶e̶ ̶i̶s̶ ̶B̶a̶y̶e̶s̶i̶a̶n̶ ̶(̶a̶s̶,̶ ̶f̶o̶r̶ ̶e̶x̶a̶m̶p̶l̶e̶,̶ ̶i̶n̶ ̶o̶u̶r̶ ̶w̶o̶r̶l̶d̶)̶? If you live in Bayesian networks and evidence, and your brain is always set up the hypothesis? Then you will automatically learn the art of Bayesian. This is a simple theory, and besides, it explains why people who read a lot of books are more Bayesian than rural residents who spend all day on a farm: they just saw more situations, more plot twists, in general, more cause-and-effect relationships.
So, we should to teach people Bayesian and basic logic, because for some people it's not as obvious as it is for you.
Finally, I think that from birth we are no more Bayesian than race car drivers, but living in the Bayesian world, we inevitably study it.
Edit: In fact, the structure of the world is not Bayesian, it's just that Bayesianism is convenient for describing the world. Therefore, now I understand this story this way: people learn logic, Bayesianism and frequentism, because the world they live in is well described by these theories.
Frequentist and Bayesian reasoning are two ways to handle Knightian uncertainty. Frequentism gives you statements that are outright true in the face of this uncertainty, which is fantastic. But this sets an incredibly high bar that is very difficult to work with.
For a classic example, let's say you want have a possibly biased coin in front of you and you want to say something about its rate of heads. From frequentism, you can lock in a method of obtaining a confidence interval after, say, 100 flips and say "I'm about to flip this coin 100 times and give you a confidence interval for p_heads. The chance that the interval will contain p_heads is at least 99%, regardless of what the true value of p_heads is" There's no Bayesian analogue.
Now let's say I had a complex network of conditional probability distributions with a bunch of parameters which have Knightian uncertainty. Getting confidence regions will be extremely expensive, and they'll probably be way too huge to be useful. So we put on a convenient prior and go.
ETA: Randomized complexity classes also feel fundamentally frequentist.